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

OpenAI excels in Industry-leading model performance and Comprehensive API features, scoring 90/100 in user ratings with 95% accuracy. Progress Agentic RAG leads 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 with 82/100 overall rating and N/A% accuracy. Key technical difference: OpenAI focuses on GPT-4 and GPT-3.5 models while Progress Agentic RAG emphasizes REMi evaluation model v2 (Llama 3.2-3B) for continuous quality scoring: answer relevance, context relevance, groundedness, correctness (0-5 scale). Pricing differs by $700/month.

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

OpenAI offers SOC 2 compliance for enterprise security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. OpenAI leads with 90/100 enterprise readiness score (85/100 enterprise features).

How much does OpenAI cost vs Progress Agentic RAG?

OpenAI starts at custom pricing/usage-based (pay-as-you-go model), while Progress Agentic RAG begins at $700/monthly (paid model). OpenAI offers 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 Progress Agentic RAG to OpenAI?

Migration from Progress Agentic RAG to OpenAI typically requires API-based data export/import, which OpenAI provides. OpenAI provides comprehensive API access excellent documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact OpenAI support for migration assistance and best practices.

What integrations do OpenAI and Progress Agentic RAG support?

OpenAI integrates with REST API, Python SDK, Node.js SDK, Community libraries, Azure OpenAI Service (8 integrations total). Progress Agentic RAG supports 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 available). Both offer REST APIs for custom integrations. OpenAI offers 14% more integrations.

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

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

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

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

How much does OpenAI cost vs Progress Agentic RAG?

OpenAI starts at custom pricing/month (subscription model), while Progress Agentic RAG 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 OpenAI to Progress Agentic RAG?

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

What integrations do OpenAI and Progress Agentic RAG support?

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

OpenAI vs Progress Agentic RAG

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

More about Progress Agentic RAG More about OpenAI 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 OpenAI 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 OpenAI 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 OpenAI if: you value industry-leading model performance
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 OpenAI

OpenAI is leading ai research company and api provider. OpenAI provides state-of-the-art language models and AI capabilities through APIs, including GPT-4, assistants with retrieval capabilities, and various AI tools for developers and enterprises. Founded in 2015, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

90/100

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, OpenAI in overall satisfaction. From a cost perspective, OpenAI starts at a lower price point. The platforms also differ in their primary focus: AI 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

OpenAI	Progress Agentic RAG	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
OpenAI gives you the GPT brains, but no ready-made pipeline for feeding it your documents—if you want RAG, you’ll build it yourself. The typical recipe: embed your docs with the OpenAI Embeddings API, stash them in a vector DB, then pull back the right chunks at query time. If you’re using Azure, the “Assistants” preview includes a beta File Search tool that accepts uploads for semantic search, though it’s still minimal and in preview. You’re in charge of chunking, indexing, and refreshing docs—there’s no turnkey ingestion service straight from OpenAI.	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
OpenAI doesn’t ship Slack bots or website widgets—you wire GPT into those channels yourself (or lean on third-party libraries). The API is flexible enough to run anywhere, but everything is manual—no out-of-the-box UI or integration connectors. Plenty of community and partner options exist (Slack GPT bots, Zapier actions, etc.), yet none are first-party OpenAI products. Bottom line: OpenAI is channel-agnostic—you get the engine and decide where it lives.	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
GPT-4 and GPT-3.5 handle multi-turn chat as long as you resend the conversation history; OpenAI doesn’t store “agent memory” for you. Out of the box, GPT has no live data hook—you supply retrieval logic or rely on the model’s built-in knowledge. “Function calling” lets the model trigger your own functions (like a search endpoint), but you still wire up the retrieval flow. The ChatGPT web interface is separate from the API and isn’t brand-customizable or tied to your private data by default.	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 turnkey chat UI to re-skin—if you want a branded front-end, you’ll build it. System messages help set tone and style, yet a polished white-label chat solution remains a developer project. ChatGPT custom instructions apply only inside ChatGPT itself, not in an embedded widget. In short, branding is all on you—the API focuses purely on text generation, with no theming layer.	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
Choose from GPT-3.5 (including 16k context), GPT-4 (8k / 32k), and newer variants like GPT-4 128k or “GPT-4o.” It’s an OpenAI-only clubhouse—you can’t swap in Anthropic or other providers within their service. Frequent releases bring larger context windows and better models, but you stay locked to the OpenAI ecosystem. No built-in auto-routing between GPT-3.5 and GPT-4—you decide which model to call and when.	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)
Excellent docs and official libraries (Python, Node.js, more) make hitting ChatCompletion or Embedding endpoints straightforward. You still assemble the full RAG pipeline—indexing, retrieval, and prompt assembly—or lean on frameworks like LangChain. Function calling simplifies prompting, but you’ll write code to store and fetch context data. Vast community examples and tutorials help, but OpenAI doesn’t ship a reference RAG architecture.	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
GPT-4 is top-tier for language tasks, but domain accuracy needs RAG or fine-tuning. Without retrieval, GPT can hallucinate on brand-new or private info outside its training set. A well-built RAG layer delivers high accuracy, but indexing, chunking, and prompt design are on you. Larger models (GPT-4 32k/128k) can add latency, though OpenAI generally scales well under load.	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)
You can fine-tune (GPT-3.5) or craft prompts for style, but real-time knowledge injection happens only through your RAG code. Keeping content fresh means re-embedding, re-fine-tuning, or passing context each call—developer overhead. Tool calling and moderation are powerful but require thoughtful design; no single UI manages persona or knowledge over time. Extremely flexible for general AI work, but lacks a built-in document-management layer for live updates.	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
Pay-as-you-go token billing: GPT-3.5 is cheap (~$0.0015/1K tokens) while GPT-4 costs more (~$0.03-0.06/1K). [OpenAI API Rates] Great for low usage, but bills can spike at scale; rate limits also apply. No flat-rate plan—everything is consumption-based, plus you cover any external hosting (e.g., vector DB). [API Reference] Enterprise contracts unlock higher concurrency, compliance features, and dedicated capacity after a chat with sales.	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
API data isn’t used for training and is deleted after 30 days (abuse checks only). [Data Policy] Data is encrypted in transit and at rest; ChatGPT Enterprise adds SOC 2, SSO, and stronger privacy guarantees. Developers must secure user inputs, logs, and compliance (HIPAA, GDPR, etc.) on their side. No built-in access portal for your users—you build auth in your own front-end.	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
A basic dashboard tracks monthly token spend and rate limits in the dev portal. No conversation-level analytics—you’ll log Q&A traffic yourself. Status page, error codes, and rate-limit headers help monitor uptime, but no specialized RAG metrics. Large community shares logging setups (Datadog, Splunk, etc.), yet you build the monitoring pipeline.	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
Massive dev community, thorough docs, and code samples—direct support is limited unless you’re on enterprise. Third-party frameworks abound, from Slack GPT bots to LangChain building blocks. OpenAI tackles broad AI tasks (text, speech, images)—RAG is just one of many use cases you can craft. ChatGPT Enterprise adds premium support, success managers, and a compliance-friendly environment.	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
Great when you need maximum freedom to build bespoke AI solutions, or tasks beyond RAG (code gen, creative writing, etc.). Regular model upgrades and bigger context windows keep the tech cutting-edge. Best suited to teams comfortable writing code—near-infinite customization comes with setup complexity. Token pricing is cost-effective at small scale but can climb quickly; maintaining RAG adds ongoing dev effort.	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
OpenAI alone isn't no-code for RAG—you'll code embeddings, retrieval, and the chat UI. The ChatGPT web app is user-friendly, yet you can't embed it on your site with your data or branding by default. No-code tools like Zapier or Bubble offer partial integrations, but official OpenAI no-code options are minimal. Extremely capable for developers; less so for non-technical teams wanting a self-serve domain chatbot.	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: Leading AI model provider offering state-of-the-art GPT models (GPT-4, GPT-3.5) as building blocks for custom AI applications, requiring developer implementation for RAG functionality Target customers: Development teams building bespoke AI solutions, enterprises needing maximum flexibility for diverse AI use cases beyond RAG (code generation, creative writing, analysis), and organizations comfortable with DIY RAG implementation using LangChain/LlamaIndex frameworks Key competitors: Anthropic Claude API, Google Gemini API, Azure AI, AWS Bedrock, and complete RAG platforms like CustomGPT/Vectara that bundle retrieval infrastructure Competitive advantages: Industry-leading GPT-4 model performance, frequent model upgrades with larger context windows (128k), excellent developer documentation with official Python/Node.js SDKs, massive community ecosystem with extensive tutorials and third-party integrations, ChatGPT Enterprise for compliance-friendly deployment with SOC 2/SSO, and API data not used for training (30-day retention for abuse checks only) Pricing advantage: Pay-as-you-go token pricing highly cost-effective at small scale ($0.0015/1K tokens GPT-3.5, $0.03-0.06/1K GPT-4); no platform fees or subscriptions beyond API usage; best value for low-volume use cases or teams with existing infrastructure (vector DB, embeddings) who only need LLM layer; can become expensive at scale without optimization Use case fit: Ideal for developers building custom AI solutions requiring maximum flexibility, teams working on diverse AI tasks beyond RAG (code generation, creative writing, analysis), and organizations with existing ML infrastructure who want best-in-class LLM without bundled RAG platform; less suitable for teams wanting turnkey RAG chatbot without development resources	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
GPT-4 Family: GPT-4 (8k/32k context), GPT-4 Turbo (128k context), GPT-4o (optimized) - industry-leading language understanding and generation GPT-3.5 Family: GPT-3.5 Turbo (4k/16k context) - cost-effective for high-volume applications with good performance Frequent Model Upgrades: Regular releases with improved capabilities, larger context windows, and better performance benchmarks OpenAI-Only Ecosystem: Cannot swap to Anthropic Claude, Google Gemini, or other providers - locked to OpenAI models No Auto-Routing: Developers explicitly choose which model to call per request - no automatic GPT-3.5/GPT-4 selection based on complexity Fine-Tuning Available: GPT-3.5 fine-tuning for domain-specific customization with training data Cutting-Edge Performance: GPT-4 consistently ranks top-tier for language tasks, reasoning, and complex problem-solving in benchmarks	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
NO Built-In RAG: OpenAI provides LLM models only - developers must build entire RAG pipeline (embeddings, vector DB, retrieval, prompting) Embeddings API: text-embedding-ada-002 and newer models for generating vector embeddings from text for semantic search DIY Architecture: Typical RAG implementation: embed documents → store in external vector DB (Pinecone, Weaviate) → retrieve at query time → inject into GPT prompt Azure Assistants Preview: Azure OpenAI Service offers beta File Search tool with uploads for semantic search (minimal, preview-stage) Function Calling: Enables GPT to trigger external functions (like retrieval endpoints) but requires developer implementation Framework Integration: Works with LangChain, LlamaIndex for RAG scaffolding - but these are third-party tools, not OpenAI products Developer Responsibility: Chunking strategies, indexing pipelines, retrieval optimization, context management all require custom code NO Turnkey RAG Service: Unlike RAG platforms with managed infrastructure, OpenAI leaves retrieval architecture entirely to developers	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
Custom AI Applications: Building bespoke solutions requiring maximum flexibility beyond pre-packaged chatbot platforms Code Generation: GitHub Copilot-style tools, IDE integrations, automated code review, and development acceleration Creative Writing: Content generation, marketing copy, storytelling, and creative ideation at scale Data Analysis: Natural language queries over structured data, report generation, and insight extraction Customer Service: Custom chatbots for support workflows integrated with business systems and knowledge bases Education: Tutoring systems, adaptive learning platforms, and educational content generation Research & Summarization: Document analysis, literature review, and multi-document summarization Enterprise Automation: Workflow automation, document processing, and business intelligence with ChatGPT Enterprise NOT IDEAL FOR: Non-technical teams wanting turnkey RAG chatbot without coding - better served by complete RAG platforms	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
API Data Privacy: API data not used for training - deleted after 30 days (abuse check retention only) ChatGPT Enterprise: SOC 2 Type II compliant with SSO, stronger privacy guarantees, and enterprise-grade security Encryption: Data encrypted in transit (TLS) and at rest with enterprise-grade standards GDPR Support: Data Processing Addendum (DPA) available for API and enterprise customers for GDPR compliance HIPAA Compliance: Business Associate Agreement (BAA) available for API healthcare customers supporting HIPAA requirements Regional Data Residency: Eligible customers (Enterprise, Edu, API) can select regional data residency (e.g., Europe) Zero-Retention Option: Enterprise/API customers can opt for no data retention at all for maximum privacy Developer Responsibility: Application-level security (user auth, input validation, logging) entirely on developers - not provided by OpenAI Third-Party Audits: SOC 2 Type 2 evaluated by independent auditors for API and enterprise products	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
Pay-As-You-Go Tokens: $0.0015/1K tokens GPT-3.5 Turbo (input), ~$0.03-0.06/1K tokens GPT-4 depending on model variant No Platform Fees: Pure consumption pricing - no subscriptions, monthly minimums, or seat-based fees beyond API usage Embeddings Pricing: Separate cost for text-embedding models used in RAG workflows (~$0.0001/1K tokens) Rate Limits by Tier: Usage tiers automatically increase limits as spending grows (Tier 1: 3,500 RPM / 200K TPM for GPT-3.5) ChatGPT Enterprise: Custom pricing with higher rate limits, dedicated capacity, and compliance features after sales engagement Cost at Scale: Bills can spike without optimization - high-volume applications need token management strategies External Costs: RAG implementations incur additional costs for vector databases (Pinecone, Weaviate) and hosting infrastructure Best Value For: Low-volume use cases or teams with existing infrastructure who only need LLM layer - becomes expensive at scale No Free Tier: Trial credits may be available for new accounts, but ongoing usage requires payment	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
Excellent Documentation: Comprehensive at platform.openai.com with API reference, guides, code samples, and best practices Official SDKs: Python, Node.js, and other language libraries with well-maintained code examples and tutorials Massive Community: Extensive third-party tutorials, LangChain/LlamaIndex integrations, and developer ecosystem resources Limited Direct Support: Community forums and documentation for standard API users - direct support requires Enterprise plan ChatGPT Enterprise: Premium support with dedicated success managers, priority assistance, and custom SLAs Status Page: Uptime monitoring and incident notifications at status.openai.com OpenAI Cookbook: Practical examples and recipes for common use cases including RAG patterns Third-Party Frameworks: LangChain, LlamaIndex, and other tools provide RAG scaffolding with OpenAI integration Developer Community: Active forums, GitHub discussions, and Stack Overflow for peer-to-peer assistance	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
NO Built-In RAG: Entire retrieval infrastructure must be built by developers - not turnkey knowledge base solution NO Managed Vector DB: Must integrate external vector databases (Pinecone, Weaviate, Qdrant) for embeddings storage Developer-Only: Requires coding expertise - no no-code interface for non-technical teams Rate Limits: Usage tiers start restrictive (Tier 1: 500 RPM for GPT-4) - high-volume apps need tier upgrades Model Lock-In: Cannot use Anthropic Claude, Google Gemini, or other providers - tied to OpenAI ecosystem Hallucination Without RAG: GPT-4 can hallucinate on private/recent data without proper retrieval implementation Context Window Costs: Larger models (GPT-4 128k) increase latency and costs - require optimization strategies NO Chat UI: ChatGPT web interface separate from API - not embeddable or customizable for business use DIY Monitoring: Application-level logging, analytics, and observability entirely on developers to implement RAG Maintenance: Ongoing effort for keeping embeddings updated, managing vector DB, and optimizing retrieval pipelines Cost at Scale: Token pricing can spike without careful optimization - high-volume applications need cost management Best For Developers: Maximum flexibility for technical teams, but inappropriate for non-coders wanting self-serve chatbot	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
Assistants API (v2): Build AI assistants with built-in conversation history management, persistent threads, and tool access - removes need to manually track context Function Calling: Models can describe and invoke external functions/tools - describe structure to Assistant and receive function calls with arguments to execute Parallel Tool Execution: Assistants access multiple tools simultaneously - Code Interpreter, File Search, and custom functions via function calling in parallel Built-In Tools: OpenAI-hosted Code Interpreter (Python code execution in sandbox), File Search (retrieval over uploaded files in beta), web search (Responses API only) Responses API (New 2024): New primitive combining Chat Completions simplicity with Assistants tool-use capabilities - supports web search, file search, computer use Structured Outputs: Launched June 2024 - strict: true in function definition guarantees arguments match JSON Schema exactly for reliable parsing Assistants API Deprecation: Plans to deprecate Assistants API after Responses API achieves feature parity - target sunset H1 2026 Custom Tool Integration: Build and host custom tools accessed through function calling - agents can invoke your APIs, databases, services Multi-Turn Conversations: Assistants maintain conversation state across multiple turns without manual history management Agent Limitations: Less control vs LangChain/LlamaIndex for complex agentic workflows - simpler assistant paradigm not full autonomous agents NO Multi-Agent Orchestration: No built-in support for coordinating multiple specialized agents - requires custom implementation Tool Use Growth: Function calling enables agentic behavior where model decides when to take action vs always responding with text	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: NOT RAG-AS-A-SERVICE - OpenAI provides LLM models and basic tool APIs, not managed RAG infrastructure Core Focus: Best-in-class language models (GPT-4, GPT-3.5) as building blocks - RAG implementation entirely on developers DIY RAG Architecture: Typical workflow: embed docs with Embeddings API → store in external vector DB (Pinecone/Weaviate) → retrieve at query time → inject into prompt File Search Tool (Beta): Azure OpenAI Assistants preview includes minimal File Search for semantic search over uploads - still preview-stage, not production RAG service No Managed Infrastructure: Unlike true RaaS (CustomGPT, Vectara, Nuclia), OpenAI leaves chunking, indexing, retrieval, vector storage to developers Framework Integration: Works with LangChain, LlamaIndex for RAG scaffolding - but these are third-party tools, not OpenAI products Developer Responsibility: Chunking strategies, indexing pipelines, retrieval optimization, context management all require custom code Framework vs Service: Comparison to RAG-as-a-Service platforms invalid - fundamentally different category (LLM API vs managed RAG platform) Best Comparison Category: Direct LLM APIs (Anthropic Claude API, Google Gemini API, AWS Bedrock) or developer frameworks (LangChain) NOT managed RAG services Use Case Fit: Teams building custom AI applications requiring maximum LLM flexibility vs organizations wanting turnkey RAG chatbot without coding External Costs: RAG implementations incur additional costs: vector databases (Pinecone $70+/month), hosting infrastructure, embeddings API calls Hosted Alternatives: For managed RAG-as-a-Service, consider CustomGPT, Vectara, Nuclia, Azure AI Search, AWS Kendra - not OpenAI API alone	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: OpenAI vs Progress Agentic RAG

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

When to Choose OpenAI

You value industry-leading model performance
Comprehensive API features
Regular model updates

Best For: Industry-leading model performance

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 OpenAI 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

OpenAI 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

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 OpenAI 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 12, 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.

OpenAI vs Progress Agentic RAG