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
✅ Embeddings API – text-embedding models generate vectors for semantic search workflows ⚠️ DIY Pipeline – No ready-made ingestion; build chunking, indexing, refreshing yourself Azure File Search – Beta preview tool accepts uploads for semantic search Manual Architecture – Embed docs → vector DB → retrieve chunks at query time	60+ Document Formats – PDF, Word, Excel, PPT, email with auto parsing Multimedia Processing – Auto speech-to-text, video transcripts, OCR for scans Cloud Connectors – SharePoint, Confluence, OneDrive, Google Drive, S3 Fast Deployment – 2-hour initial ingestion, 48-hour full deployment ⚠️ Missing – No Dropbox, Notion, or YouTube transcript integration	1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
⚠️ No First-Party Channels – Build Slack bots, widgets, integrations yourself or use third-party ✅ API Flexibility – Run GPT anywhere; channel-agnostic engine for custom implementations Community Tools – Zapier, community Slack bots exist but aren't official OpenAI Manual Wiring – Everything is code-based; no out-of-the-box UI or connectors	Python SDK – 21K weekly downloads, Python 3.8+ support JavaScript/TypeScript SDK – React, Next.js, Angular, Vue, Svelte support Progress Ecosystem – OpenEdge database, Sitefinity CMS integration ⚠️ No Messaging Channels – No native Slack, WhatsApp, Teams integrations	Website embedding – Lightweight JS widget or iframe with customizable positioning CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints LiveChat + Slack – Native chat widgets with human handoff capabilities
Core Chatbot Features
✅ Multi-Turn Chat – GPT-4/3.5 handle conversations; you resend history for context ⚠️ No Agent Memory – OpenAI doesn't store conversational state; you manage it Function Calling – Model triggers your functions (search endpoints); you wire retrieval ChatGPT Web UI – Separate from API; not brand-customizable for private data	AI Search & Answers – Semantic search with source-linked trusted answers Multi-Turn Conversations – Context-aware dialogue maintains conversation history Multilingual Support – Nuclia multilingual embedding model handles multiple languages ⚠️ Missing – No lead capture or human handoff workflows	✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI ✅ Source citations – Every response includes clickable links to original documents ✅ 93% resolution rate – Handles queries autonomously, reducing human workload ✅ 92 languages – Native multilingual support without per-language config ✅ Lead capture – Built-in email collection, custom forms, real-time notifications ✅ Human handoff – Escalation with full conversation context preserved
Customization & Branding
⚠️ No Turnkey UI – Build branded front-end yourself; no theming layer provided System Messages – Set tone/style via prompts; white-label chat requires development ChatGPT Custom Instructions – Apply only inside ChatGPT app, not embedded widgets Developer Project – All branding, UI customization is your responsibility	Prompt Lab – Test LLMs side-by-side using actual customer data 30+ RAG Parameters – Custom chunking, context size, hybrid search weighting Widget Customization – Visual editor for filters, metadata, thumbnails, answer modes RBAC – Account and Knowledge Box level permissions with SSO	Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost 2-minute setup – No-code wizard with drag-and-drop interface Persona customization – Control AI personality, tone, response style via pre-prompts Visual theme editor – Real-time preview of branding changes Domain allowlisting – Restrict embedding to approved sites only
L L M Model Options
✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o top-tier performance ✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective for high-volume use ⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI ecosystem Manual Routing – Developer chooses model per request; no automatic selection ✅ Frequent Upgrades – Regular releases with larger context windows and better benchmarks	7 Providers – Anthropic, OpenAI, Google, Meta Llama, Mistral, Cohere Nuclia Private GenAI – 100% data isolation for maximum security Model Switching – Change providers without architectural changes	GPT-5.1 models – Latest thinking models (Optimal & Smart variants) GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available Claude 4.5 – Anthropic's Opus available for Enterprise Auto model routing – Balances cost/performance automatically Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
✅ Excellent Docs – Official Python/Node.js SDKs; comprehensive API reference and guides Function Calling – Simplifies prompting; you build RAG pipeline (indexing, retrieval, assembly) Framework Support – Works with LangChain/LlamaIndex (third-party tools, not OpenAI products) ⚠️ No Reference Architecture – Vast community examples but no official RAG blueprint	Open-Source Foundation – NucliaDB 710+ stars, AGPLv3, Python/Rust Python SDK – 21K weekly downloads, full API coverage JavaScript/TypeScript SDK – React, Next.js, Angular, Vue, Svelte support REST API – Regional endpoints /ask, /find, /upload, /remi	REST API – Full-featured for agents, projects, data ingestion, chat queries Python SDK – Open-source `customgpt-client` with full API coverage Postman collections – Pre-built requests for rapid prototyping Webhooks – Real-time event notifications for conversations and leads OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Performance & Accuracy
✅ GPT-4 Top-Tier – Leading performance for language tasks; requires RAG for domain accuracy ⚠️ Hallucination Risk – Can hallucinate on private/recent data without retrieval implementation Well-Built RAG Delivers – High accuracy achievable with proper indexing, chunking, prompt design Latency Considerations – Larger models (128k context) add latency; scales well under load	✅ Benchmark Leader – Highest scores vs Vectara on Docmatix 1.4k dataset 100M Vectors – Fully ingested and optimized in ~20 minutes REMi v2 Speed – 30x faster inference, Llama 3.2-3B based Four-Index Hybrid – Document, Full Text, Vector/Chunk, Knowledge Graph	Sub-second responses – Optimized RAG with vector search and multi-layer caching Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API Anti-hallucination tech – Responses grounded only in your provided content OpenGraph citations – Rich visual cards with titles, descriptions, images 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Customization & Flexibility ( Behavior & Knowledge)
✅ Fine-Tuning Available – GPT-3.5 fine-tuning for style; knowledge injection via RAG code ⚠️ Content Freshness – Re-embed, re-fine-tune, or pass context each call; developer overhead Tool Calling Power – Powerful moderation/tools but requires thoughtful design; no unified UI Maximum Flexibility – Extremely flexible for general AI; lacks built-in document management	30+ RAG Parameters – Fine-grained control over retrieval behavior Hybrid Weighting – Balance keyword vs semantic search relevance Agent Autonomy – Auto select optimal strategies per query Prompt Lab – Test configurations with actual data before production	Live content updates – Add/remove content with automatic re-indexing System prompts – Shape agent behavior and voice through instructions Multi-agent support – Different bots for different teams Smart defaults – No ML expertise required for custom behavior
Pricing & Scalability
✅ Pay-As-You-Go – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing ⚠️ Scale Costs – Great low usage; bills spike at scale with rate limits No Flat Rate – Consumption-based only; cover external hosting (vector DB) separately Enterprise Contracts – Higher concurrency, compliance features, dedicated capacity via sales	Fly – $700/mo: 10GB/15K resources, 750MB max file Growth – $1,750/mo: 50GB/80K resources, 1.5GB max, Prompt Lab Enterprise – Custom: Unlimited data, 11 KBs, hybrid/on-prem ✅ Competitive Entry – $700/mo undercuts enterprise alternatives	Standard: $99/mo – 60M words, 10 bots Premium: $449/mo – 300M words, 100 bots Auto-scaling – Managed cloud scales with demand Flat rates – No per-query charges
Security & Privacy
✅ API Data Privacy – Not used for training; 30-day retention for abuse checks ✅ Encryption Standard – TLS in transit, at rest encryption; ChatGPT Enterprise adds SOC 2/SSO ⚠️ Developer Responsibility – You secure user inputs, logs, auth, HIPAA/GDPR compliance No User Portal – Build auth/access control in your own front-end	✅ SOC2 Type 2 – Annually audited enterprise security ✅ ISO 27001 – Annually audited information security management ✅ GDPR Compliant – Built-in PII anonymization auto-detects personal data Encryption – AES-256 at rest, TLS in transit ⚠️ NO HIPAA – Healthcare PHI processing requires sales clarification	SOC 2 Type II + GDPR – Third-party audited compliance Encryption – 256-bit AES at rest, SSL/TLS in transit Access controls – RBAC, 2FA, SSO, domain allowlisting Data isolation – Never trains on your data
Observability & Monitoring
⚠️ Basic Dashboard – Tracks monthly token spend, rate limits; no conversation analytics DIY Logging – Log Q&A traffic yourself; no specialized RAG metrics Status Page – Uptime monitoring, error codes, rate-limit headers available Community Solutions – Datadog/Splunk setups shared; you build monitoring pipeline	REMi Dashboard – Real-time metrics for relevance, groundedness, correctness (0-5) Rolling Averages – Performance graphs spanning 24h to 30 days Benchmark Validated – Tested vs Vectara on Docmatix 1.4k dataset ⚠️ No Proactive Alerts – Alerting not documented despite monitoring	Real-time dashboard – Query volumes, token usage, response times Customer Intelligence – User behavior patterns, popular queries, knowledge gaps Conversation analytics – Full transcripts, resolution rates, common questions Export capabilities – API export to BI tools and data warehouses
Support & Ecosystem
✅ Massive Community – Thorough docs, code samples; direct support requires Enterprise Third-Party Frameworks – Slack bots, LangChain, LlamaIndex building blocks abound Broad AI Focus – Text, speech, images; RAG is one of many use cases Enterprise Premium Support – Success managers, SLAs, compliance environment for Enterprise customers	Documentation – docs.rag.progress.cloud + legacy docs.nuclia.dev SDK Ecosystem – Python 21K weekly + JavaScript/TypeScript active usage Progress Support – 2,000+ employee parent company infrastructure AWS Marketplace – November 2025 for streamlined procurement	Comprehensive docs – Tutorials, cookbooks, API references Email + in-app support – Under 24hr response time Premium support – Dedicated account managers for Premium/Enterprise Open-source SDK – Python SDK, Postman, GitHub examples 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations
✅ Maximum Freedom – Best for bespoke AI solutions beyond RAG (code gen, creative writing) ✅ Regular Upgrades – Frequent model releases with bigger context windows keep tech current ⚠️ Coding Required – Near-infinite customization comes with setup complexity; developer-friendly only Cost Management – Token pricing cost-effective at small scale; maintaining RAG adds ongoing effort	✅ REMi Quality – 30x faster v2, continuous evaluation addresses hallucination ✅ Open-Source Trust – NucliaDB 710+ stars enables security audits ⚠️ Recent Acquisition – June 2025 Progress purchase, platform transitioning	Time-to-value – 2-minute deployment vs weeks with DIY Always current – Auto-updates to latest GPT models Proven scale – 6,000+ organizations, millions of queries Multi-LLM – OpenAI + Claude reduces vendor lock-in
No- Code Interface & Usability
⚠️ Not No-Code – Requires coding embeddings, retrieval, chat UI; no-code OpenAI options minimal ChatGPT Web App – User-friendly but not embeddable with your data/branding by default Third-Party Tools – Zapier/Bubble offer partial integrations; not official OpenAI solutions Developer-Focused – Extremely capable for coders; less for non-technical teams wanting self-serve	Target Users – Non-technical teams: marketing, HR, legal, support Visual Dashboard – Create KB, upload docs, deploy widget in one session Point-and-Click Editor – Configure suggestions, filters, metadata visually Rapid Deployment – Minutes-to-production capability for business users	2-minute deployment – Fastest time-to-value in the industry Wizard interface – Step-by-step with visual previews Drag-and-drop – Upload files, paste URLs, connect cloud storage In-browser testing – Test before deploying to production Zero learning curve – Productive on day one
Competitive Positioning
Market Position – Leading AI model provider; top GPT models as custom AI building blocks Target Customers – Dev teams building bespoke solutions; enterprises needing flexibility beyond RAG Key Competitors – Anthropic Claude API, Google Gemini, Azure AI, AWS Bedrock, RAG platforms ✅ Competitive Advantages – Top GPT-4 performance, frequent upgrades, excellent docs, massive ecosystem, Enterprise SOC 2/SSO ✅ Pricing Advantage – Pay-as-you-go highly cost-effective at small scale; best value low-volume use Use Case Fit – Ideal for custom AI requiring flexibility; less suitable for turnkey RAG without dev resources	✅ Pricing Advantage – $700/mo entry undercuts competitors ✅ REMi Differentiator – Continuous quality monitoring ⚠️ Integration Gaps – No native Slack/WhatsApp/Teams	Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox. Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster vs Botsonic/Chatbase – More file formats, source citations, no hidden costs vs LangChain – Production-ready in 2 min vs weeks of development
A I Models
✅ GPT-4 Family – GPT-4 (8k/32k), GPT-4 Turbo (128k), GPT-4o - top language understanding/generation ✅ GPT-3.5 Family – GPT-3.5 Turbo (4k/16k) cost-effective with good performance ✅ Frequent Upgrades – Regular releases with improved capabilities, larger context windows ⚠️ OpenAI-Only – Cannot swap to Claude, Gemini; locked to OpenAI models ✅ Fine-Tuning – GPT-3.5 fine-tuning for domain-specific customization with training data	7 Providers – Anthropic, OpenAI, Google, Meta, Mistral, Cohere Nuclia Private GenAI – 100% data isolation for security	OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series Anthropic – Claude 4.5 Opus/Sonnet (Enterprise) Auto-routing – Intelligent model selection for cost/performance Managed – No API keys or fine-tuning required
R A G Capabilities
⚠️ NO Built-In RAG – LLM models only; build entire RAG pipeline yourself ✅ Embeddings API – text-embedding-ada-002 and newer for vector embeddings/semantic search DIY Architecture – Embed docs → external vector DB → retrieve → inject into prompt Azure Assistants Preview – Beta File Search tool; minimal, preview-stage only Framework Integration – Works with LangChain/LlamaIndex (third-party, not OpenAI products) ⚠️ Developer Responsibility – Chunking, indexing, retrieval optimization all require custom code	Agentic RAG Engine – Retrieval agents auto-select strategies Four-Index Hybrid – Document, Full Text, Vector, Knowledge Graph REMi v2 Quality – Continuous evaluation, 30x faster Multimodal – OCR, speech-to-text, 60+ formats	GPT-4 + RAG – Outperforms OpenAI in independent benchmarks Anti-hallucination – Responses grounded in your content only Automatic citations – Clickable source links in every response Sub-second latency – Optimized vector search and caching Scale to 300M words – No performance degradation at scale
Use Cases
✅ Custom AI Applications – Bespoke solutions requiring maximum flexibility beyond pre-packaged platforms ✅ Code Generation – GitHub Copilot-style tools, IDE integrations, automated review ✅ Creative Writing – Content generation, marketing copy, storytelling at scale ✅ Data Analysis – Natural language queries over structured data, report generation Customer Service – Custom chatbots integrated with business systems and knowledge bases ⚠️ NOT IDEAL FOR – Non-technical teams wanting turnkey RAG chatbot without coding	Enterprise Knowledge – Deploy knowledge bases in minutes Healthcare & Pharma – Medical protocol search Developer RAG Backend – API-first for custom AI apps	Customer support – 24/7 AI handling common queries with citations Internal knowledge – HR policies, onboarding, technical docs Sales enablement – Product info, lead qualification, education Documentation – Help centers, FAQs with auto-crawling E-commerce – Product recommendations, order assistance
Security & Compliance
✅ API Data Privacy – Not used for training; 30-day retention for abuse checks only ✅ ChatGPT Enterprise – SOC 2 Type II, SSO, stronger privacy, enterprise-grade security ✅ Encryption – TLS in transit, at rest encryption with enterprise standards ✅ GDPR/HIPAA – DPA for GDPR; BAA for HIPAA; regional data residency available ✅ Zero-Retention Option – Enterprise/API customers can opt for no data retention ⚠️ Developer Responsibility – User auth, input validation, logging entirely on you	✅ SOC2 Type 2, ISO 27001, GDPR Encryption – AES-256 at rest, TLS in transit ⚠️ NO HIPAA – Healthcare requires sales contact	SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance 256-bit AES encryption – Data at rest; SSL/TLS in transit SSO + 2FA + RBAC – Enterprise access controls with role-based permissions Data isolation – Never trains on customer data Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
✅ Pay-As-You-Go – $0.0015/1K tokens GPT-3.5; ~$0.03-0.06/1K GPT-4 token pricing ✅ No Platform Fees – Pure consumption pricing; no subscriptions, monthly minimums Rate Limits by Tier – Usage tiers auto-increase limits as spending grows ⚠️ Cost at Scale – Bills spike without optimization; high-volume needs token management External Costs – RAG incurs vector DB (Pinecone, Weaviate) and hosting costs ✅ Best Value For – Low-volume use or teams with existing infrastructure	Fly – $700/mo: 10GB/15K resources Growth – $1,750/mo: 50GB/80K resources Enterprise – Custom: Unlimited, hybrid/on-prem	Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot Enterprise: Custom – SSO, dedicated support, custom SLAs 7-day free trial – Full Standard access, no charges Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
✅ Excellent Documentation – Comprehensive guides, API reference, code samples at platform.openai.com ✅ Official SDKs – Well-maintained Python, Node.js libraries with examples ✅ Massive Community – Extensive tutorials, LangChain/LlamaIndex integrations, ecosystem resources ⚠️ Limited Direct Support – Community forums for standard users; Enterprise gets premium support OpenAI Cookbook – Practical examples and recipes for common use cases including RAG	Documentation – docs.rag.progress.cloud SDK Ecosystem – Python 21K weekly + JavaScript Progress Support – 2,000+ employee parent company	Documentation hub – Docs, tutorials, API references Support channels – Email, in-app chat, dedicated managers (Premium+) Open-source – Python SDK, Postman, GitHub examples Community – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ NO Built-In RAG – Entire retrieval infrastructure must be built by developers ⚠️ Developer-Only – Requires coding expertise; no no-code interface for non-technical teams ⚠️ Rate Limits – Usage tiers start restrictive (Tier 1: 500 RPM GPT-4) ⚠️ Model Lock-In – Cannot use Claude, Gemini; tied to OpenAI ecosystem ⚠️ NO Chat UI – ChatGPT web interface not embeddable or customizable for business ⚠️ Cost at Scale – Token pricing can spike without optimization; needs cost management	⚠️ NO HIPAA – Healthcare PHI requires sales contact ⚠️ NO Messaging – No Slack, WhatsApp, Teams ⚠️ Acquisition Risk – June 2025 purchase, transitioning	Managed service – Less control over RAG pipeline vs build-your-own Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source Real-time data – Requires re-indexing; not ideal for live inventory/prices Enterprise features – Custom SSO only on Enterprise plan
Core Agent Features
✅ Assistants API (v2) – Built-in conversation history, persistent threads, tool access management ✅ Function Calling – Models invoke external functions/tools; describe structure, receive calls with arguments ✅ Parallel Tool Execution – Access Code Interpreter, File Search, custom functions simultaneously Responses API (2024) – New primitive with web search, file search, computer use ✅ Structured Outputs – strict: true guarantees arguments match JSON Schema for reliable parsing ⚠️ Agent Limitations – Less control vs LangChain for complex workflows; simpler assistant paradigm	Retrieval Agents – Autonomously select optimal retrieval strategies per query Pre-Built Agents (Beta) – Labeler, summarization, graph extraction, Q&A, safety Web Components – <nuclia-search-bar> and <nuclia-chat> for embedding ⚠️ No Proactive Alerts – Monitoring exists but alerting undocumented	Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks Multi-Agent Systems – Specialized agents for support, sales, knowledge Memory & Context – Persistent conversation history across sessions Tool Integration – Webhooks + 5,000 Zapier apps for automation Continuous Learning – Auto re-indexing without manual retraining
R A G-as-a- Service Assessment
⚠️ NOT RAG-AS-A-SERVICE – Provides LLM models/APIs, not managed RAG infrastructure DIY RAG Architecture – Embed docs → external vector DB → retrieve → inject into prompt File Search (Beta) – Azure preview includes minimal semantic search; not production RAG ⚠️ No Managed Infrastructure – Unlike CustomGPT/Vectara, leaves chunking, indexing, retrieval to developers Framework vs Service – Compare to LLM APIs (Claude, Gemini), not managed RAG platforms External Costs – RAG needs vector DBs (Pinecone $70+/month), hosting, embeddings API	✅ TRUE RAG-AS-A-SERVICE – Core mission is RAG backend LLM Flexibility – 7 providers switchable without changes Open-Source – NucliaDB 710+ stars, AGPLv3	Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure API-first – REST API, Python SDK, OpenAI compatibility, MCP Server No-code option – 2-minute wizard deployment for non-developers Hybrid positioning – Serves both dev teams (APIs) and business users (no-code) Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
R E Mi Evaluation Model ( Core Differentiator)
N/A	✅ Proprietary Investment – Addresses hallucination, absent from competitors REMi v2 – Llama 3.2-3B based, 30x faster inference Continuous Quality – Evaluates every interaction across 4 dimensions (0-5) ✅ Benchmark Leader – Highest scores vs Vectara on Docmatix 1.4k	N/A
Open- Source Nuclia D B Foundation
N/A	GitHub Presence – 710+ stars, AGPLv3 transparency into retrieval Technology Stack – Python and Rust for performance Four Index Types – Document, Full Text, Chunk/Vector, Knowledge Graph 100M Vector Performance – Full ingestion in ~20 minutes	N/A
Multi- Lingual Support
N/A	Nuclia Multilingual Embedding – Default model supports multiple languages 60+ Format Processing – Multi-language across PDF, Word, Excel, PPT Auto Transcription – Multi-language speech-to-text for audio/video	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: February 3, 2026 | 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