Which is better for enterprises: Progress Agentic RAG or RAGFlow?

Progress Agentic RAG offers SOC 2 compliance for enterprise security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. Progress Agentic RAG leads with 82/100 enterprise readiness score.

How much does Progress Agentic RAG cost vs RAGFlow?

Progress Agentic RAG starts at $700/monthly (paid model), while RAGFlow begins at contact sales/month (open-source model). RAGFlow provides a free tier. This represents a significant price difference of $700/month. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from RAGFlow to Progress Agentic RAG?

Migration from RAGFlow to Progress Agentic RAG typically requires API-based data export/import, which Progress Agentic RAG provides. Progress Agentic RAG provides comprehensive API access good documentation for the migration process. Estimated migration time: 1-2 weeks for full deployment. Contact Progress Agentic RAG support for migration assistance and best practices.

What integrations do Progress Agentic RAG and RAGFlow support?

Progress Agentic RAG integrates with Python SDK: pip install nuclia (Python 3.8+, ~21K weekly downloads), JavaScript/TypeScript SDK: @nuclia/core (React, Next.js, Angular, Vue.js, Svelte), CMS plugins: WordPress, Strapi, Workflow: Pipedream official app, Zapier API-compatible, Chrome extension for web page indexing (7 integrations total). RAGFlow supports RESTful APIs for conversation, query, and document management, Python interfaces for programmatic control, Reference chat demo UI included in repository, Developer-driven integrations (no pre-built channel connectors), Requires custom coding for Slack, Teams, WhatsApp, etc. (5 integrations available). Both offer REST APIs for custom integrations. Progress Agentic RAG offers 40% more integrations.

What are the main differences between Progress Agentic RAG and RAGFlow?

Progress Agentic RAG offers strong capabilities, scoring 0/100 in user ratings. RAGFlow provides unique strengths with 0/100 overall rating. Key technical difference: Progress Agentic RAG focuses on advanced features while RAGFlow emphasizes specialized capabilities.

Which is better for enterprises: Progress Agentic RAG or RAGFlow?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. RAGFlow leads with strong/100 enterprise readiness score.

How much does Progress Agentic RAG cost vs RAGFlow?

Progress Agentic RAG starts at custom pricing/month (subscription model), while RAGFlow begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Progress Agentic RAG to RAGFlow?

Migration from Progress Agentic RAG to RAGFlow typically requires manual data transfer since API access may be limited. RAGFlow offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact RAGFlow support for migration assistance and best practices.

What integrations do Progress Agentic RAG and RAGFlow support?

Progress Agentic RAG integrates with (multiple platforms). RAGFlow supports (various tools). API availability varies by plan. Integration coverage is comparable.

Progress Agentic RAG vs RAGFlow

Q: What are the main differences between Progress Agentic RAG and RAGFlow?

Progress Agentic RAG excels in Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors and Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility, scoring 82/100 in user ratings with N/A% accuracy. RAGFlow leads in Truly open-source (Apache 2.0) with 68K+ GitHub stars - vibrant community and State-of-the-art hybrid retrieval with multiple recall + fused re-ranking with 80/100 overall rating and N/A% accuracy. Key technical difference: Progress Agentic RAG focuses on REMi evaluation model v2 (Llama 3.2-3B) for continuous quality scoring: answer relevance, context relevance, groundedness, correctness (0-5 scale) while RAGFlow emphasizes Deep document understanding with template-based chunking. Pricing differs by $700/month.

Make an informed decision with our comprehensive comparison. Discover which RAG solution perfectly fits your needs.

More about RAGFlow More about Progress Agentic RAG Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Progress Agentic RAG and RAGFlow across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

When choosing between Progress Agentic RAG and RAGFlow, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

Choose Progress Agentic RAG if: you value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Choose RAGFlow if: you value truly open-source (apache 2.0) with 68k+ github stars - vibrant community

About Progress Agentic RAG

Progress Agentic RAG Landing Page Screenshot

Progress Agentic RAG is enterprise application development and deployment platform. Enterprise RAG-as-a-Service platform launched Sept 2025 following Progress Software's acquisition of Barcelona-based Nuclia. Combines SOC2/ISO 27001 security with proprietary REMi evaluation model for continuous answer quality monitoring. Built on open-source NucliaDB (710+ GitHub stars) with Python/JavaScript SDKs. Starting at $700/month. Founded in 2019 (Nuclia), acquired 2025, headquartered in Barcelona, Spain (Nuclia) / Bedford, MA, USA (Progress), the platform has established itself as a reliable solution in the RAG space.

Overall Rating

82/100

Starting Price

$700/mo

About RAGFlow

RAGFlow is open-source rag orchestration engine for document ai. Open-source RAG engine with deep document understanding, hybrid retrieval, and template-based chunking for extracting knowledge from complex formatted data. Founded in 2024, headquartered in Global (Open Source), the platform has established itself as a reliable solution in the RAG space.

Overall Rating

80/100

Starting Price

Custom

Key Differences at a Glance

In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, RAGFlow offers more competitive entry pricing. The platforms also differ in their primary focus: Enterprise Software versus RAG Platform. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

Progress Agentic RAG	RAGFlow	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
60+ Document Formats: PDF, Word (.docx), Excel, PowerPoint, plain text, email formats with automatic parsing Multimedia Processing: Automatic speech-to-text (MP3, WAV, AIFF), video transcript extraction (MP4, etc.), OCR for scanned documents/images Cloud Connectors: SharePoint, Confluence, OneDrive, Google Drive, Amazon S3 with direct integration Sync Agent Desktop App: 60-minute automatic sync with content hashing to prevent redundant reindexing Manual Upload Interface: Files, folders, web links, sitemaps, Q&A pairs via dashboard Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline CRITICAL GAPS: NO Dropbox integration, NO Notion integration, NO explicit YouTube transcript extraction documented Architecture Focus: Comprehensive knowledge retrieval vs lead conversion focus (unlike Drift)	Supported Formats: PDFs, Word documents (.docx), Excel spreadsheets, PowerPoint slides, plain text, images, scanned PDFs with OCR Deep Document Understanding: Template-based chunking with layout recognition model preserving document structure, sections, headings, and formatting External Data Connectors: Confluence pages, AWS S3 buckets, Google Drive folders, Notion workspaces, Discord channels Scheduled Syncing: Automated refresh frequencies for continuous data ingestion from external sources Scalability: Built on Elasticsearch/Infinity vector store - handles virtually unlimited tokens and millions of documents Manual Upload: Via Admin UI or API for individual file ingestion Complex Format Support: Advanced parsing for richly formatted documents, scanned PDFs, and image-based content Self-Hosted Infrastructure: User manages scaling by allocating sufficient servers/cluster resources	Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API. Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs. Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text. View Transcription Guide Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier. See Zapier Connectors Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
Python SDK: pip install nuclia (Python 3.8+, ~21,000 weekly downloads) JavaScript/TypeScript SDK: @nuclia/core on NPM (React, Next.js, Angular, Vue.js, Svelte) CMS Plugins: WordPress, Strapi integrations Workflow Automation: Pipedream official app, Zapier API-compatible Chrome Extension: Web page indexing capability Progress Ecosystem: OpenEdge database connector, Sitefinity CMS integration ('first Generative CMS') CRITICAL LIMITATION: NO native Slack, WhatsApp, Telegram, or Microsoft Teams integrations Platform Design: RAG backend + embeddable widget, NOT omnichannel conversational AI platform Custom Development Required: Messaging platform integrations need API-based custom builds	Native Integrations: None - no pre-built connectors for Slack, Teams, WhatsApp, Telegram API-Driven Integration: RESTful conversation/query APIs enable custom integrations with developer effort Reference Chat UI: Demo interface included in repository - can be embedded or customized Web/Mobile Embedding: Requires custom frontend development calling RAGFlow APIs Workflow Automation: No built-in Zapier/webhook support - developers build custom workflow triggers Deployment Flexibility: Can be integrated into any channel/platform via API - ultimate flexibility with engineering work Internal Tools: Suitable for internal knowledge portals, command-line tools, or custom applications Developer-First: Provides building blocks (APIs, libraries) but no turnkey channel deployment	Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app. Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more. Explore API Integrations Connects with 5,000+ apps via Zapier and webhooks to automate your workflows. Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases. Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc. Read more here. Supports OpenAI API Endpoint compatibility. Read more here.
Core Chatbot Features
AI Search & Generative Answers: Semantic search and Q&A across knowledge bases with trusted, source-linked answers Multi-Turn Conversations: Context-aware dialogue with conversation history maintained for follow-up questions Source Citations: Every answer includes citations linking to source documents for verification and transparency Auto-Summarization: Automatic summarization of long documents for quick understanding Entity Recognition: AI classification and entity extraction enriching corpus for better Q&A Answer-Only Mode: Widget configuration for concise answers vs detailed responses based on use case Multilingual Support: Nuclia multilingual embedding model handles multiple languages out-of-box MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO chat history export for users	Q&A Foundation: Core focus on accurate retrieval-augmented answers with source transparency and grounded citations reducing hallucinations Multi-Lingual Support: Depends on chosen LLM - language-agnostic retrieval engine with Chinese UI supported natively for Asian markets Conversation Context: Session-based conversation API (v0.22+) maintains multi-turn dialogue context and conversation history across interactions Reference Chat UI: Demo interface included in repository - can be embedded or customized as starting point for custom implementations Grounded Citations: Answers backed by source citations with specific text chunks dramatically reducing hallucinations through evidence transparency Lead Capture: Not built-in - would require custom implementation in frontend application layer vs native platform features Analytics Dashboard: Not provided out-of-box - developers must build or integrate external tools (Prometheus, Grafana, Datadog) for metrics Human Handoff: Not native - custom logic required to detect low-confidence answers and redirect to human agents with context transfer Customer Engagement Features: Business features (lead capture, handoff, analytics, sentiment tracking) left to user implementation vs turnkey chatbot platforms Developer-First Philosophy: Provides building blocks (APIs, libraries, retrieval engine) but no turnkey channel deployment or business user dashboards	Reduces hallucinations by grounding replies in your data and adding source citations for transparency. Benchmark Details Handles multi-turn, context-aware chats with persistent history and solid conversation management. Speaks 90+ languages, making global rollouts straightforward. Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Core Agent Features
Retrieval Agents: Autonomously select optimal retrieval strategies based on query characteristics Pre-Built Ingestion Agents (Beta): Labeler (auto-classification), Generator (summaries/JSON/extraction), Graph Extraction (entities/relationships), Q&A Generator (automatic FAQ), Content Safety (inappropriate content flagging) Web Components: <nuclia-search-bar> and <nuclia-chat> for website embedding Widget Configuration: Point-and-click for suggestions, filters, metadata display, thumbnails, answer-only modes CSS Customization: Shadow DOM architecture with cssPath attribute for advanced styling White-Labeling: Full OEM deployment support via API-first design MISSING FEATURES: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented)	Multi-Lingual Support: Depends on chosen LLM - language-agnostic retrieval engine. Chinese UI supported natively Conversation Context: Session-based conversation API (v0.22+) maintains multi-turn dialogue context Grounded Citations: Answers backed by source citations with reduced hallucinations Lead Capture: Not built-in - would require custom implementation in frontend Analytics Dashboard: Not provided out-of-box - developers must build or integrate external tools Human Handoff: Not native - custom logic required to detect low-confidence answers and redirect to human agents Q&A Foundation: Core focus on accurate retrieval-augmented answers with source transparency Customer Engagement: Business features (lead capture, handoff, analytics) left to user implementation	Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions View Agent Documentation Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
Additional Considerations
Recent Acquisition (June 2025): Progress Software acquired Nuclia for $50M - platform transitioning under new ownership with potential strategic direction changes Genuine No-Code + Developer Appeal: Dual-track value proposition - non-technical teams use dashboard, developers leverage API/SDKs for custom builds REMi Quality Differentiator: Proprietary continuous evaluation model (30x faster in v2) addresses hallucination problem absent from most RAG competitors Open-Source Trust Factor: NucliaDB (710+ GitHub stars, AGPLv3) provides code transparency vs black-box platforms - security audits possible Multimodal Strength: OCR for images, speech-to-text for audio/video creates comprehensive searchable corpus beyond text-only competitors Enterprise RAG Focus: Platform optimized for knowledge retrieval and semantic search - not conversational marketing/sales engagement like Drift/Yellow.ai Progress Ecosystem Integration: OpenEdge database connector, Sitefinity CMS integration provides distribution channels unavailable to standalone platforms Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + legacy docs.nuclia.dev) during transition may cause developer confusion Competitive Pricing Entry: $700/month Fly tier undercuts enterprise RAG alternatives while providing genuine capabilities vs limited free tiers Best For: Organizations wanting model flexibility (7 providers), multimodal indexing, open-source transparency, and developer API access without managing infrastructure	Platform Type Clarity: TRUE RAG PLATFORM (Open-Source Engine) - self-hosted infrastructure platform, NOT SaaS - requires DevOps expertise for deployment and maintenance Target Audience: Developer teams, enterprises with DevOps capabilities, research organizations requiring complete control and customization vs turnkey SaaS solutions Primary Strength: Open-source freedom with zero licensing costs, complete customization, cutting-edge RAG innovation (GraphRAG, RAPTOR, agentic workflows) often implemented before commercial platforms State-of-the-Art RAG Capabilities: Hybrid retrieval (full-text + vector + re-ranking) with deep document understanding, layout recognition, structure preservation, multiple recall strategies, and grounded citations Complete Data Control: Self-hosted architecture means data never leaves your infrastructure - suitable for government/corporate secrets, strict data governance, air-gapped operation with local LLMs CRITICAL LIMITATION - DevOps Expertise Required: Not suitable for teams without technical infrastructure and container orchestration skills - steep learning curve for setup, maintenance, scaling, and monitoring CRITICAL LIMITATION - No Managed Service: Self-hosted only with NO SaaS option for teams wanting turnkey deployment without infrastructure management - ongoing operational overhead CRITICAL LIMITATION - Maintenance Burden: User handles Docker updates, security patches, monitoring, backups, disaster recovery, and scaling - continuous hands-on technical work required Business Feature Gaps: Lead capture, human handoff, sentiment analysis, analytics dashboards not built-in - custom development required for customer engagement features Infrastructure Costs Variability: Cloud hosting, storage, bandwidth, and engineering costs can exceed SaaS pricing for smaller deployments - unpredictable vs fixed subscriptions No Commercial SLA: Community support without guaranteed response times or uptime commitments - not suitable for mission-critical 24/7 requirements requiring formal support agreements Production Readiness Effort: Requires significant effort to operationalize with monitoring, logging, alerting, security hardening, disaster recovery vs instant SaaS deployment Use Case Fit: Ideal for enterprises prioritizing control, compliance, and customization over convenience; poor fit for non-technical teams or rapid deployment needs	Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required. Gets you to value quickly: launch a functional AI assistant in minutes. Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech. Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
Customization & Branding
Prompt Lab: Test LLMs side-by-side using actual customer data with real-time comparison 30+ RAG Parameters: Custom chunking strategies, context size configuration, hybrid search weighting Retrieval Strategy Customization: Agents autonomously select optimal approaches per query Widget Customization: Visual editor for suggestions, filters, metadata, thumbnails, answer modes Advanced CSS Styling: Shadow DOM with cssPath attribute for deep customization White-Labeling Support: Full OEM deployments via API-first architecture Role-Based Access Control: Account-level (Owners, Members), Knowledge Box-level (Manager, Writer, Reader) with cascading permissions SSO Integration: Enterprise identity provider connectivity	UI Customization: Full control via source code modification - Admin UI can be styled/rebranded White-Labeling: Self-hosted nature enables complete removal of RAGFlow branding (requires code editing) Custom Frontend: Developers can build entirely custom chat interfaces using RAGFlow as backend No Point-and-Click Theming: UI changes require editing configuration files or frontend code Domain Restrictions: Not built-in - access control managed at network/application level Persona/Tone: Customizable via prompt template editing (requires technical configuration) Unlimited Branding Potential: Open-source freedom means any look/feel achievable with development effort Developer-Required: All customization beyond basic Admin UI requires coding expertise	Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand. White-label Options Provides a no-code dashboard to set welcome messages, bot names, and visual themes. Lets you shape the AI’s persona and tone using pre-prompts and system instructions. Uses domain allowlisting to ensure the chatbot appears only on approved sites.
L L M Model Options
Anthropic: Claude 3.7, Claude 3.5 Sonnet v2 OpenAI: ChatGPT 4o, 4o mini Google: Gemini Flash 2.5, Palm2 Meta: Llama 3.2 Microsoft/Azure: Mistral Large 2 Cohere: Command-R suite Nuclia Private GenAI: 100% data isolation for maximum security Model Switching: Change providers without architectural changes via Prompt Lab Embedding Flexibility: Configurable per Knowledge Box (Nuclia multilingual default + OpenAI embeddings) Side-by-Side Testing: Compare responses across models using actual data in Prompt Lab	Model Agnostic: Integrates with OpenAI (GPT-3.5, GPT-4), local models (Xinference, Ollama), or custom LLMs Configurable Selection: Developer chooses which model to use per deployment/query No Automatic Routing: Dynamic model selection based on query complexity not built-in (user can code this) Embedding Models: Switchable with safeguards for vector space integrity Self-Hosted Models: Support for running models on-premise (no API dependency) Hybrid Retrieval Quality: Multiple recall + fused re-ranking surfaces highly relevant context for any LLM Provider Independence: Not tied to single model vendor - swap providers freely Advanced Retrieval: Sophisticated retrieval pipeline boosts accuracy regardless of model choice	Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ). Automatically balances cost and performance by picking the right model for each request. Model Selection Details Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers. Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
Open-Source Foundation: NucliaDB (710+ GitHub stars, AGPLv3 license, Python/Rust) provides transparency into core retrieval mechanisms Python SDK: pip install nuclia (Python 3.8+, ~21,000 weekly downloads) - full API coverage JavaScript/TypeScript SDK: @nuclia/core (React, Next.js, Angular, Vue.js, Svelte support) REST API: Regional endpoints https://{region}.rag.progress.cloud/api/v1/ with comprehensive documentation Key Endpoints: /ask (generative answers), /find (semantic search), /upload (ingestion), /remi (quality evaluation) Dual Documentation: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern) RAG Cookbook: Downloadable comprehensive guide for developers Code Example Simplicity: Upload and search in just a few Python lines with intuitive SDK design API-First Design: Complete programmatic control over all platform capabilities	APIs: RESTful endpoints for document upload, parsing, dataset management, conversation queries Python Interfaces: Library calls available for programmatic control Conversation API: Session-based chat API (v0.22+) for multi-turn dialogues No Official SDK: No packaged SDK like npm/PyPI module - developers use HTTP requests or call modules directly Deployment: Clone repository or pull Docker image - self-hosted setup required Documentation: Extensive guides at ragflow.io/docs with Get Started, configuration references, examples Community Resources: Active GitHub discussions, Medium articles, community tutorials Source Code Access: Can modify RAGFlow's source for specialized needs Hands-On Experience: More DIY than turnkey - comfortable with Docker, APIs, server management required	Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat. API Documentation Offers open-source SDKs—like the Python `customgpt-client`—plus Postman collections to speed integration. Open-Source SDK Backs you up with cookbooks, code samples, and step-by-step guides for every skill level.
Performance & Accuracy
Benchmark Leader: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness 100M Vectors: Fully ingested and optimized in ~20 minutes with sufficient worker allocation REMi v2 Speed: 30x faster inference than original Mistral-based implementation (Llama 3.2-3B based) Four-Index Hybrid Search: Document Index (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships) Dynamic Sharding: Automatic shard creation as vector counts grow with index node replication for fault tolerance Fast Deployment: 2-hour initial ingestion, 48-hour full deployment timeline ACID Compliance: TiKV key-value store (Tier 2) manages resource metadata with transaction guarantees Three-Tier Storage: Tier 3 (S3/GCS blobs), Tier 2 (TiKV metadata), Tier 1 (sharded indexes)	Hybrid Retrieval: Full-text search + vector similarity + multiple recall with fused re-ranking Grounded Citations: Answers tied to specific source text chunks - reduces hallucinations Deep Document Parsing: Layout recognition and structure preservation improves retrieval precision Targeted Information Retrieval: Well-rounded evidence sets presented to LLM for accurate answers Production-Grade Architecture: Optimized for large datasets and fast queries (Elasticsearch-backed) Community Validation: 68K+ GitHub stars, battle-tested by many production deployments State-of-the-Art Techniques: Cutting-edge RAG algorithms often introduced before commercial systems Tuning Required: Optimal performance achieved through proper configuration (embedding model, chunking templates)	Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching. Independent tests rate median answer accuracy at 5/5—outpacing many alternatives. Benchmark Results Always cites sources so users can verify facts on the spot. Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Customization & Flexibility ( Behavior & Knowledge)
30+ RAG Optimization Parameters: Fine-grained control over retrieval behavior Custom Chunking Strategies: Configurable text segmentation for optimal context windows Context Size Configuration: Adjust context sent to LLMs based on use case Hybrid Search Weighting: Balance keyword vs semantic search relevance Retrieval Agent Autonomy: Automatically select optimal strategies per query characteristics Embedding Model Flexibility: Switch per Knowledge Box (Nuclia multilingual + OpenAI options) Prompt Lab Experimentation: Test configurations with actual data before production deployment LLM Provider Switching: Change models without architectural changes (7 providers supported)	Knowledge Updates: Add/remove files anytime via Admin UI or API - continuous indexing without downtime for always-current knowledge bases External Sync: Automated data source refresh from Google Drive, S3, Confluence, Notion with near real-time updates eliminating manual re-uploads Behavior Customization: Edit prompt templates and system logic for tone, personality, response handling through configuration files or code modifications Chunking Strategies: Template-based chunking configurable per document type - paragraph-sized for FAQs, larger with overlap for narratives preserving context No GUI Toggles: Customization requires editing config files or source code vs point-and-click dashboards - technical expertise assumed Ultimate Freedom: Integrate translation services, custom re-ranking algorithms, specialized embeddings, or proprietary retrieval mechanisms through code modifications Deep Tuning Potential: Modify retrieval pipeline, add custom modules, extend functionality at source code level - complete architectural flexibility Developer Dependency: Specialized behavior changes assume technical expertise and comfort with Python, Docker, API development, and system architecture Admin UI (v0.22+): Basic graphical interface for file upload, dataset management, data source connections - power users can maintain content after developer setup No Role-Based Access: Single admin login by default - multi-user management and role-based access control require custom implementation	Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current. Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus. Learn How to Update Sources Supports multiple agents per account, so different teams can have their own bots. Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
Fly Tier: $700/month - 10GB/15K resources, 750MB max file, 1 Knowledge Box, cloud only, 10K tokens/month Growth Tier: $1,750/month - 50GB/80K resources, 1.5GB max file, 2 Knowledge Boxes, Prompt Lab, 10K tokens/month Enterprise Tier: Custom pricing - Unlimited data/file size, 11 Knowledge Boxes, hybrid/on-prem deployment, 10K tokens/month Token Consumption: $0.008/token beyond 10K/month included across all tiers 14-Day Free Trial: Available without disclosed credit card requirement AWS Marketplace: Simplifies enterprise procurement with existing cloud spend commitments Competitive Entry Point: $700/month undercuts enterprise alternatives (Drift $30K+/year, Yellow.ai similar) Scaling Consideration: Token-based consumption pricing requires careful usage forecasting for budget predictability	License Cost: $0 - Apache 2.0 open-source license, free to use Infrastructure Costs: User pays for cloud servers (CPU, memory, GPU), storage, networking LLM API Costs: Separate charges for OpenAI or other third-party model APIs (if used) Engineering Costs: Developer/DevOps salaries for installation, maintenance, monitoring, updates Scalability: Horizontally scalable with cluster deployment - no predefined plan limits Enterprise Scale: Can handle hundreds of millions of words with sufficient infrastructure investment Cost Variability: Unpredictable - usage spikes require rapid server allocation Total Cost of Ownership: Often competitive for large orgs with existing infrastructure, higher for those without DevOps capabilities	Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans. Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates. View Pricing Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
SOC2 Type 2 Certified: Annual audits for enterprise security assurance ISO 27001 Certified: Annually audited information security management GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data Encryption: AES-256 at rest, TLS in transit for comprehensive data protection AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails Human-in-the-Loop: Validation options for critical workflows Tenant Isolation: Customer data separation ensures multi-tenant security Audit Logs: Standard across all pricing tiers for compliance tracking API Key Management: Temporal keys and rotation for security hygiene CRITICAL: CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification Data Governance: Enterprise tier supports complete on-premise deployment for 100% data control	Data Control: Complete - self-hosted means data never leaves your infrastructure On-Premise Deployment: Suitable for government/corporate secrets and strict data governance No Third-Party Risk: Using local LLMs eliminates external API data exposure Encryption: User-configured - deploy with TLS, VPN, OS-level disk encryption Access Control: User implements via network security, firewalls, reverse proxies No Formal Certifications: No SOC 2, ISO 27001, HIPAA certifications (community-driven) Code Auditing: Open-source allows security audits and community vulnerability patching Compliance: Achievable through proper deployment configuration and external compliance frameworks Multi-Tenancy: User must implement isolation (separate instances or custom segregation)	Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption. Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private. Security Certifications Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
REMi Real-Time Dashboard: Answer relevance, context relevance, groundedness, correctness (0-5 scale) 7-Day Rolling Averages: Performance evolution graphs spanning 24 hours to 30 days Health Displays: Quality metrics shown in real-time for immediate visibility Four Quality Dimensions: Answer Relevance (query alignment), Context Relevance (passage quality), Groundedness (source derivation), Answer Correctness (ground truth alignment) REMi v2 Performance: 30x faster inference (Llama 3.2-3B) vs original Mistral implementation Benchmark Validation: Tested against Vectara on Docmatix 1.4k dataset with highest scores Audit Logs: Standard across all tiers for compliance and security tracking MISSING FEATURE: Proactive alerting not documented (monitoring exists, automated alerts unclear)	Built-In Analytics: None - no polished analytics dashboard out-of-box Admin UI Stats: Basic document counts, recent query history, indexing progress Logs: Console logs and log files for operations, errors, query times External Monitoring: User integrates Prometheus, Grafana, Datadog, Splunk for metrics No Alerting: User must configure alert mechanisms (e.g., Kubernetes probes, log watchers) Conversation Logging: Developer must implement storage and analysis of chat sessions Trend Analysis: Requires custom data collection and external analytics tools Ultimate Flexibility: Can instrument with any monitoring stack - Prometheus, ELK, custom dashboards	Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status. Lets you export logs and metrics via API to plug into third-party monitoring or BI tools. Analytics API Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Dual Documentation Portals: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern) RAG Cookbook: Comprehensive downloadable guide for developers SDK Ecosystem: Python (~21K weekly downloads) + JavaScript/TypeScript with active developer usage 14-Day Free Trial: Hands-on evaluation without credit card requirement Progress Enterprise Support: Backed by 2,000+ employee parent company infrastructure AWS Marketplace: Available November 2025 for streamlined enterprise procurement Open-Source Community: NucliaDB 710+ GitHub stars with AGPLv3 license transparency API-First Support: Comprehensive REST API documentation with regional endpoints	Customer Support: None - no formal support team or SLA Community Support: Very active GitHub (68K+ stars), Discord server, Twitter/X presence Response Time: No guarantees - relies on community volunteers and maintainer availability Documentation: Extensive at ragflow.io/docs and GitHub README Knowledge Base: Community tutorials, Medium articles, blog posts, integration guides Commercial Support: May be available from InfiniFlow team on request (unofficial) Ecosystem Growth: Fastest-growing open-source RAG project (GitHub Octoverse 2024) Community Contributions: Plugins, scripts, integrations shared by developers Innovation Pace: Rapid feature releases driven by active contributor community	Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast. Developer Docs Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs. Enterprise Solutions Benefits from an active user community plus integrations through Zapier and GitHub resources.
No- Code Interface & Usability
Target Users: Non-technical teams (marketing, HR, legal, customer support) with zero coding required Visual Dashboard: Create Knowledge Box, upload documents, deploy search widget in single session Point-and-Click Widget Editor: Configure suggestions, filters, metadata, thumbnails, answer modes visually Pre-Built Ingestion Agents (Beta): Automated workflows for labeling, summarization, graph extraction, Q&A generation, content safety Prompt Lab: Visual interface for side-by-side LLM testing with actual data Role-Based Access Control: Visual permission management separating Account and Knowledge Box concerns Rapid Deployment: Progress explicitly markets minutes-to-production capability for business users Shadow DOM Architecture: Advanced users can apply CSS styling via cssPath attribute for customization	Admin UI: Basic graphical interface (v0.22+) for file upload, dataset management, data source connections No True No-Code: Initial setup requires Docker, OAuth configuration, technical deployment Power User Access: Analysts can maintain content via Admin UI after developer setup No Pre-Built Templates: Agent configuration requires defining datasets and LLM settings manually Behavior Customization: Not exposed in friendly way - requires config file or prompt template editing Single Admin Login: No role-based multi-user system by default Developer Target Audience: Primarily built for technical teams, not business users Custom Frontend Option: Developers can build simple UI for end-users, abstracting RAGFlow complexity Limited Business User Access: Not suitable for non-technical teams without developer support	Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance. Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing. User Experience Review Uses role-based access so business users and devs can collaborate smoothly.
R E Mi Evaluation Model ( Core Differentiator)
Proprietary Investment: Significant R&D differentiator addressing hallucination problem - absent from most competitors REMi v2 (Current): Llama-REMi v1 based on Llama 3.2-3B with 30x faster inference vs original Mistral implementation Continuous Quality Monitoring: Evaluates EVERY interaction across four dimensions (0-5 scale) Answer Relevance: Measures how directly response addresses the query Context Relevance: Assesses quality of retrieved passages relative to question Groundedness: Evaluates degree to which answers derive from source context (hallucination detection) Answer Correctness: Alignment with ground truth when available (optional dimension) Benchmark Validation: Nuclia with OpenAI embeddings achieved highest scores vs Vectara on Docmatix 1.4k dataset across answer relevance, context relevance, correctness Real-Time Visibility: Dashboard health displays with 7-day rolling averages and performance graphs (24h to 30d) Competitive Advantage: Most RAG platforms lack continuous quality evaluation - Progress makes this core differentiator	N/A	N/A
Open- Source Nuclia D B Foundation
GitHub Presence: 710+ stars, AGPLv3 license provides full transparency into core retrieval mechanisms Technology Stack: Python and Rust implementation for performance and reliability Managed Infrastructure: Progress removes operational burden while maintaining technical transparency Three-Tier Storage: Tier 3 (S3/GCS blob storage), Tier 2 (TiKV key-value with ACID), Tier 1 (sharded indexes) Four Index Types: Document Index (property filtering), Full Text (keyword/fuzzy search), Chunk/Vector (semantic similarity), Knowledge Graph (entity relationships) Dynamic Sharding: Automatic shard creation as vectors grow with index node replication for fault tolerance Embedding Flexibility: Switchable per Knowledge Box (Nuclia multilingual + OpenAI options) 100M Vector Performance: Full ingestion and optimization in ~20 minutes with sufficient worker allocation Developer Trust: Open-source foundation allows code inspection and contribution vs black-box competitors	N/A	N/A
Multi- Lingual Support
Nuclia Multilingual Embedding Model: Default model supporting multiple languages out-of-box 60+ Document Format Processing: Multi-language content across PDF, Word, Excel, PPT, text, email Automatic Transcription: Multi-language speech-to-text for audio/video content Configurable Embeddings: Per Knowledge Box language optimization LLM Provider Flexibility: 7 providers with varying multilingual capabilities (Claude, GPT, Gemini, Llama, etc.) Global Customer Base: Deployed across Spain, US, international markets indicating production multilingual usage	N/A	N/A
R A G-as-a- Service Assessment
Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - Core mission is retrieval-augmented generation backend with developer-first API access Core Focus: Semantic search and generative Q&A across knowledge bases with transparent NucliaDB architecture RAG Backend Design: Fully managed RAG infrastructure with embeddable widgets (NOT closed conversational marketing like Drift/Yellow.ai) Programmatic Access: Complete REST API + dual SDKs (Python/JavaScript) for full knowledge base management LLM Flexibility: 7 provider options switchable without architectural changes (Anthropic, OpenAI, Google, Meta, Cohere, Azure, Nuclia) Open-Source Transparency: NucliaDB foundation (710+ GitHub stars) provides visibility into retrieval mechanisms vs black-box platforms (Lindy.ai) Comparison Alignment: Direct architectural comparison to CustomGPT.ai is valid - both are RAG-as-a-Service platforms with API-first design Use Case Fit: Organizations prioritizing knowledge retrieval, semantic search, and generative Q&A over conversational marketing/sales engagement	Platform Type: TRUE RAG PLATFORM (Open-Source Engine) Core Architecture: Hybrid retrieval (full-text + vector + re-ranking) with deep document understanding Service Model: Self-hosted infrastructure platform - not SaaS Retrieval Quality: State-of-the-art with multiple recall strategies and fused re-ranking Document Processing: Advanced parsing with layout recognition, OCR, structure preservation LLM Integration: Model-agnostic with support for any LLM (OpenAI, local, custom) Citation Support: Grounded answers with source references and reduced hallucinations Enterprise Readiness: Production-grade architecture but requires user-managed deployment Target Users: Developer teams, enterprises with DevOps capabilities, research organizations Key Differentiator: Complete control, zero vendor lock-in, cutting-edge open-source RAG innovation	Platform Type: TRUE RAG-AS-A-SERVICE PLATFORM - all-in-one managed solution combining developer APIs with no-code deployment capabilities Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat API Documentation Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform) RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses Benchmark Details Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
Competitive Positioning
Market Position: Enterprise RAG-as-a-Service with genuine no-code accessibility + developer-first API design (dual-track appeal) Pricing Advantage: $700/month entry undercuts enterprise competitors (Drift $30K+/year, Yellow.ai similar, CustomGPT varies) REMi Differentiator: Proprietary continuous quality monitoring addresses hallucination problem - capability absent from most competitors Benchmark Leadership: Achieved highest scores vs Vectara on Docmatix 1.4k dataset (answer relevance, context relevance, correctness) Open-Source Trust: NucliaDB transparency (710+ GitHub stars) vs black-box competitors (Lindy.ai, Drift, Yellow.ai) vs. CustomGPT: Similar RAG-as-a-Service category, Progress emphasizes REMi quality monitoring + open-source foundation differentiation vs. Drift/Yellow.ai: TRUE RAG platform vs conversational marketing/sales engagement platforms (fundamentally different categories) vs. Lindy.ai: Full API/SDK access vs NO public API (Progress developer-friendly, Lindy no-code only) Integration Gaps: NO native messaging channels (Slack/WhatsApp/Teams) vs omnichannel competitors - requires custom development HIPAA Gap: No documented certification creates healthcare trust gap vs compliant competitors (Drift has HIPAA) Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes Progress Ecosystem Advantage: Integration with OpenEdge, Sitefinity CMS provides distribution channels unavailable to standalone competitors	Primary Advantage: Open-source freedom with zero licensing costs and complete customization Technical Superiority: State-of-the-art hybrid retrieval often exceeds commercial RAG accuracy Data Sovereignty: Self-hosted deployment ensures complete data control and privacy Innovation Speed: Cutting-edge features (GraphRAG, agentic workflows) before many commercial platforms Primary Challenge: Requires DevOps expertise - not suitable for teams without technical resources Cost Trade-Off: No license fees but infrastructure and engineering costs can be significant Market Position: Developer-first alternative to SaaS RAG platforms for technical organizations Use Case Fit: Ideal for enterprises prioritizing control, compliance, and customization over convenience Community Strength: Largest open-source RAG community provides validation and ongoing innovation	Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts) Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
Deployment & Infrastructure
Fully Managed Cloud: EU (primary) and US data centers with regional API routing (https://{region}.rag.progress.cloud/api/v1/) Hybrid Deployment: Cloud processing with on-premise NucliaDB storage for data sovereignty requirements Complete On-Premise: Enterprise tier supports 100% on-premise deployment for maximum data governance AWS Marketplace: Available November 2025 for streamlined enterprise procurement with existing cloud spend Three-Tier Architecture: S3/GCS blob storage (Tier 3), TiKV metadata (Tier 2), sharded indexes (Tier 1) Dynamic Scaling: Automatic shard creation as vector counts grow with index node replication Web Component Embedding: <nuclia-search-bar> and <nuclia-chat> for website integration Multi-Region Support: Regional data residency options (EU/US) for compliance requirements	Deployment Method: Docker containers - pull image or clone repository Infrastructure Required: Cloud VMs (AWS, GCP, Azure), on-premise servers, or Kubernetes clusters Scalability Model: Horizontal (add servers) and vertical (upgrade hardware) scaling Database Backend: Elasticsearch or Infinity vector store for document indexing Resource Management: User provisions CPU, memory, storage, GPU (for local models) No SaaS Option: Self-hosted only - no managed cloud service available High Availability: User configures load balancing, redundancy, failover Maintenance Burden: User handles updates, patches, monitoring, backups Enterprise Capability: Can scale to enterprise demands with proper infrastructure investment	N/A
Customer Base & Case Studies
SRS Distribution (Wholesale Building Materials): "Progress Agentic RAG has fundamentally changed how we access and act on information across our organisation. Its ability to deliver fast, accurate, and verifiable insights from our unstructured data has been a game-changer for productivity and decision-making." BrokerChooser (Financial Services): Replaced keyword search with generative AI, reporting significant conversion increases and better user experience NAFEMS (Engineering Simulation Association): Knowledge discovery across thousands of technical publications for international membership community Althaia Hospitals (Spain's Largest Central Catalonia Hospital): Medical protocol search supporting 5,000+ healthcare professionals Columbia Business School: Academic knowledge discovery and research support Barry University: Education sector deployment for institutional knowledge management CCOO (Spain's Largest Trade Union): 1M+ members served with knowledge retrieval platform Buff Sportswear: Commercial deployment for product and customer knowledge management Pre-Acquisition Scale: ~20 customers across healthcare, pharmaceutical, education, public administration sectors	N/A	N/A
A I Models
Anthropic Models: Claude 3.7, Claude 3.5 Sonnet v2 for safety-focused, high-quality generation OpenAI Models: ChatGPT 4o, 4o mini for industry-leading language capabilities Google Models: Gemini Flash 2.5, PaLM2 for multimodal and search-optimized tasks Meta Models: Llama 3.2 for open-source flexibility and customization Microsoft/Azure: Mistral Large 2 for enterprise deployments with Azure integration Cohere Models: Command-R suite for retrieval-optimized generation Nuclia Private GenAI: 100% data isolation mode for maximum security without third-party LLM exposure Model Switching: Change providers without architectural changes via Prompt Lab for side-by-side testing Embedding Flexibility: Configurable per Knowledge Box (Nuclia multilingual default + OpenAI embeddings)	OpenAI Models: Full support for GPT-4, GPT-4o, GPT-4o-mini, GPT-3.5-turbo, and all OpenAI API-compatible models Anthropic Claude: Native integration with Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku through dedicated provider Google Gemini: Support for Gemini Pro and Gemini Ultra via Google Cloud integration Local Model Deployment: Deploy locally using Ollama, Xinference, IPEX-LLM, or Jina for complete offline operation Popular Open-Source Models: Embed Llama 2, Llama 3, Mistral, DeepSeek, WizardLM, Vicuna, and other Hugging Face models Chinese LLM Support: Baichuan, VolcanoArk, Tencent Hunyuan, Baidu Yiyan, XunFei Spark integration Additional Providers: PerfXCloud, TogetherAI, Upstage, Novita AI, 01.AI, SiliconFlow, PPIO, Jiekou.AI OpenAI-Compatible APIs: Configure any model with OpenAI-compatible APIs through universal OpenAI-API-Compatible provider Embedding Models: Switchable embedding models with safeguards for vector space integrity - supports Voyage AI embeddings Model Agnostic Architecture: Not tied to single vendor - swap providers freely without vendor lock-in	Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request Model Selection Details Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Agentic RAG Engine: Retrieval agents autonomously select optimal strategies based on query characteristics Four-Index Hybrid Search: Document (property filtering), Full Text (keyword/fuzzy), Vector/Chunk (semantic), Knowledge Graph (entity relationships) 30+ RAG Parameters: Custom chunking strategies, context size configuration, hybrid search weighting for fine-tuned optimization REMi v2 Quality Monitoring: Continuous evaluation across Answer Relevance, Context Relevance, Groundedness, Correctness (30x faster inference) Benchmark Leadership: Highest scores vs Vectara on Docmatix 1.4k dataset (answer relevance, context relevance, correctness) Pre-Built Ingestion Agents (Beta): Labeler (auto-classification), Generator (summaries/JSON), Graph Extraction (entities/relationships), Q&A Generator, Content Safety Multimodal Processing: OCR for scanned documents/images, automatic speech-to-text for audio (MP3, WAV, AIFF), video transcript extraction 60+ Document Formats: PDF, Word, Excel, PowerPoint, plain text, email formats with automatic parsing Open-Source Foundation: NucliaDB (710+ GitHub stars, AGPLv3) provides transparency into retrieval mechanisms vs black-box platforms	Hybrid Retrieval Engine: Combines full-text (lexical) search + vector (semantic) similarity + multiple recall with fused re-ranking GraphRAG: Graph-based retrieval augmentation for relationship-aware knowledge extraction across connected entities RAPTOR: Recursive abstractive processing for tree-organized retrieval with hierarchical knowledge structures Agentic Workflows: Multi-step reasoning, tool use, code execution in sandbox for complex analytical tasks Template-Based Chunking: Document-type-specific chunking strategies preserving headers, sections, tables, and formatting Layout Recognition Model: Deep document understanding preserving structure during parsing - handles richly formatted documents Multiple Recall Strategies: Retrieves candidates via multiple methods, then fuses results with ML re-ranking for precision Grounded Citations: Answers backed by source citations with specific text chunks - dramatically reduces hallucinations OCR Integration: Scanned PDFs and image-based content processing with optical character recognition Code Sandbox Execution: Safe code execution environment enabling agent to perform complex analytical tasks Elasticsearch Backend: Production-grade vector store handling virtually unlimited tokens and millions of documents Infinity Vector Store: Alternative vector storage option for massive-scale document indexing Multi-Repository Federation: Unified retrieval across multiple data sources with comprehensive context assembly Cutting-Edge Research: Implements latest academic RAG techniques in production-ready form before commercial platforms	Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks RAG Performance Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content Benchmark Details Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Enterprise Knowledge Management: Non-technical teams (marketing, HR, legal, customer support) deploying knowledge bases in minutes Healthcare & Pharma: Althaia Hospitals medical protocol search for 5,000+ healthcare professionals with HIPAA-grade security needs Financial Services: BrokerChooser replaced keyword search with generative AI for significant conversion increases Education: Columbia Business School and Barry University for academic knowledge discovery and institutional knowledge management Engineering & Research: NAFEMS knowledge discovery across thousands of technical publications for international membership Trade Organizations: CCOO (Spain's largest union) serving 1M+ members with knowledge retrieval platform Intelligent Document Processing: Automatic document classification, routing, extraction, risk identification, and summary generation Dynamic Knowledge Management: Continuous updates, gap identification, and automatic documentation generation Developer RAG Backend: API-first infrastructure for building custom AI applications with Prompt Lab experimentation	Enterprise Document Analysis: Financial risk analysis, fraud detection, investment research by retrieving and analyzing reports, financial statements, and regulatory documents with verifiable insights Customer Support Chatbots: Accurate, citation-backed responses for customer inquiries - integrate into virtual assistants to reduce dependency on human agents while improving satisfaction Legal Document Processing: Complex legal document analysis with structure preservation, citation tracking, and relationship mapping across case law and statutes Healthcare Documentation: Medical literature review, clinical decision support, patient record analysis with strict data privacy through self-hosted deployment Research & Development: Scientific paper analysis, patent research, literature review with relationship extraction and knowledge graph construction Internal Knowledge Management: Enterprise-level low-code tool for managing personal and organizational data with integration into company knowledge bases Compliance & Regulatory: Compliance document tracking, regulatory analysis, audit support with complete data control and citation trails Financial Services: Investment research, market analysis, risk assessment by querying vast financial data repositories with accuracy Technical Documentation: API documentation, product manuals, troubleshooting guides with structure-aware retrieval for developers Education & Training: Course material organization, student question answering, academic research support with multi-turn dialogue capabilities Government & Defense: Classified document analysis, intelligence gathering, policy research with complete on-premise deployment and air-gapped operation	Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts) Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data) Financial services: Product guides, compliance documentation, customer education with GDPR compliance E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
SOC2 Type 2: Annually audited for enterprise security assurance ISO 27001: Annually audited information security management certification GDPR Compliant: Built-in PII anonymization automatically detects and removes personal data Encryption: AES-256 at rest, TLS in transit for comprehensive data protection AI Risk Classification: Low to minimal AI risk category with policy-as-code guardrails Human-in-the-Loop: Validation options for critical workflows requiring human oversight Tenant Isolation: Customer data separation ensures multi-tenant security with isolated Knowledge Boxes Audit Logs: Standard across all pricing tiers for compliance tracking and security monitoring API Key Management: Temporal keys and rotation for security hygiene CRITICAL LIMITATION: NO HIPAA certification documented - healthcare organizations processing PHI must contact sales for compliance clarification Data Governance: Enterprise tier supports complete on-premise deployment for 100% data control and sovereignty	Complete Data Control: Self-hosted architecture means data never leaves your infrastructure - suitable for government/corporate secrets On-Premise Deployment: Full air-gapped operation possible - no external API dependencies when using local LLMs Zero Third-Party Risk: Using local models (Ollama, Xinference) eliminates external API data exposure entirely User-Configured Encryption: Deploy with TLS/SSL for transit encryption, VPN tunneling, and OS-level disk encryption (AES-256) Access Control: User implements via network security, firewall rules, reverse proxies, and authentication layers No Formal Certifications: Community-driven project without SOC 2, ISO 27001, or HIPAA certifications - compliance achieved through proper deployment Open-Source Auditing: Full code transparency enables security audits and community vulnerability patching - no black-box systems Multi-Tenancy Implementation: User must implement isolation through separate instances or custom segregation logic Data Residency: Complete control over data location - deploy in any geography meeting regulatory requirements Compliance Frameworks: Can be configured to meet GDPR, HIPAA, SOC 2, FedRAMP through proper deployment and operational procedures Audit Trails: User configures logging, monitoring, and audit mechanisms through application and infrastructure layers Single-Tenant by Default: Each deployment isolated - no cross-tenant data leakage risk Network Isolation: Can be deployed in isolated networks, behind firewalls, with VPN-only access	Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest SOC 2 Type II certification: Industry-leading security standards with regular third-party audits Security Certifications GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security Data isolation: Customer data stays isolated and private - platform never trains on user data Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Fly Tier: $700/month - 10GB/15K resources, 750MB max file, 1 Knowledge Box, cloud only, 10K tokens/month included Growth Tier: $1,750/month - 50GB/80K resources, 1.5GB max file, 2 Knowledge Boxes, Prompt Lab access, 10K tokens/month Enterprise Tier: Custom pricing - Unlimited data/file size, 11 Knowledge Boxes, hybrid/on-prem deployment, 10K tokens/month Token Consumption: $0.008/token beyond 10K/month included across all tiers for usage-based scaling 14-Day Free Trial: Available without disclosed credit card requirement for hands-on evaluation AWS Marketplace: Available November 2025 for simplified enterprise procurement with existing cloud spend commitments Competitive Entry Point: $700/month undercuts enterprise alternatives (Drift $30K+/year, Yellow.ai similar, LiveChat per-agent scaling) Scaling Consideration: Token-based consumption pricing requires careful usage forecasting for budget predictability beyond included tier Best Value For: Organizations wanting to control costs through usage optimization vs fixed seat-based or per-project pricing models	License Cost: $0 - Apache 2.0 open-source license, completely free to use, modify, and distribute No Subscription Fees: Zero ongoing licensing costs - no per-user, per-query, or per-document charges Infrastructure Costs: User pays for cloud VMs (AWS, GCP, Azure), on-premise servers, or Kubernetes cluster resources Compute Requirements: CPU, memory, storage, optional GPU for local model inference - costs scale with usage LLM API Costs: Separate charges for third-party APIs (OpenAI, Anthropic) if used - can be eliminated with local models Engineering Costs: Developer/DevOps salaries for installation, configuration, maintenance, monitoring, security, and updates Storage Costs: Vector database storage (Elasticsearch/Infinity), document storage, backup storage costs Network Costs: Bandwidth for data ingestion, API calls, cross-region data transfer if applicable Horizontal Scalability: Add servers/nodes to handle increased load - no predefined plan limits or caps Vertical Scalability: Upgrade hardware (CPU, RAM, GPU) for improved performance per node Cost Predictability Challenges: Usage spikes require rapid resource allocation - costs can be unpredictable vs fixed SaaS pricing TCO Considerations: Often competitive for large organizations with existing infrastructure, higher for those without DevOps capabilities Enterprise Scale: Can handle hundreds of millions of words with sufficient infrastructure investment - no artificial limits Commercial Support: May be available from InfiniFlow team on request for paid support agreements (unofficial)	Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security View Pricing Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs Enterprise Solutions 7-Day Free Trial: Full access to Standard features without charges - available to all users Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium) Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans) Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
Dual Documentation Portals: docs.rag.progress.cloud (primary) + legacy docs.nuclia.dev (fragmentation concern during transition) RAG Cookbook: Comprehensive downloadable guide for developers with implementation patterns and best practices SDK Ecosystem: Python (~21K weekly downloads via pip install nuclia) + JavaScript/TypeScript (@nuclia/core on NPM) REST API: Regional endpoints https://{region}.rag.progress.cloud/api/v1/ with complete programmatic control Key Endpoints: /ask (generative answers), /find (semantic search), /upload (ingestion), /remi (quality evaluation) 14-Day Free Trial: Hands-on evaluation platform without credit card requirement Progress Enterprise Support: Backed by 2,000+ employee parent company infrastructure with dedicated account management Open-Source Community: NucliaDB 710+ GitHub stars with AGPLv3 license transparency and community contributions Integration Examples: WordPress, Strapi plugins, Pipedream official app, Zapier API-compatible, Chrome extension for web indexing Progress Ecosystem: OpenEdge database connector, Sitefinity CMS integration ("first Generative CMS") for distribution advantages	Community Support: Very active GitHub community (68,000+ stars) with discussions, issues, and community contributions Discord Server: Active Discord community for real-time help, discussions, and troubleshooting from users and maintainers Official Documentation: Comprehensive guides at ragflow.io/docs covering Get Started, configuration, deployment, API reference GitHub Repository: Complete source code, README, examples, configuration templates at github.com/infiniflow/ragflow Medium Articles: Technical blog posts and tutorials from InfiniFlow team and community contributors Community Tutorials: User-generated guides, integration examples, best practices shared across platforms No Formal SLA: Community support with no guaranteed response times or availability commitments No Customer Support Team: Relies on community volunteers and maintainer availability - not suitable for mission-critical 24/7 support needs Response Time: Varies based on community activity and maintainer availability - typically hours to days for complex issues Issue Tracking: Public GitHub issues for bug reports, feature requests, and troubleshooting - transparent development process Commercial Support Option: May be available from InfiniFlow team on request for paid consulting and support agreements Knowledge Base: Community-maintained wiki, FAQ, troubleshooting guides, and deployment best practices Release Notes: Detailed release notes for each version with new features, improvements, and breaking changes API Documentation: RESTful API documentation, Python interfaces, SDK examples for programmatic integration Rapid Innovation: Frequent releases with cutting-edge features driven by active community and maintainers	Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding Developer Docs Email and in-app support: Quick support via email and in-app chat for all users Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs Code samples: Cookbooks, step-by-step guides, and examples for every skill level API Documentation Open-source resources: Python SDK (customgpt-client), Postman collections, GitHub integrations Open-Source SDK Active community: User community plus 5,000+ app integrations through Zapier ecosystem Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
NO HIPAA Certification Documented: Healthcare organizations processing PHI must contact sales - major compliance gap vs competitors with documented HIPAA NO Native Messaging Channels: No Slack, WhatsApp, Telegram, or Microsoft Teams integrations - requires custom API-based development Documentation Fragmentation: Dual portals (docs.rag.progress.cloud + docs.nuclia.dev) during Progress acquisition transition may cause confusion Recent Acquisition Risk: June 2025 Progress purchase means platform still maturing under new ownership with potential direction changes Scalability Concerns: Multiple problems limit scalability - hard to scale nodes up/down, write operations affect concurrent search performance NO Dropbox Integration: Missing Dropbox connector vs competitors - limits cloud storage sync options NO Notion Integration: Missing Notion connector - gap for knowledge management workflows NO YouTube Transcript Extraction: Not explicitly documented vs competitors with video indexing features Token-Based Billing Complexity: $0.008/token beyond 10K/month requires careful usage forecasting vs predictable seat-based pricing Missing Features: NO lead capture, NO human handoff/escalation workflows, NO proactive alerting (monitoring exists, alerting undocumented) Learning Curve: 30+ RAG parameters and Prompt Lab may feel technical for non-developer teams despite no-code dashboard Best For: Development teams and technical users - powerful for experts but may overwhelm business users wanting simple deployment	DevOps Expertise Required: Not suitable for teams without technical infrastructure and container orchestration skills - steep learning curve No Managed Service: Self-hosted only - no SaaS option for teams wanting turnkey deployment without infrastructure management Maintenance Burden: User handles Docker updates, security patches, monitoring, backups, disaster recovery, and scaling - ongoing operational overhead No Native Channel Integrations: No pre-built connectors for Slack, Teams, WhatsApp, Telegram - requires API-driven custom development Limited No-Code Features: Admin UI (v0.22+) basic - not suitable for non-technical business users without developer support No Built-In Analytics: No polished analytics dashboard out-of-box - must integrate external tools (Prometheus, Grafana, Datadog) Single Admin Login: No role-based access control or multi-user management by default - requires custom implementation No Formal Certifications: Community-driven project without SOC 2, ISO 27001, HIPAA certifications - compliance responsibility on user Business Feature Gaps: Lead capture, human handoff, sentiment analysis not built-in - custom development required for customer engagement features Infrastructure Costs: Cloud hosting, storage, bandwidth, and engineering costs can exceed SaaS pricing for smaller deployments Cost Unpredictability: Usage spikes require rapid resource scaling - budgeting more complex than fixed SaaS subscription No Commercial SLA: Community support without guaranteed response times or uptime commitments - not suitable for mission-critical 24/7 requirements Initial Setup Complexity: Docker configuration, OAuth setup, LLM integration, vector store setup requires technical deployment expertise Limited Ecosystem: Smaller ecosystem of third-party integrations, plugins, and turnkey solutions vs commercial platforms Production Readiness: Requires significant effort to operationalize (monitoring, logging, alerting, security hardening, disaster recovery)	Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models) Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory) Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Customization & Flexibility
N/A	Knowledge Updates: Add/remove files anytime via Admin UI or API - continuous indexing without downtime External Sync: Automated data source refresh from Google Drive, S3, Confluence, Notion (near real-time updates) Behavior Customization: Edit prompt templates and system logic for tone, personality, response handling Chunking Strategies: Template-based chunking configurable per document type No GUI Toggles: Customization requires editing config files or source code Ultimate Freedom: Integrate translation, custom re-ranking, or specialized algorithms Deep Tuning Potential: Modify retrieval pipeline, add custom modules, extend functionality Developer Dependency: Specialized behavior changes assume technical expertise	N/A
Advanced R A G Capabilities
N/A	GraphRAG: Graph-based retrieval augmentation for relationship-aware knowledge extraction RAPTOR: Recursive abstractive processing for tree-organized retrieval Agentic Workflows: Multi-step reasoning, tool use, code execution in sandbox Hybrid Search: Combines full-text (lexical) + vector (semantic) + ML re-ranking Template-Based Chunking: Document-type-specific chunking strategies for optimal context Layout Recognition: Preserves document structure (headers, sections, tables) during parsing Multiple Recall: Retrieves candidates via multiple strategies, then fuses with re-ranking Cutting-Edge Research: Implements latest RAG techniques often before commercial platforms Code Sandbox: Enables agent to execute code safely for complex analytical tasks	N/A
Community & Innovation
N/A	GitHub Stars: 68,000+ stars - top open-source RAG project Growth Recognition: GitHub Octoverse 2024 - fastest-growing open-source AI project Active Development: Frequent releases, rapid feature additions, responsive maintainers Community Contributions: Plugins, integrations, tutorials from global developer community Innovation Leadership: Introduces cutting-edge RAG techniques (hybrid retrieval, deep parsing) early Transparency: Open-source codebase enables full audit and understanding of retrieval logic Learning Resource: Serves as reference implementation for RAG best practices Ecosystem Growth: Third-party tools, wrappers, and integrations emerging from community Research Alignment: Implements latest academic RAG research in production-ready form	N/A

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Final Thoughts

Final Verdict: Progress Agentic RAG vs RAGFlow

After analyzing features, pricing, performance, and user feedback, both Progress Agentic RAG and RAGFlow are capable platforms that serve different market segments and use cases effectively.

When to Choose Progress Agentic RAG

You value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility
Genuine no-code accessibility: business users (marketing, HR, legal, support) can deploy functional RAG pipelines in minutes via visual dashboard

Best For: Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors

When to Choose RAGFlow

You value truly open-source (apache 2.0) with 68k+ github stars - vibrant community
State-of-the-art hybrid retrieval with multiple recall + fused re-ranking
Deep document understanding extracts knowledge from complex formats (OCR, layouts)

Best For: Truly open-source (Apache 2.0) with 68K+ GitHub stars - vibrant community

Migration & Switching Considerations

Switching between Progress Agentic RAG and RAGFlow requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

Progress Agentic RAG starts at $700/month, while RAGFlow begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Progress Agentic RAG and RAGFlow comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.

📚 Next Steps

Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.

• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements

Last updated: December 11, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.

Progress Agentic RAG vs RAGFlow