Pyx vs Supavec

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 Pyx and Supavec 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 Pyx and Supavec, 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 Pyx if: you value very quick setup (30-60 minutes)
  • Choose Supavec if: you value 100% open source with no vendor lock-in

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

About Supavec

Supavec Landing Page Screenshot

Supavec is the open source rag as a service platform. SupaVec is an open-source RAG platform that serves as an alternative to Carbon.ai. Built on transparency and data sovereignty, it allows developers to build powerful RAG applications with complete control over their infrastructure, supporting any data source at any scale. Founded in 2024, headquartered in Remote, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
84/100
Starting Price
Custom

Key Differences at a Glance

In terms of user ratings, both platforms score similarly in overall satisfaction. From a cost perspective, Supavec offers more competitive entry pricing. The platforms also differ in their primary focus: AI Search versus RAG Platform. 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

logo of pyx
Pyx
logo of supavec
Supavec
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CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
  • ✅ 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
  • REST API Upload – PDFs, Markdown, TXT via API endpoints or raw text
  • No Pre-Built Connectors – ⚠️ Script your own Google Drive/Notion fetchers
  • Open Source Extensibility – ✅ Build connectors to Postgres, MongoDB, S3
  • Supabase Scalability – Handles millions of docs with horizontal scaling
  • 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
  • ⚠️ 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
  • Pure REST API – No built-in widget or messaging platform bots
  • DIY Front-End – ⚠️ Code your own chat UI or Slack bridge
  • HTTP Compatibility – ✅ Any HTTP-capable app can integrate
  • No Zapier – ⚠️ Webhooks and automations are manual
  • 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
  • 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
  • Basic RAG – Retrieve chunks + LLM answer, stateless calls
  • No Chat History – ⚠️ No built-in conversation tracking
  • No Lead Capture – ⚠️ No human handoff or escalation features
  • Fast Retrieval – ✅ Pulls relevant text quickly for LLM response
  • ✅ #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
  • ⚠️ 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
  • No Pre-Made UI – Branding lives in your custom front-end
  • White-Label by Default – ✅ API-only means no Supavec branding
  • Full Control – Add domain checks and auth in your code
  • 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
  • ⚠️ 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
  • Model-Agnostic – Defaults to GPT-3.5, switch to GPT-4 or self-hosted
  • Simple Config – Change model via config or prompt path
  • No Prompt Magic – ⚠️ Plain RAG without anti-hallucination layer
  • Quality Dependency – ⚠️ Rests on your LLM choice and prompting
  • 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)
  • ⚠️ 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
  • Straightforward REST – File uploads, text uploads, search endpoints
  • No Official SDKs – ⚠️ Use fetch/axios or build wrapper
  • Concise Docs – JS snippets with Postman collection included
  • Open Source on GitHub – ✅ Community contributions welcome
  • 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
  • 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
  • Standard GPT RAG – Accuracy equals GPT quality plus RAG lift
  • Fast Vector Search – Postgres pgvector keeps retrieval snappy
  • No Benchmarks – ⚠️ Expect typical GPT-3.5/4 RAG performance
  • Manual Citations – ⚠️ Prompt-engineer your own validation
  • 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)
  • ✅ 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
  • Instant Re-Embedding – ✅ Upload/overwrite docs with near-instant reindex
  • Prompt-Based Behavior – ⚠️ No GUI for personas or rules
  • Multi-Lingual Support – Tell LLM in your prompt for language
  • Metadata & Chunking – Add custom metadata and build logic around it
  • 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
  • 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
  • MIT Open Source – ✅ Self-host free with your infrastructure costs
  • Hosted Plans – Free (100 calls/mo), $190/yr (750 calls), $1,490/yr (5K calls)
  • No Storage Metering – Only query volume counts toward limits
  • Negotiate or Self-Host – Scale beyond caps with custom limits
  • 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
  • ✅ 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
  • Self-Hosting Privacy – ✅ Everything on your servers for compliance
  • Supabase RLS – Row-level security fences team data when hosted
  • No Model Training – ✅ Your docs never used for LLM training
  • GDPR/HIPAA Ready – Self-host for compliance on your infrastructure
  • 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 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
  • No Built-In Dashboard – ⚠️ Log requests yourself or use Supabase metrics
  • Basic Call Counts – Hosted plan shows simple usage stats
  • External Logging – Wire up your own monitoring tools
  • 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
  • ✅ 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
  • Community Help – GitHub/Discord for free tier and self-hosted
  • Email Support – Paid plans get email support with priority levels
  • Lean Docs – ⚠️ Endpoint references, not extensive tutorials
  • Open-Source PRs – ✅ Forks and contributions welcome
  • 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
  • ✅ 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
N/A
  • 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
  • ✅ 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
N/A
  • 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 – 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 – MIT open-source RAG API on Supabase
  • Target Customers – Developers building custom RAG, budget-conscious startups
  • Key Competitors – Carbon.ai, LangChain, SimplyRetrieve, hosted RAG APIs
  • Advantages – ✅ MIT license, Supabase foundation, model-agnostic, privacy-focused
  • Best For – Lightweight RAG backend, self-hosting, avoiding platform costs
  • 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
  • ⚠️ 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
  • GPT-3.5 Default – Cost-effective with GPT-4/4-turbo support
  • Self-Hosted Models – ✅ Llama, Mistral via API endpoints
  • No Model Lock-In – ✅ Switch by changing config
  • Direct API Keys – ✅ Connect your own OpenAI without markup
  • 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
  • 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
  • Standard RAG – Document chunking with Postgres pgvector search
  • OpenAI Embeddings – Automatic embedding generation on upload
  • Fast Re-Indexing – ✅ Almost instant document updates
  • No Advanced Features – ⚠️ No hybrid search, reranking, or multi-query
  • No Hallucination Detection – ⚠️ Implement citations manually
  • 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
  • ✅ 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
  • Custom Chatbot Backends – Developers building own chat interfaces
  • Self-Hosted Retrieval – ✅ Data sovereignty with Supabase infrastructure
  • Budget RAG Apps – Startups minimizing costs with MIT license
  • Supabase Projects – Teams already using Supabase stack
  • Not Ideal For – ⚠️ Non-technical teams or advanced RAG needs
  • 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
  • ✅ 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
  • Self-Hosting Advantage – ✅ Complete data sovereignty on your servers
  • Supabase RLS – Row-level security for multi-tenant isolation
  • GDPR/HIPAA Ready – Self-host for compliance requirements
  • No SOC 2 – ⚠️ Open-source lacks formal certifications
  • DIY Access Controls – ⚠️ Implement auth and RBAC yourself
  • 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
  • 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
  • Free Self-Hosting – ✅ MIT license with only infrastructure costs
  • Hosted Free – 100 API calls/month for testing
  • Basic $190/year – 750 calls/mo with email support
  • Enterprise $1,490/year – 5K calls/mo with priority support
  • 40-90% Cheaper – ✅ vs commercial RAG platforms
  • 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
  • ✅ 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
  • Lean API Docs – ⚠️ Technical reference, not tutorial-heavy
  • Community Support – GitHub Discussions and Discord
  • Email for Paid – Basic/Enterprise get email support
  • Postman Collection – ✅ Quick testing and integration
  • 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 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
  • No GUI/Dashboard – ⚠️ Everything via API, no business-user interface
  • Developer-Only – ⚠️ Requires coding for setup and integration
  • Basic RAG Only – ⚠️ No hybrid search, reranking, or query expansion
  • No Observability – ⚠️ Must build your own logging layer
  • Manual Connectors – ⚠️ Script your own Google Drive/Notion fetches
  • Stateless API – ⚠️ No chat history or session management
  • 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
  • ⚠️ 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
  • Stateless RAG – Pure retrieval + generation without conversation state
  • Postgres Vector Search – ✅ Fast pgvector with cosine similarity
  • Metadata Filtering – Custom tagging for organized knowledge
  • Supabase Integration – Built on PostgreSQL with RLS security
  • No Chat UI – ⚠️ API-only, build your own interface
  • No Advanced RAG – ⚠️ Missing hybrid search, knowledge graphs, reranking
  • 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 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
  • TRUE RAG-AS-A-SERVICE API – Lightweight MIT open-source backend
  • Carbon.ai Alternative – Created as transparent open-source response
  • Target Market – Developers on budget, self-hosting for data sovereignty
  • Standard RAG – Document chunking, embeddings, pgvector search
  • API-First Design – ✅ Pure REST without GUI or widgets
  • Affordable Pricing – ✅ 40-90% cheaper than commercial platforms
  • Developer Platform – ⚠️ Not for non-technical teams
  • 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: Pyx vs Supavec

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

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)

When to Choose Supavec

  • You value 100% open source with no vendor lock-in
  • Complete control over data and infrastructure
  • Strong privacy with Supabase RLS integration

Best For: 100% open source with no vendor lock-in

Migration & Switching Considerations

Switching between Pyx and Supavec 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

Pyx starts at $30/month, while Supavec begins at custom pricing. 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 Pyx and Supavec 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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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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