What are the main differences between Progress Agentic RAG and Pyx AI?

Progress Agentic RAG excels in Proprietary REMi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors and Open-source NucliaDB transparency (710+ GitHub stars) with managed infrastructure removes operational burden while maintaining technical visibility, scoring 82/100 in user ratings with N/A% accuracy. Pyx AI leads in Very quick setup (30-60 minutes) and No manual data imports required with 83/100 overall rating and 85% accuracy. Key technical difference: Progress Agentic RAG focuses on REMi evaluation model v2 (Llama 3.2-3B) for continuous quality scoring: answer relevance, context relevance, groundedness, correctness (0-5 scale) while Pyx AI emphasizes Conversational search interface. Pricing differs by $670/month.

Which is better for enterprises: Progress Agentic RAG or Pyx AI?

Progress Agentic RAG offers SOC 2 compliance for enterprise security. HIPAA compliance varies - contact vendors for details. For scalability, Pyx AI supports on-premise deployment for maximum control. Pyx AI leads with 83/100 enterprise readiness score (72/100 enterprise features).

How much does Progress Agentic RAG cost vs Pyx AI?

Progress Agentic RAG starts at $700/monthly (paid model), while Pyx AI begins at $30/monthly (freemium model). Pyx AI provides a free tier, and free trials are available. This represents a significant price difference of $670/month. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Progress Agentic RAG to Pyx AI?

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

What integrations do Progress Agentic RAG and Pyx AI support?

Progress Agentic RAG integrates with Python SDK: pip install nuclia (Python 3.8+, ~21K weekly downloads), JavaScript/TypeScript SDK: @nuclia/core (React, Next.js, Angular, Vue.js, Svelte), CMS plugins: WordPress, Strapi, Workflow: Pipedream official app, Zapier API-compatible, Chrome extension for web page indexing (7 integrations total). Pyx AI supports SharePoint, Google Drive, Dropbox, OneDrive, Local file systems (7 integrations available). Progress Agentic RAG provides comprehensive API access. Integration coverage is comparable.

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

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

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

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

How much does Progress Agentic RAG cost vs Pyx?

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

Can I migrate from Progress Agentic RAG to Pyx?

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

What integrations do Progress Agentic RAG and Pyx support?

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

Progress Agentic RAG vs Pyx

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

More about Pyx More about Progress Agentic RAG Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

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

Overview

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

Quick Decision Guide

Choose Progress Agentic RAG if: you value proprietary remi v2 model (30x faster inference) addresses hallucination problem with continuous quality monitoring - differentiated capability absent from most competitors
Choose Pyx if: you value very quick setup (30-60 minutes)

About Progress Agentic RAG

Progress Agentic RAG Landing Page Screenshot

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

Overall Rating

82/100

Starting Price

$700/mo

About Pyx

Pyx is find. don't search.. Pyx AI is an enterprise conversational search tool that leverages Retrieval-Augmented Generation (RAG) to deliver real-time answers from company data. It continuously synchronizes with data sources and enables natural language queries across unstructured documents without keywords or pre-sorting. Founded in 2022, headquartered in Europe, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

83/100

Starting Price

$30/mo

Key Differences at a Glance

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

⚠️ What This Comparison Covers

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

Detailed Feature Comparison

Progress Agentic RAG	Pyx	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
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	✅ Auto-Indexing – Points at files, indexes unstructured data automatically without manual setup ✅ Auto-Sync – Connected repositories sync automatically, document changes reflected almost instantly File Formats – Supports PDF, DOCX, PPT, TXT and common enterprise formats ⚠️ Limited Scope – No website crawling or YouTube ingestion, narrower than CustomGPT Enterprise Scale – Handles large corporate data sets, exact limits not published	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
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	⚠️ Standalone Only – Own chat/search interface, not a "deploy everywhere" platform ⚠️ No External Channels – No Slack bot, Zapier connector, or public API Web/Desktop UI – Users interact through Pyx's interface, minimal third-party chat synergy Custom Integration – Deeper integrations require custom dev work or future updates	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
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	Conversational Search – Context-aware Q&A over enterprise documents with follow-up questions ⚠️ Internal Focus – Designed for knowledge management, no lead capture or human handoff Multi-Language – Likely supports multiple languages, though not a headline feature ⚠️ Basic Analytics – Stores chat history, fewer business insights than customer-facing tools	✅ #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
Core Agent Features
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	⚠️ NO Agent Capabilities – No autonomous agents, tool calling, or multi-agent orchestration Conversational Search Only – Context-aware dialogue for Q&A, not agentic or autonomous behavior Basic RAG Architecture – Standard retrieval without function calling, tool use, or workflows ⚠️ No External Actions – Cannot invoke APIs, execute code, query databases, or interact externally Internal Knowledge Focus – Employee Q&A about documents, not task automation or workflows	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
Additional Considerations
✅ 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	✅ No-Fuss Internal Search – Employees use without coding, simple deployment for teams ⚠️ Not Public-Facing – Not ideal for customer chatbots or developer-heavy customization Siloed Environment – Single AI search environment, not broad extensible platform Simpler Scope – Less flexible than CustomGPT, but faster setup for internal use	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
Customization & Branding
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	⚠️ Minimal Branding – Logo/color tweaks only, designed as internal tool not white-label ⚠️ No Embedding – Standalone interface, no domain-embed or widget options available Pyx UI Only – Look stays "Pyx AI" by design, public branding not supported Security Focus – Emphasis on user management and access controls over theming	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
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	⚠️ Undisclosed Model – Likely GPT-3.5/GPT-4 but exact model not publicly documented ⚠️ No Model Selection – Cannot switch LLMs or configure speed vs accuracy tradeoffs ⚠️ Single Configuration – Every query uses same model, no toggles or fine-tuning Closed Architecture – Model details, context window, capabilities hidden from users intentionally	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)
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	⚠️ No API – No open API or SDKs, everything through Pyx interface ⚠️ No Embedding – Cannot integrate into other apps or call programmatically Closed Ecosystem – No GitHub examples, community plug-ins, or extensibility options Turnkey Only – Great for ready-made tool, limits deep customization or extensions	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
✅ 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	Real-Time Answers – Serves accurate responses from internal documents, sparse public benchmarks Auto-Sync Freshness – Connected repositories keep retrieval context always current automatically ⚠️ Limited Transparency – No anti-hallucination metrics or advanced re-ranking details published Competitive RAG – Likely comparable to standard GPT-based systems on relevance control	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)
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	✅ Auto-Sync Updates – Knowledge base updated without manual uploads or scheduling ⚠️ No Persona Controls – AI voice stays neutral, no tone or behavior customization ✅ Access Controls – Strong role-based permissions, admins set document visibility per user Closed Environment – Great for content updates, limited for AI behavior or deployment	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
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	Seat-Based Pricing – ~$30 per user per month, predictable monthly costs ✅ Cost-Effective Small Teams – Affordable for teams under 50 users ⚠️ Large Team Costs – 100 users = $3,000/month, can scale expensively Unlimited Content – Document/token limits not published, gated only by user seats Free Trial + Enterprise – Hands-on trial available, custom pricing for large deployments	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
✅ 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	✅ GDPR Compliance – Germany-based, implicit EU data protection and regional sovereignty ✅ Enterprise Privacy – Data isolated per customer, encrypted in transit and rest ✅ No Model Training – Customer data not used for external LLM training ✅ Role-Based Access – Built-in controls, admins set document visibility per role ⚠️ Limited Certifications – On-prem or SOC 2/ISO 27001/HIPAA not publicly documented	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
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	Basic Stats – User activity, query counts, top-referenced documents for admins ⚠️ No Deep Analytics – No conversation analytics dashboards or real-time logging Adoption Tracking – Useful for usage monitoring, lighter insights than full suites Set-and-Forget – Minimal monitoring overhead, contact support for issues	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
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	✅ Direct Support – Email, phone, chat with hands-on onboarding approach ⚠️ No Open Community – Closed solution, no plug-ins or user-built extensions Internal Roadmap – Product updates from Pyx only, no community marketplace Quick Setup Focus – Emphasizes minimal admin overhead for internal knowledge search	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
No- Code Interface & Usability
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	✅ Straightforward UI – Users log in, ask questions, get answers without coding ✅ No-Code Admin – Admins connect data sources, Pyx indexes automatically Minimal Customization – UI stays consistent and uncluttered by design Internal Q&A Hub – Perfect for employee use, not external embedding or branding	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
R E Mi Evaluation Model ( Core Differentiator)
✅ 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	N/A
Open- Source Nuclia D B Foundation
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	N/A
Multi- Lingual Support
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	N/A
R A G-as-a- Service Assessment
✅ TRUE RAG-AS-A-SERVICE – Core mission is RAG backend LLM Flexibility – 7 providers switchable without changes Open-Source – NucliaDB 710+ stars, AGPLv3	⚠️ NOT TRUE RAG-AS-A-SERVICE – Standalone internal app, not API-accessible RAG platform Turnkey Application – Self-contained Q&A tool vs developer-accessible RAG infrastructure ⚠️ No API Access – No REST API, SDKs, programmatic access unlike CustomGPT/Vectara Closed Application – Web/desktop interface only, cannot build custom applications on top SaaS vs RaaS – Software-as-a-Service (standalone app) NOT Retrieval-as-a-Service (API infrastructure) Best Comparison Category – Internal search tools (Glean, Guru), not developer RAG platforms	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
Competitive Positioning
✅ Pricing Advantage – $700/mo entry undercuts competitors ✅ REMi Differentiator – Continuous quality monitoring ⚠️ Integration Gaps – No native Slack/WhatsApp/Teams	Market Position – Turnkey internal knowledge search (Germany), not embeddable chatbot platform Target Customers – Small-mid European teams needing GDPR compliance and simple deployment Key Competitors – Glean, Guru, Notion AI; not customer-facing chatbots like CustomGPT ✅ Advantages – Simple scope, auto-sync, GDPR compliance, ~$30/user/month predictable pricing ⚠️ Use Case Fit – Perfect for <50 user teams, not API integrations or public chatbots	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
7 Providers – Anthropic, OpenAI, Google, Meta, Mistral, Cohere Nuclia Private GenAI – 100% data isolation for security	⚠️ Undisclosed LLM – Likely GPT-3.5/GPT-4 but model details not publicly documented ⚠️ No Model Selection – Cannot switch LLMs or choose speed vs accuracy configurations ⚠️ Opaque Architecture – Context window size and capabilities not exposed to users Simplicity Focus – Hides technical complexity, users ask questions and get answers ⚠️ No Fine-Tuning – Cannot customize model on domain data for specialized responses	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
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	Conversational RAG – Context-aware search over enterprise documents with follow-up support ✅ Auto-Sync – Repositories sync automatically, changes reflected almost instantly Document Formats – PDF, DOCX, PPT, TXT and common enterprise formats supported ⚠️ No Advanced Controls – Chunking, embedding models, similarity thresholds not exposed ⚠️ Limited Transparency – No citation metrics or anti-hallucination details published Closed System – Optimized for internal Q&A, limited visibility into retrieval architecture	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
Enterprise Knowledge – Deploy knowledge bases in minutes Healthcare & Pharma – Medical protocol search Developer RAG Backend – API-first for custom AI apps	✅ Internal Knowledge Search – Employees asking questions about company documents and policies ✅ Team Onboarding – New hires finding information without bothering colleagues ✅ Policy Lookup – HR, compliance, operational procedure retrieval for staff ✅ Small European Teams – GDPR-compliant internal search with EU data residency ⚠️ NOT SUITABLE FOR – Public chatbots, customer support, API integrations, multi-channel deployment	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
✅ SOC2 Type 2, ISO 27001, GDPR Encryption – AES-256 at rest, TLS in transit ⚠️ NO HIPAA – Healthcare requires sales contact	✅ GDPR Compliance – Germany-based with implicit EU data protection compliance ✅ German Data Residency – EU storage location for regional data sovereignty requirements ✅ Enterprise Privacy – Customer data isolated, encrypted in transit and at rest ✅ Role-Based Access – Built-in controls, admins set document visibility per user ⚠️ Limited Certifications – SOC 2, ISO 27001, HIPAA not publicly documented	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
Fly – $700/mo: 10GB/15K resources Growth – $1,750/mo: 50GB/80K resources Enterprise – Custom: Unlimited, hybrid/on-prem	Seat-Based Pricing – ~$30 per user per month ✅ Small Team Value – Affordable for teams under 50 users, predictable costs ⚠️ Scalability Cost – 100 users = $3,000/month, expensive for large organizations Unlimited Content – No published document limits, gated only by user seats Free Trial + Enterprise – Evaluation available, custom pricing for volume discounts	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
Documentation – docs.rag.progress.cloud SDK Ecosystem – Python 21K weekly + JavaScript Progress Support – 2,000+ employee parent company	✅ Direct Support – Email, phone, chat with hands-on onboarding approach ✅ Quick Deployment – Minimal admin overhead, connect sources and start asking questions ⚠️ No Open Community – Closed solution, no plug-ins or user extensions ⚠️ No Developer Docs – No API documentation or programmatic access guides Internal Roadmap – Updates from Pyx only, no user-contributed features	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 HIPAA – Healthcare PHI requires sales contact ⚠️ NO Messaging – No Slack, WhatsApp, Teams ⚠️ Acquisition Risk – June 2025 purchase, transitioning	⚠️ No Public API – Cannot embed or call programmatically, standalone UI only ⚠️ No Messaging Integrations – No Slack, Teams, WhatsApp or chat platform connectors ⚠️ Limited Branding – Minimal customization, not white-label solution for public deployment ⚠️ No Advanced Controls – Cannot configure RAG parameters, model selection, retrieval strategies ⚠️ Seat-Based Scaling – Expensive for large orgs vs usage-based pricing models ✅ Best For – Small European teams (<50 users) prioritizing simplicity and GDPR over flexibility	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

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Final Thoughts

Final Verdict: Progress Agentic RAG vs Pyx

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

When to Choose Progress Agentic RAG

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

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

When to Choose Pyx

You value very quick setup (30-60 minutes)
No manual data imports required
Excellent ease of use with conversational interface

Best For: Very quick setup (30-60 minutes)

Migration & Switching Considerations

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

Pricing Comparison Summary

Progress Agentic RAG starts at $700/month, while Pyx begins at $30/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 Progress Agentic RAG and Pyx 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 25, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.

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Priyansh Khodiyar

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

Progress Agentic RAG vs Pyx