SciPhi vs Supavec

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

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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 SciPhi 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 SciPhi 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 SciPhi if: you value state-of-the-art retrieval accuracy
  • Choose Supavec if: you value 100% open source with no vendor lock-in

About SciPhi

SciPhi Landing Page Screenshot

SciPhi is the most advanced ai retrieval system. R2R is a production-ready AI retrieval system supporting Retrieval-Augmented Generation with advanced features including multimodal ingestion, hybrid search, knowledge graphs, and a Deep Research API for multi-step reasoning across documents and the web. Founded in 2023, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.

Overall Rating
89/100
Starting Price
Custom

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, pricing is comparable. The platforms also differ in their primary focus: RAG Platform 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

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SciPhi
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Supavec
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Data Ingestion & Knowledge Sources
  • Handles 40 + formats—from PDFs and spreadsheets to audio—at massive scale Reference.
  • Async ingest auto-scales, crunching millions of tokens per second—perfect for giant corpora Benchmark details.
  • Ingest via code or API, so you can tap proprietary databases or custom pipelines with ease.
  • 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
  • Ships a REST RAG API—plug it into websites, mobile apps, internal tools, or even legacy systems.
  • No off-the-shelf chat widget; you wire up your own front end API snippet.
  • 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
  • Core RAG engine serves retrieval-grounded answers; hook it to your UI for multi-turn chat.
  • Multi-lingual if the LLM you pick supports it.
  • Lead-capture or human handoff flows are yours to build through the API.
  • 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
  • Fully bespoke—design any UI you want and skin it to match your brand.
  • SciPhi focuses on the back end, so front-end look-and-feel is entirely up to you.
  • 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
  • LLM-agnostic—GPT-4, Claude, Llama 2, you choose.
  • Pick, fine-tune, or swap models anytime to balance cost and performance Model options.
  • 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)
  • REST API plus a Python client (R2RClient) handle ingest and query tasks
  • Docs and GitHub repos offer deep dives and open-source starter code SciPhi GitHub.
  • 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
  • Hybrid search (dense + keyword) keeps retrieval fast and sharp.
  • Knowledge-graph boosts (HybridRAG) drive up to 150 % better accuracy
  • Sub-second latency—even at enterprise scale.
  • 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)
  • Add new sources, tweak retrieval, mix collections—everything’s programmable.
  • Chain API calls, re-rank docs, or build full agentic flows
  • 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
  • Free tier plus a $25/mo Dev tier for experiments.
  • Enterprise plans with custom pricing and self-hosting for heavy traffic Pricing.
  • 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
  • Customer data stays isolated in SciPhi Cloud; self-host for full control.
  • Standard encryption in transit and at rest; tune self-hosted setups to meet any regulation.
  • 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
  • Dev dashboard shows real-time logs, latency, and retrieval quality Dashboard.
  • Hook into Prometheus, Grafana, or other tools for deep monitoring.
  • 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
  • Community help via Discord and GitHub; Enterprise customers get dedicated support
  • Open-source core encourages community contributions and integrations.
  • 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
Core Agent Features
  • Agentic RAG – Reasoning agent for autonomous research across documents/web with multi-step problem solving
  • Advanced Toolset – Semantic search, metadata search, document retrieval, web search, web scraping capabilities
  • Multi-Turn Context – Stateful dialogues maintaining conversation history via conversation_id for follow-ups
  • Citation Transparency – Detailed responses with source citations for fact-checking and verification
  • ⚠️ No Pre-Built UI – API-first platform requires custom front-end development
  • ⚠️ No Lead Analytics – Lead capture and dashboards must be implemented at application layer
  • 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
Additional Considerations
  • Advanced extras like GraphRAG and agentic flows push beyond basic Q&A
  • Great fit for enterprises needing deeply customized, fully integrated AI solutions.
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
  • No no-code UI—built for devs to wire into their own front ends.
  • Dashboard is utilitarian: good for testing and monitoring, not for everyday business users.
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 – Developer-first RAG infrastructure combining open-source flexibility with managed cloud service
  • Target customers – Dev teams needing high-performance RAG, enterprises requiring millions tokens/second ingestion
  • Key competitors – LangChain/LangSmith, Deepset/Haystack, Pinecone Assistant, custom RAG implementations
  • Competitive advantages – HybridRAG (150% accuracy boost), async auto-scaling, 40+ formats, sub-second latency
  • Pricing advantage – Free tier + $25/mo Dev plan; open-source foundation enables cost optimization
  • Use case fit – Massive document volumes, advanced RAG needs, self-hosting control requirements
  • 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
  • LLM-Agnostic Architecture – GPT-4, GPT-3.5, Claude, Llama 2, and other open-source models
  • Model Flexibility – Easy swapping to balance cost/performance without vendor lock-in
  • Custom Support – Configure any LLM via API including fine-tuned or proprietary models
  • Embedding Providers – Multiple embedding options for semantic search and vector generation
  • ✅ Full control over temperature, max tokens, and generation parameters
  • 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
  • HybridRAG Technology – Vector search + knowledge graphs for 150% accuracy improvement
  • Hybrid Search – Dense vector + keyword with reciprocal rank fusion
  • Agentic RAG – Reasoning agent for autonomous research across documents and web
  • Multimodal Ingestion – 40+ formats (PDFs, spreadsheets, audio) at massive scale
  • ✅ Millions of tokens/second async auto-scaling ingestion throughput
  • ✅ Sub-second latency even at enterprise scale with optimized operations
  • 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
  • Enterprise Knowledge – Process millions of documents with knowledge graph relationships
  • Support Automation – RAG-powered support bots with accurate, grounded responses
  • Research & Analysis – Agentic RAG for autonomous research across collections and web
  • Compliance & Legal – Large document repositories with precise citation tracking
  • Internal Docs – Developer-focused RAG for code, API references, technical knowledge
  • Custom AI Apps – API-first architecture integrates into any application or workflow
  • 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
  • Data Isolation – Single-tenant architecture with isolated customer data in SciPhi Cloud
  • Self-Hosting Option – On-premise deployment for complete data control in regulated industries
  • Encryption Standards – TLS in transit, AES-256 at rest encryption
  • Access Controls – Document-level granular permissions with role-based access control (RBAC)
  • ✅ Open-source R2R core enables security audits and compliance validation
  • ✅ Self-hosted deployments tunable for HIPAA, SOC 2, and other regulations
  • 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
  • Free Tier – Generous no-credit-card tier for experimentation and development
  • Developer Plan – $25/month for individual developers and small projects
  • Enterprise Plans – Custom pricing based on scale, features, and support
  • Self-Hosting – Open-source R2R available free (infrastructure costs only)
  • ✅ Flat subscription pricing without per-query or per-document charges
  • ✅ Managed cloud handles infrastructure, deployment, scaling, updates, maintenance
  • 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
  • Comprehensive Docs – Detailed docs at r2r-docs.sciphi.ai covering all features and endpoints
  • GitHub Repository – Active open-source development at github.com/SciPhi-AI/R2R with code examples
  • Community Support – Discord community and GitHub issues for peer support
  • Enterprise Support – Dedicated channels for enterprise customers with SLAs
  • ✅ Python client (R2RClient) with extensive examples and starter code
  • ✅ Developer dashboard with real-time logs, latency, and retrieval quality metrics
  • 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
R A G-as-a- Service Assessment
  • Platform Type – HYBRID RAG-AS-A-SERVICE combining open-source R2R with managed SciPhi Cloud
  • Core Mission – Bridge experimental RAG models to production-ready systems with deployment flexibility
  • Developer Target – Built for OSS community, startups, enterprises emphasizing developer flexibility and control
  • RAG Leadership – HybridRAG (150% accuracy), millions tokens/second, 40+ formats, sub-second latency
  • ✅ Open-source R2R core on GitHub enables customization, portability, avoids vendor lock-in
  • ⚠️ NO no-code features – No chat widgets, visual builders, pre-built integrations or dashboards
  • 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
Limitations & Considerations
  • ⚠️ Developer-Focused – Requires technical expertise to build and wire custom front ends
  • ⚠️ Infrastructure Requirements – Self-hosting needs GPU infrastructure and DevOps expertise
  • ⚠️ Integration Effort – API-first design means building your own chat UI
  • ⚠️ Learning Curve – Advanced features like knowledge graphs require RAG concept understanding
  • ⚠️ Community Support Limits – Open-source support relies on community unless enterprise plan
  • 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

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Final Thoughts

Final Verdict: SciPhi vs Supavec

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

When to Choose SciPhi

  • You value state-of-the-art retrieval accuracy
  • Open-source with strong community
  • Production-ready with proven scalability

Best For: State-of-the-art retrieval accuracy

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 SciPhi 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

SciPhi starts at custom pricing, 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 SciPhi 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: December 31, 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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