Deepset vs Progress Agentic RAG

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

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Priyansh Khodiyar's avatar
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 Deepset and Progress Agentic RAG 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 Deepset and Progress Agentic RAG, 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 Deepset if: you value mature open-source framework (since 2020)
  • 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

About Deepset

Deepset Landing Page Screenshot

Deepset is open-source framework and enterprise platform for llm orchestration. Deepset is the creator of Haystack, the leading open-source framework for building production-ready LLM applications, and offers an enterprise AI platform for developing and deploying custom AI agents and applications. Founded in 2018, headquartered in Berlin, Germany, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
83/100
Starting Price
Custom

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

Key Differences at a Glance

In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, Deepset starts at a lower price point. The platforms also differ in their primary focus: AI Development Platform versus Enterprise Software. 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

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Deepset
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Progress Agentic RAG
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Gives developers a flexible framework to wire up connectors and process nearly any file type or data source with libraries like Unstructured.
  • Lets you push content into vector stores such as OpenSearch, Pinecone, Weaviate, or Snowflake—pick the backend that fits best. Learn more
  • Setup is hands-on, but the payoff is deep, domain-specific customization of your ingestion pipelines.
  • 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)
  • 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
  • API-first approach—drop the RAG system into your own app through REST endpoints or the Haystack SDK.
  • Shareable pipeline prototypes are great for demos, but production channels (Slack bots, web chat, etc.) need a bit of custom code. See prototype feature
  • 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
  • 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
  • Builds RAG agents as modular pipelines—retriever + reader, plus optional rerankers or multi-step logic.
  • Multi-turn chat? Source attributions? Fine-grained retrieval tweaks? All possible with the right config. Pipeline overview
  • Advanced users can layer in tool use and external API calls for richer agent behavior.
  • 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
  • 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.
Customization & Branding
  • No drag-and-drop theming here—you’ll craft your own front end if you need branded UI.
  • That also means full freedom to shape the visuals and conversational tone any way you like. Custom components
  • 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
  • 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
  • Model-agnostic: plug in GPT-4, Llama 2, Claude, Cohere, and more—whatever works for you.
  • Switch models or embeddings through the “Connections” UI with just a few clicks. View supported models
  • 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
  • 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)
  • Comprehensive REST API plus the open-source Haystack SDK for building, running, and querying pipelines.
  • Deepset Studio’s visual editor lets you drag-and-drop components, then export YAML for version control. Studio overview
  • 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
  • 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
  • Tune for max accuracy with multi-step retrieval, hybrid search, and custom rerankers.
  • Mix and match components to hit your latency targets—even at large scale. Benchmark insights
  • 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)
  • 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)
  • Build anything: multi-hop retrieval, custom logic, bespoke prompts—your pipeline, your rules.
  • Create multiple datastores, add role-based filters, or pipe in external APIs as extra tools. Component templates
  • 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)
  • 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
  • Start free in Deepset Studio, then move to usage-based Enterprise plans as you scale.
  • Deploy in cloud, hybrid, or on-prem setups to handle huge corpora and heavy traffic. Pricing overview
  • 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
  • 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
  • SOC 2 Type II, ISO 27001, GDPR, HIPAA—you’re covered for enterprise compliance.
  • Choose cloud, VPC, or on-prem to keep data exactly where you need it. Security compliance
  • 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
  • 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
  • Deepset Studio dashboard shows latency, error rates, resource use—everything you’d expect.
  • Detailed logs integrate with Prometheus, Splunk, and more for deep observability. Monitoring features
  • 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)
  • 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
  • Lean on the Haystack open-source community (Discord, GitHub) or paid enterprise support. Community insights
  • Wide ecosystem of vector DBs, model providers, and ML tools means plenty of plug-ins and extensions.
  • 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
  • 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.
Additional Considerations
  • Perfect for teams that need heavily customized, domain-specific RAG solutions.
  • Full control and future portability—but expect a steeper learning curve and more dev effort. More details
  • 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
  • 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.
No- Code Interface & Usability
  • Deepset Studio offers low-code drag-and-drop, yet it's still aimed at developers and ML engineers.
  • Non-tech users may need help, and production UIs will be custom-built.
  • 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
  • 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.
Competitive Positioning
  • Market position: Developer-first RAG framework (Haystack) with enterprise cloud offering (Deepset Cloud) for heavily customized, domain-specific RAG solutions
  • Target customers: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, and organizations wanting future portability with open-source foundation
  • Key competitors: LangChain/LangSmith, Contextual.ai, Dataworkz, Vectara.ai, and custom implementations using Pinecone/Weaviate
  • Competitive advantages: Open-source Haystack framework for full portability, model-agnostic with easy model switching via Connections UI, Deepset Studio visual pipeline editor with YAML export for version control, modular components (retriever, reader, reranker) for maximum flexibility, wide ecosystem of vector DB integrations (OpenSearch, Pinecone, Weaviate, Snowflake), and SOC 2/ISO 27001/GDPR/HIPAA compliance with cloud/VPC/on-prem deployment
  • Pricing advantage: Free Deepset Studio for development, then usage-based Enterprise plans; competitive for teams wanting deep customization without vendor lock-in; best value comes from open-source foundation enabling future migration if needed
  • Use case fit: Perfect for teams needing heavily customized, domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, and ML engineers wanting developer-friendly APIs with future portability through open-source Haystack foundation
  • 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
  • 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
A I Models
  • Model-agnostic architecture: Supports GPT-4, GPT-3.5, Claude (Anthropic), Llama 2, Cohere, and 80+ model providers through unified interface
  • Easy model switching: Change models via Connections UI with just a few clicks without code changes
  • Embedding models: OpenAI, Cohere, Sentence Transformers, and custom embedding models supported
  • Multiple LLMs per pipeline: Use different models for different pipeline components (retrieval vs generation)
  • Custom model fine-tuning: Fine-tune on proprietary data for domain-specific terminology and accuracy
  • Baseline models available: Pre-configured with common models for quick prototyping
  • 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)
  • 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
  • Advanced RAG architecture: Multi-step retrieval, hybrid search (semantic + keyword), and custom rerankers for maximum accuracy
  • Modular pipeline design: Flexible retriever + reader + optional reranker components for customized workflows
  • Multi-hop retrieval: Chain multiple retrieval steps for complex queries requiring deep context
  • Vector database flexibility: OpenSearch, Pinecone, Weaviate, Snowflake, Qdrant, and more - choose your preferred backend
  • Benchmark-proven performance: Published performance metrics on MTEB and domain-specific benchmarks
  • Source attribution: Full citation tracking with document references and confidence scores
  • Haystack framework: Open-source foundation enables complete RAG customization and future portability
  • 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
  • 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
  • Domain-specific Q&A: Enterprise knowledge bases with specialized terminology requiring fine-tuned models
  • Research and analysis: Multi-hop retrieval for complex research questions across large document corpora
  • Technical documentation: Developer-focused RAG for code documentation, API references, and technical guides
  • Compliance and legal: HIPAA/GDPR-compliant RAG systems for regulated industries requiring on-prem deployment
  • Custom AI agents: Build specialized agents with external API calls, tool use, and multi-step reasoning
  • Enterprise search: Large-scale search across millions of documents with hybrid retrieval and reranking
  • Future-proof AI: Migrate between LLM providers, vector databases, and hosting options without vendor lock-in
  • 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
  • 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
  • SOC 2 Type II certification: Annual audits ensuring enterprise security standards
  • ISO 27001 certification: International information security management compliance
  • GDPR compliance: European data protection regulation adherence with data sovereignty options
  • HIPAA compliance: Healthcare data protection standards for sensitive medical information
  • Flexible deployment: Cloud, hybrid, VPC, or on-premises deployment for complete data control
  • Data residency options: Choose where data is stored and processed (US, EU, on-prem)
  • No model training on customer data: Customer data never used to train third-party models
  • Audit trails: Comprehensive logging of all queries, retrievals, and system access
  • 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
  • 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
  • Deepset Studio (Free): Development environment with unlimited files and core features for prototyping
  • Enterprise pricing: Custom usage-based pricing based on queries, documents indexed, and compute resources
  • Deployment options pricing: Cloud (managed SaaS), hybrid, or on-premises with separate pricing tiers
  • No per-seat charges: Usage-based model scales with actual platform usage, not team size
  • Professional services: Optional consulting, integration support, and custom pipeline development available
  • Scaling flexibility: Enterprise plans handle huge corpora (millions of documents) and heavy traffic loads
  • Open-source advantage: Haystack framework free forever - only pay for managed cloud services if needed
  • 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
  • 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
  • Haystack community: Active Discord server and GitHub community (14,000+ stars) with responsive maintainers
  • Enterprise support tiers: Email, Slack Connect channels, and dedicated support engineers for paid customers
  • Comprehensive documentation: docs.cloud.deepset.ai with tutorials, API references, and integration guides
  • Video tutorials: YouTube channel with pipeline building guides and best practices
  • GitHub examples: Open-source example projects and starter templates for common use cases
  • Integration ecosystem: Wide community of vector DB providers, model vendors, and tool developers
  • Professional services: Custom development, architecture consulting, and hands-on implementation support available
  • 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
  • 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
  • Steeper learning curve: Developer-first platform requires ML/engineering skills - not ideal for non-technical users
  • Custom UI required: No drag-and-drop chat widget - must build production interfaces from scratch
  • Hands-on setup: More initial configuration effort compared to plug-and-play SaaS platforms
  • Deepset Studio limitations: Visual editor still aimed at technical users - requires understanding of RAG concepts
  • Production readiness: Moving from Studio prototype to production deployment requires additional DevOps work
  • Enterprise costs: Usage-based pricing can become expensive at high query volumes without careful optimization
  • Best for technical teams: Maximum value requires ML engineers and developers - not suited for business users seeking no-code solutions
  • Integration effort: Native integrations like Slack bots require custom code vs turnkey options from competitors
  • 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
  • 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
Core Agent Features
  • AI Agents with Haystack: Build LLM-powered autonomous agents that can reason, reflect, and act using tools, data, and critical introspection into their own decision-making processes Building Agents
  • Spectrum Approach: Combines structured workflows with autonomous capabilities - AI systems exist on a spectrum between linearity and autonomy based on decision-making capability needs Agentic Spectrum
  • Planning Mechanisms: Agents break tasks into steps using chain-of-thought or tree-of-thought planning, enabling complex multi-step reasoning and execution
  • Dynamic Routing: LLMs serve as "brains" of decision systems, using reasoning capabilities to evaluate and choose among multiple tools, courses of action, databases, and resources based on context and goals
  • Reflection & Self-Correction: Agents analyze intermediate results through reflection mechanisms, improving accuracy and adapting strategies based on outcomes
  • Tool Integration: Modular pipeline design allows agents to use retriever, reader, reranker components, external API calls, and custom tools for richer autonomous behavior
  • Agentic RAG Enhancement: Build agentic RAG pipelines in Deepset Studio that combine graphs, agentic properties, multimodal capabilities, and innovations to significantly reduce inaccurate or misleading information Agentic RAG Guide
  • Custom Workflows: Create anything from multi-hop retrieval to custom logic to bespoke prompts - modular components enable building specialized agents for domain-specific autonomous workflows
  • 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)
  • 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
R A G-as-a- Service Assessment
  • Platform Type: HYBRID RAG FRAMEWORK + CLOUD SERVICE - open-source Haystack foundation with enterprise Deepset Cloud offering for heavily customized, domain-specific RAG solutions
  • Core Architecture: Modular pipeline architecture with retriever + reader + optional reranker components, full control over embedding models, vector databases (OpenSearch, Pinecone, Weaviate, Snowflake), and chunking strategies
  • Agentic Capabilities: Build autonomous AI agents with planning, routing, reflection mechanisms using Haystack framework - supports agentic RAG pipelines with graphs and multimodal capabilities Agent Development
  • Developer Experience: Comprehensive REST API, open-source Haystack SDK, Deepset Studio visual pipeline editor with YAML export for version control - targets ML engineers and development teams Studio Overview
  • No-Code Capabilities: Deepset Studio offers drag-and-drop visual editor for pipeline building, but still aimed at developers and ML engineers - not accessible to non-technical users
  • Target Market: ML engineers and development teams needing deep RAG customization, enterprises requiring domain-specific solutions with modular pipeline architecture, organizations wanting future portability with open-source foundation
  • RAG Technology Leadership: Advanced RAG with multi-step retrieval, hybrid search (semantic + keyword), custom rerankers for maximum accuracy, model-agnostic support (GPT-4, Llama 2, Claude, Cohere, 80+ providers), and benchmark-proven performance on MTEB Benchmark Insights
  • Deployment Flexibility: Free Deepset Studio for development, usage-based Enterprise plans, cloud/VPC/on-prem deployment options, and SOC 2/ISO 27001/GDPR/HIPAA compliance with flexible data residency
  • Enterprise Readiness: SOC 2 Type II, ISO 27001, GDPR, HIPAA compliance, cloud/hybrid/on-prem deployment, no model training on customer data, and comprehensive audit trails
  • Use Case Fit: Perfect for teams needing heavily customized domain-specific RAG with multi-hop retrieval and custom rerankers, organizations requiring modular pipeline architecture for complex workflows, ML engineers wanting developer-friendly APIs with future portability
  • Open-Source Advantage: Haystack framework (14,000+ GitHub stars) free forever with full portability - only pay for managed Deepset Cloud services if needed, avoiding vendor lock-in
  • NOT Suitable For: Non-technical teams seeking turnkey chatbots, business users wanting no-code deployment, organizations needing pre-built chat widgets or Slack/WhatsApp integrations
  • Competitive Positioning: Competes with LangChain/LangSmith, Contextual.ai, Dataworkz - differentiates through open-source Haystack foundation, model-agnostic flexibility, visual pipeline editor, and wide vector DB ecosystem
  • 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-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
R E Mi Evaluation Model ( Core Differentiator)
N/A
  • 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
Open- Source Nuclia D B Foundation
N/A
  • 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
Multi- Lingual Support
N/A
  • 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
Deployment & Infrastructure
N/A
  • 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
N/A
Customer Base & Case Studies
N/A
  • 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

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Final Thoughts

Final Verdict: Deepset vs Progress Agentic RAG

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

When to Choose Deepset

  • You value mature open-source framework (since 2020)
  • Production-ready from day one
  • Highly modular and customizable

Best For: Mature open-source framework (since 2020)

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

Migration & Switching Considerations

Switching between Deepset and Progress Agentic RAG 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

Deepset starts at custom pricing, while Progress Agentic RAG begins at $700/month. 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

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

For most organizations, the decision between Deepset and Progress Agentic RAG 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's avatar

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.

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