Deepset vs Pyx

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

Priyansh Khodiyar's avatar
Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Deepset and 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 Deepset 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 Deepset if: you value mature open-source framework (since 2020)
  • Choose Pyx if: you value very quick setup (30-60 minutes)

About Deepset

Deepset Landing Page Screenshot

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

Overall Rating
83/100
Starting Price
Custom

About Pyx

Pyx Landing Page Screenshot

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, Deepset starts at a lower price point. The platforms also differ in their primary focus: AI Development Platform 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

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Deepset
logo of pyx
Pyx
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • Flexible ingestion – Process any file type with connectors and Unstructured library
  • Vector store options – OpenSearch, Pinecone, Weaviate, Snowflake support Learn more
  • ⚠️ Hands-on setup required for domain-specific pipeline customization
  • ✅ 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
  • API-first design – REST endpoints and Haystack SDK for custom app integration
  • Shareable prototypes – Quick demos available See feature
  • ⚠️ Production channels (Slack, web chat) require custom code development
  • ⚠️ 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
  • Modular RAG pipelines – Retriever + reader + optional rerankers/multi-step logic
  • Advanced features – Multi-turn chat, source attribution, fine-grained retrieval Overview
  • ✅ Tool use and external API integration for rich agent behavior
  • 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
Customization & Branding
  • ⚠️ No drag-and-drop theming – requires custom front-end development for branded UI
  • ✅ Full freedom for visuals and conversational tone Custom components
  • ⚠️ 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
  • Model-agnostic – GPT-4, Llama 2, Claude, Cohere, 80+ providers supported
  • ✅ Switch models via Connections UI with few clicks View models
  • ⚠️ 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)
  • REST API + Haystack SDK – Build, run, and query pipelines with comprehensive tooling
  • ✅ Visual editor with drag-and-drop, export YAML for version control Studio overview
  • ⚠️ 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
  • ✅ Multi-step retrieval, hybrid search, custom rerankers for max accuracy
  • ✅ Modular components optimize latency at scale Benchmark insights
  • 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)
  • ✅ Full control: multi-hop retrieval, custom logic, bespoke prompts available
  • ✅ Multiple datastores, role-based filters, external API integration Templates
  • ✅ 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
  • Free Studio – Development environment, then usage-based Enterprise plans at scale
  • ✅ Cloud, hybrid, or on-prem deployment options Pricing overview
  • 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
  • ✅ SOC 2 Type II, ISO 27001, GDPR, HIPAA enterprise compliance
  • ✅ Cloud, VPC, or on-prem data residency options Security compliance
  • ✅ 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
  • Studio dashboard – Latency, error rates, resource usage tracking available
  • ✅ Logs integrate with Prometheus, Splunk, and more Monitoring features
  • 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
  • Community support – Haystack open-source community (Discord, GitHub, 14K+ stars) Insights
  • ✅ Wide ecosystem: vector DBs, model providers, ML tool integrations
  • Enterprise support – Paid tiers with dedicated assistance available
  • ✅ 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
Additional Considerations
  • ✅ Ideal for heavily customized, domain-specific RAG solutions with full control
  • ⚠️ Steeper learning curve and more dev effort required Details
  • ✅ 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
No- Code Interface & Usability
  • Low-code Studio – Drag-and-drop interface aimed at developers and ML engineers
  • ⚠️ Non-tech users need help; production UIs require custom development
  • ✅ 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
Competitive Positioning
  • Market position – Developer-first RAG framework with enterprise cloud offering for custom solutions
  • Target customers – ML engineers, dev teams needing deep RAG customization and portability
  • Key competitors – LangChain/LangSmith, Contextual.ai, Dataworkz, Vectara.ai, Pinecone/Weaviate implementations
  • Advantages – Open-source Haystack, model-agnostic, visual editor, modular components, wide ecosystem, compliance
  • Pricing advantage – Free Studio, usage-based Enterprise; no vendor lock-in via open-source
  • Use case fit – Customized domain-specific RAG, complex workflows, developer-friendly APIs with portability
  • 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
  • Model-agnostic – GPT-4, Claude, Llama 2, Cohere, 80+ providers via unified interface
  • ✅ Switch models via Connections UI without code changes
  • Embeddings – OpenAI, Cohere, Sentence Transformers, custom models supported
  • ✅ Multiple LLMs per pipeline for different components (retrieval vs generation)
  • Fine-tuning – Train on proprietary data for domain-specific accuracy
  • ⚠️ 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
  • ✅ Multi-step retrieval, hybrid search (semantic + keyword), custom rerankers for max accuracy
  • Modular design – Flexible retriever + reader + reranker for customized workflows
  • Multi-hop retrieval – Chain steps for complex queries requiring deep context
  • Vector DB flexibility – OpenSearch, Pinecone, Weaviate, Snowflake, Qdrant backends
  • ✅ Source attribution with citations, confidence scores; MTEB benchmark-proven performance
  • Haystack framework – Open-source foundation for full customization and portability
  • 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
  • Domain-specific Q&A – Enterprise knowledge bases with specialized terminology and fine-tuned models
  • Research & analysis – Multi-hop retrieval for complex questions across large corpora
  • Technical documentation – Developer-focused RAG for code docs, API references, guides
  • Compliance & legal – HIPAA/GDPR systems for regulated industries with on-prem deployment
  • Custom AI agents – External API calls, tool use, multi-step reasoning capabilities
  • ✅ Enterprise search and future-proof AI with no vendor lock-in
  • ✅ 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
  • ✅ SOC 2 Type II, ISO 27001, GDPR, HIPAA certifications with annual audits
  • Flexible deployment – Cloud, hybrid, VPC, or on-premises for complete data control
  • Data residency – Choose storage location (US, EU, on-prem) for compliance
  • ✅ No model training on customer data; comprehensive audit trails
  • ✅ 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
  • Studio (Free) – Development environment with unlimited files for prototyping
  • Enterprise – Usage-based pricing (queries, documents, compute); no per-seat charges
  • Deployment tiers – Cloud (managed SaaS), hybrid, or on-prem with separate pricing
  • ✅ Professional services and custom development available; handles millions of documents
  • ✅ Haystack framework free forever; only pay for managed cloud services
  • 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
  • Community – Active Discord, GitHub (14K+ stars) with responsive maintainers
  • Enterprise support – Email, Slack Connect, dedicated engineers for paid customers
  • ✅ Comprehensive docs at docs.cloud.deepset.ai with tutorials, API references, guides
  • Resources – YouTube tutorials, GitHub examples, starter templates for common use cases
  • ✅ Wide ecosystem: vector DB providers, model vendors, tool developers
  • Professional services – Custom development, architecture consulting, implementation support
  • ✅ 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
  • ⚠️ Steeper learning curve – Requires ML/engineering skills, not ideal for non-technical users
  • ⚠️ Custom UI required – No drag-and-drop widget; build production interfaces from scratch
  • ⚠️ Hands-on setup – More config effort vs plug-and-play SaaS platforms
  • ⚠️ Studio limitations – Visual editor still needs RAG understanding; DevOps work for production
  • ⚠️ Enterprise costs – Usage-based pricing expensive at high volumes without optimization
  • ⚠️ Best for technical teams – Not for business users seeking no-code solutions
  • ⚠️ 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
Core Agent Features
  • AI Agents – LLM-powered agents with reasoning, reflection, tool use Guide
  • Spectrum approach – Balance structured workflows with autonomous capabilities Details
  • ✅ Planning mechanisms: chain-of-thought/tree-of-thought for multi-step reasoning
  • Dynamic routing – LLMs evaluate and choose tools, databases, actions based on context
  • ✅ Reflection & self-correction for improved accuracy and adaptive strategies
  • Agentic RAG – Build pipelines with graphs, multimodal capabilities RAG Guide
  • ⚠️ 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
R A G-as-a- Service Assessment
  • Platform Type – HYBRID: Open-source Haystack + enterprise Deepset Cloud for custom RAG solutions
  • Architecture – Modular pipelines (retriever + reader + reranker), full control over embeddings/vector DBs
  • Agentic capabilities – Autonomous agents with planning, routing, reflection Guide
  • Developer experience – REST API, Haystack SDK, visual Studio editor Studio
  • ⚠️ No-code limited – Studio drag-and-drop for developers, not non-tech users
  • Target market – ML engineers, dev teams needing deep customization and portability
  • ✅ RAG leadership: multi-step retrieval, hybrid search, model-agnostic (80+ providers), MTEB benchmarks Data
  • ✅ Enterprise ready: SOC 2, ISO 27001, GDPR, HIPAA; cloud/VPC/on-prem deployment
  • Use case fit – Custom domain RAG, complex workflows, developer APIs with portability
  • ✅ Open-source advantage: Haystack (14K+ stars) free; no vendor lock-in
  • ⚠️ NOT for: Non-tech teams, turnkey chatbots, pre-built widgets/Slack integrations
  • Competition – LangChain, Contextual.ai, Dataworkz; differentiated by open-source foundation
  • ⚠️ 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

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Final Thoughts

Final Verdict: Deepset vs Pyx

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

When to Choose Deepset

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

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

When to Choose 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 Deepset 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

Deepset starts at custom pricing, 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

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

For most organizations, the decision between Deepset and 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: December 29, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar's avatar

Priyansh Khodiyar

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

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