In this comprehensive guide, we compare Deviniti 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 Deviniti 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 Deviniti if: you value strong compliance and security focus
Choose Supavec if: you value 100% open source with no vendor lock-in
About Deviniti
Deviniti is self-hosted genai solutions for compliance-critical industries. Deviniti is an AI development company specializing in secure, self-hosted AI agents and LLM solutions for highly regulated industries like finance, healthcare, and legal, with expertise in RAG architecture and custom AI development. Founded in 2010, headquartered in Kraków, Poland, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
77/100
Starting Price
Custom
About Supavec
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, Supavec in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: AI Development 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
Deviniti
Supavec
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Builds custom pipelines to pull in pretty much any source—internal docs, FAQs, websites, databases, even proprietary APIs.
Works with all the usual suspects (PDF, DOCX, etc.) and can tap uncommon sources if the project needs it.
Project case study
Designs scalable setups—hardware, storage, indexing—to handle huge data sets and keep everything fresh with automated pipelines.
Learn more
No one-click Google Drive or Notion connectors—you’ll script the fetch and hit the API yourself.
Because it’s open source, you can build connectors to anything—Postgres, Mongo, S3, you name it.
Runs on Supabase and scales sideways, chunking millions of docs for fast retrieval.
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
Plugs the chatbot into any channel you need—web, mobile, Slack, Teams, or even legacy apps—tailored to your stack.
Spins up custom API endpoints or webhooks to hook into CRMs, ERPs, or ITSM tools (dev work included).
Integration approach
Pure REST for retrieval and generation—no built-in widget or Slack bot.
You code the chat UI or Slack bridge, calling Supavec for answers.
No Zapier—webhooks and automations are DIY inside your app.
If it speaks HTTP, it can talk to Supavec—you just handle the front-end.
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc.
Read more here.
Need more calls? Negotiate or self-host to ditch caps.
Storage isn’t metered—only query volume counts toward the plan.
Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
Deploy on-prem or private cloud for full data control and compliance peace of mind.
Uses strong encryption, access controls, and hooks into your existing security stack.
Security details
Self-hosting keeps everything on your servers—great for tight compliance.
[Privacy note]
Hosted Supavec runs on Supabase with row-level security—each team’s data is fenced off.
No training on your docs—data stays yours.
Enterprises can go dedicated or on-prem for HIPAA/GDPR peace of mind.
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Custom monitoring ties into tools like CloudWatch or Prometheus to track everything.
Can add an admin dashboard or SIEM feeds for real-time analytics and alerts.
More info
No dashboard baked in—log requests yourself or use Supabase metrics when self-hosting.
Hosted plan shows basic call counts; no transcript analytics out of the box.
Need deep insights? Wire up your own monitoring layer.
Designed to play nicely with external logging tools, not ship its own.
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
Hands-on support from Deviniti—from kickoff through post-launch—direct access to the dev team.
Docs, training, and integrations are built around your stack, not one-size-fits-all.
Our services
Community help via GitHub/Discord; paid plans unlock email or priority support.
[Docs]
Open-source means forks, PRs, and home-grown connectors are welcome.
Docs are lean—mostly endpoint references rather than big tutorials.
Code samples pop up in the community, but it’s not a huge library yet.
Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast.
Developer Docs
Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs.
Enterprise Solutions
Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
Can build hybrid agents that run complex, transactional tasks—not just Q&A.
You own the solution end-to-end and can evolve it as AI tech moves forward.
Custom governance
No vendor lock-in: transparent code, offline option, host wherever you like.
Focuses on core RAG—no SSO, dashboards, or fancy UI included.
Great for devs who want full control or must keep data in-house.
Conversation flow, advanced prompts, fancy UI—all yours to build.
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
No out-of-the-box no-code dashboard—IT or bespoke admin panels handle config.
Everyday users chat with the bot; deeper tweaks live with the tech team.
No drag-and-drop dashboard—everything's via API or CLI.
Meant for code-first teams who'll bolt it into their own chat or workflow.
Self-hosters can craft custom GUIs on top, but Supavec keeps the slate blank.
If you want a business-user UI like CustomGPT, you'll layer that yourself.
Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing.
User Experience Review
Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
Market position: Custom AI development agency (200+ clients served) specializing in self-hosted, enterprise RAG solutions with domain-specific fine-tuning and legacy system integration
Target customers: Large enterprises needing fully custom AI solutions, organizations with legacy systems requiring specialized integration, and companies requiring on-premises deployment with complete data sovereignty and compliance control
Key competitors: Azumo, internal AI development teams, Contextual.ai (enterprise), and other custom AI consulting firms
Competitive advantages: 200+ enterprise clients demonstrating proven track record, model-agnostic approach with fine-tuning on proprietary data, on-prem/private cloud deployment for full data control, custom API/workflow development tailored to exact specifications, white-glove support with direct dev team access, and complete solution ownership with bespoke UI/branding
Pricing advantage: Project-based pricing plus optional maintenance; higher upfront cost than SaaS but provides long-term ownership without subscription fees; best value for unique enterprise needs that can't be met with off-the-shelf solutions and require custom integrations
Use case fit: Ideal for enterprises with legacy systems needing specialized AI integration, organizations requiring domain-tuned models with insider terminology, companies needing hybrid AI agents handling complex transactional tasks beyond Q&A, and businesses demanding on-premises deployment with complete data sovereignty and custom compliance measures
Market position: MIT-licensed open-source RAG API built on Supabase, offering lightweight alternative to Carbon.ai with self-hosting capability and minimal API surface
Target customers: Developers building custom RAG applications on budget, startups wanting to avoid RAG platform costs, and organizations requiring self-hosted solutions with Supabase infrastructure for data sovereignty
Key competitors: Carbon.ai, LangChain, SimplyRetrieve, and hosted RAG APIs like CustomGPT/Pinecone Assistant
Competitive advantages: MIT open-source license with no vendor lock-in, Supabase foundation for familiar infrastructure, model-agnostic with easy LLM swapping (GPT-3.5, GPT-4, self-hosted), REST API simplicity with straightforward endpoints, privacy-focused with self-hosting option keeping data on your servers, and minimal abstraction enabling deep customization
Pricing advantage: Free (MIT license) for self-hosting; hosted plans extremely affordable ($190/year Basic for 750 calls/month, $1,490/year Enterprise for 5K calls/month); best value for low-volume applications or teams with Supabase expertise wanting to avoid expensive RAG platforms; 40-90% cheaper than commercial alternatives
Use case fit: Perfect for developers wanting lightweight RAG backend without heavy frameworks, startups minimizing costs with self-hosting on existing Supabase infrastructure, and teams building custom chatbot front-ends needing simple REST API for retrieval without paying for unused dashboard features
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
A I Models
Model-agnostic approach: Supports any LLM - GPT-4, Claude, Llama 2, Falcon, Cohere, or custom models based on client needs
Custom model fine-tuning: Fine-tune models on proprietary data for domain-specific terminology and insider jargon
Local LLM deployment: On-premises model hosting for complete data sovereignty and offline operation
Multiple model support: Deploy different models for different use cases within same infrastructure
Model flexibility: Swap models through new build/deploy cycle as requirements evolve
Custom training pipelines: Build specialized training workflows for continuous model improvement
Model-agnostic architecture: Defaults to GPT-3.5 Turbo for cost-effectiveness, with full support for GPT-4, GPT-4-turbo, and any OpenAI-compatible models
Self-hosted model support: Bring your own LLM - compatible with self-hosted models like Llama, Mistral, or custom fine-tuned models via API endpoints
No model lock-in: Switch between models by changing configuration or prompt path in code without platform restrictions
No markup on AI costs: Users connect their own OpenAI API keys or self-hosted endpoints, paying providers directly without Supavec markup
Note: No built-in model routing: No automatic model selection or load balancing - developers must implement routing logic manually
Note: No prompt optimization layer: Plain RAG implementation without advanced prompt engineering or anti-hallucination guardrails
Quality dependency: Output quality rests entirely on chosen LLM and developer's prompt engineering skills
Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request
Model Selection Details
Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Custom RAG architecture: Best-practice retrieval with multi-index strategies and tuned prompts for precise answers
Domain-specific fine-tuning: Train on proprietary data to eliminate hallucinations and improve accuracy for insider terminology
Custom vector databases: Choose and configure optimal vector DB backend for your scale and performance needs
Hybrid search: Combine semantic and keyword search strategies tailored to your data characteristics
Source attribution: Full citation tracking with confidence scores and document references
Continuous improvement: Ongoing tweaks and refinements to perfect retrieval accuracy over time
Standard RAG architecture: Document chunking with vector embeddings stored in Postgres pgvector extension for semantic search
Embedding generation: Automatic embedding creation during document upload using OpenAI embedding models or custom embedding endpoints
Vector search: Postgres vector search with cosine similarity for retrieval, handling millions of chunks efficiently
Re-indexing speed: Almost instant document re-embedding when updating or overwriting knowledge sources
Metadata support: Custom metadata tagging and filtering capabilities for organized knowledge management
Note: No advanced RAG features: No hybrid search (semantic + keyword), no reranking, no multi-query retrieval, no query expansion
Note: No hallucination detection: No built-in citation validation, factual consistency scoring, or confidence thresholds - developers must implement manually
Note: No retrieval parameter controls: Chunking strategy, similarity thresholds, and top-k configuration require code-level changes
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Enterprise knowledge bases: Self-hosted chatbots with custom knowledge bases for internal company documentation
Legacy system integration: AI agents that interface with proprietary APIs, ERPs, CRMs, and ITSM tools
Regulated industries: On-prem deployment for healthcare, finance, and government with complete data control
Multi-lingual support: Custom chatbots supporting any language with local LLM deployment
Custom channel deployment: Integrate into any channel - web, mobile, Slack, Teams, or legacy applications
Domain-tuned assistants: Specialized agents with fine-tuned models for technical or medical terminology
Custom chatbot backends: Ideal for developers building custom chat interfaces needing simple RAG API without heavy platform overhead
Self-hosted knowledge retrieval: Perfect for organizations requiring data sovereignty with Supabase infrastructure for compliance (GDPR, HIPAA when self-hosted)
Budget-conscious RAG applications: Startups and small teams minimizing costs with MIT open-source license and affordable hosted plans ($190-$1,490/year)
Supabase-native projects: Teams already using Supabase can integrate Supavec seamlessly without additional infrastructure complexity
Developer-first RAG: Code-first teams wanting full control over RAG implementation, eschewing GUI dashboards for API-driven workflows
Not ideal for: Non-technical users requiring no-code interfaces, enterprises needing advanced RAG features (hybrid search, reranking), or teams requiring built-in analytics/monitoring
Not ideal for: Production applications requiring hallucination detection, citation validation, or confidence scoring without custom development
Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
Data residency: Full control over where data is stored and processed (US, EU, on-prem)
No third-party data sharing: Complete data sovereignty with no cloud vendor dependencies
Custom monitoring: Integrated with CloudWatch, Prometheus, or enterprise monitoring tools
Self-hosting advantage: MIT license enables complete data sovereignty - all data stays on your servers for strict compliance requirements
[Privacy note]
Supabase security foundation: Row-level security (RLS) fences off each team's data when using hosted Supavec on Supabase infrastructure
No model training: Your documents never used for LLM training - data remains yours with zero retention by OpenAI or other providers
GDPR/HIPAA ready: Self-hosting enables GDPR and HIPAA compliance when deployed on compliant infrastructure - enterprises can go dedicated or on-premises
Encryption: Standard HTTPS encryption for API calls; at-rest encryption depends on hosting infrastructure (Supabase provides AES-256)
Note: No SOC 2 certification: Open-source project lacks formal SOC 2 Type II, ISO 27001, or other enterprise compliance certifications for hosted plans
Note: No built-in access controls: Authentication, authorization, and RBAC must be implemented by developers in their application layer
Note: Limited hosted security features: Hosted plans lack SSO/SAML, IP whitelisting, or advanced security controls without custom configuration
Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
SOC 2 Type II certification: Industry-leading security standards with regular third-party audits
Security Certifications
GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
Data isolation: Customer data stays isolated and private - platform never trains on user data
Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Project-based pricing: Custom quotes based on scope, complexity, and integration requirements
Typical project range: $50K-$500K+ for initial development depending on complexity
Optional maintenance: Ongoing support and enhancement contracts available post-launch
Infrastructure costs: Client manages cloud or on-prem infrastructure costs separately
No per-seat fees: Own the solution outright without subscription charges
Professional services: Consulting, integration, training, and documentation included in project scope
Long-term value: Higher upfront cost but no recurring SaaS fees - best for permanent enterprise solutions
200+ client portfolio: Proven track record across Fortune 500 and mid-market enterprises
Open-source (Free): MIT-licensed for self-hosting - pay only your infrastructure costs (Supabase, server, storage) with unlimited API calls and no vendor fees
Hosted Free tier: 100 API calls per month for development and testing
[Pricing]
Basic Plan: $190/year ($15.83/month equivalent) - 750 API calls per month, hosted infrastructure, automatic backups, email support
Enterprise Plan: $1,490/year ($124.17/month equivalent) - 5,000 API calls per month, priority support, SLA guarantees, dedicated resources
No per-document charges: Storage not metered separately - only query volume counts toward plan limits
No user seat fees: Pricing based purely on API call volume, not team size or number of developers
Need more calls? Negotiate custom limits with hosted provider or self-host to eliminate caps entirely
Value proposition: 40-90% cheaper than commercial RAG platforms - Basic plan costs less than 1 month of competing platforms while providing annual service
Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security
View Pricing
Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs
Enterprise Solutions
7-Day Free Trial: Full access to Standard features without charges - available to all users
Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
White-glove support: Direct access to development team from kickoff through post-launch
Custom documentation: Tailored documentation for your specific implementation and tech stack
Training programs: Custom training for IT teams and end users on solution usage and maintenance
Dedicated project manager: Single point of contact throughout development lifecycle
Post-launch support: Optional maintenance contracts with SLA guarantees and priority response
Integration support: Hands-on help connecting to existing enterprise systems and workflows
Knowledge transfer: Complete handoff of code, architecture docs, and operational runbooks
Enterprise focus: Proven experience with large-scale deployments and complex requirements
Documentation: Lean API reference docs focusing on endpoint usage with JavaScript code snippets - mostly technical rather than tutorial-heavy
[Docs]
Community support: GitHub Discussions and Discord for free tier and self-hosted users - community-driven help and troubleshooting
Email support: Paid plan users (Basic/Enterprise) get email support with priority levels based on tier
No dedicated CSM: No Customer Success Manager or account management even on Enterprise tier - support ticket-based
GitHub repository: Open-source code welcomes PRs, issues, and community contributions - active maintainer responses
Postman collection: API documentation includes Postman collection for quick testing and integration
Code samples: Community-contributed examples and integrations appearing in GitHub issues and Discord, but not extensive official library
Learning curve: Requires developer skills - no video tutorials, webinars, or certification programs like commercial platforms
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
Active community: User community plus 5,000+ app integrations through Zapier ecosystem
Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
High upfront cost: $50K-$500K+ initial development vs $29-$999/month SaaS solutions
Longer time to value: 2-6 month development cycle vs instant SaaS deployment
Custom maintenance required: Updates and changes require development work, not self-service
No out-of-box features: Everything built from scratch - no pre-built templates or no-code tools
Technical expertise required: IT team needed for ongoing management and infrastructure
Project-based approach: Each enhancement or change may require additional development sprint
Not for budget-constrained SMBs: Best suited for large enterprises with significant AI budgets
Best for unique needs only: Only justified when off-the-shelf solutions cannot meet requirements
No GUI/dashboard: Everything via API or CLI - no business-user interface for content management, analytics, or configuration
Developer-only tool: Requires coding skills for setup, integration, and maintenance - non-technical teams cannot use without developer support
Basic RAG only: Standard retrieval-augmented generation without advanced features like hybrid search, query reranking, multi-query fusion, or query expansion
No observability built-in: No metrics dashboard, conversation analytics, or performance monitoring - must wire up your own logging layer
Manual hallucination handling: No built-in citation validation, confidence scoring, or factual consistency checks - developers must implement safeguards
Limited connectors: No one-click Google Drive, Notion, or cloud storage integrations - must script data fetching and API uploads manually
No conversation management: Stateless API calls without chat history, multi-turn context, or session management - build conversation layer yourself
Infrastructure knowledge required: Self-hosting requires Supabase, Postgres, and vector database expertise - not plug-and-play for non-DevOps teams
Minimal abstraction: Intentionally low-level API design provides control but requires more integration work than higher-level RAG platforms
Managed service approach: Less control over underlying RAG pipeline configuration compared to build-your-own solutions like LangChain
Vendor lock-in: Proprietary platform - migration to alternative RAG solutions requires rebuilding knowledge bases
Model selection: Limited to OpenAI (GPT-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
Custom AI Agents: Build autonomous agents using advanced LLM architecture with planning modules, memory systems, and RAG pipelines tailored to exact business requirements
Agent Development
Planning Module: Agents break down complex tasks into smaller manageable steps using task decomposition methods - enabling multi-step autonomous workflows
Memory System: Retains past interactions ensuring consistent responses in long-running workflows, maintaining context to improve handling of complex tasks over time
RAG Integration: Agents use specialized RAG pipelines, code interpreters, and external APIs to gather and process data efficiently - enhancing ability to access and use external resources for accurate outcomes
RAG Implementation
Tool & API Integration: Agents execute actions beyond Q&A - integrate with CRMs, ERPs, ITSM tools, proprietary APIs, and legacy systems through custom webhooks and endpoints
Domain-Tuned Behavior: Fine-tune on proprietary data for insider terminology, multi-turn memory with context preservation, and any language support including local LLM deployment
Hybrid Agent Capabilities: Build agents that run complex transactional tasks beyond simple Q&A - handle workflows like IT ticket creation, CRM updates, and approval processes
Hybrid Agents
Real-World Proven: Deployed AI Agent in Credit Agricole bank for customer service automation - routes simple queries automatically, flags complex ones for human support, and drafts personalized replies
Stateless RAG Architecture: Pure retrieval and generation without built-in conversation state—developers implement multi-turn context and session management in application layer
Model-Agnostic Generation: Defaults to GPT-3.5 but supports GPT-4, self-hosted LLMs (Llama, Mistral), and any OpenAI-compatible models—no vendor lock-in for generation
Postgres Vector Search: Fast approximate nearest neighbor search using pgvector extension with cosine similarity—handles millions of chunks efficiently at enterprise scale
Metadata Filtering: Custom metadata tagging and filtering capabilities enabling organized knowledge management and multi-tenant architectures
Real-Time Re-Indexing: Almost instant document re-embedding when updating or overwriting knowledge sources—no lengthy reprocessing delays
REST API Foundation: Straightforward endpoints for file uploads, text uploads, and search with plain-JSON responses—easy integration from any programming language
Supabase Integration: Built on Supabase infrastructure leveraging PostgreSQL, Row-Level Security (RLS), and battle-tested backend for familiar deployment
LIMITATION - No Built-In Chat UI: API-only platform requiring developers to build custom chat interfaces—not a turnkey chatbot solution with widgets
LIMITATION - No Lead Capture: No built-in lead generation, email collection, or CRM integration capabilities—must be implemented at application layer
LIMITATION - No Human Handoff: No escalation workflows, live agent transfer, or fallback mechanisms—conversational features are developer responsibility
LIMITATION - No Multi-Channel Integrations: No native Slack, Teams, WhatsApp, or messaging platform connectors—developers build integration layer
LIMITATION - No Session Management: Stateless API design without conversation history tracking or multi-turn context retention—application must manage state
LIMITATION - No Advanced RAG: Missing hybrid search, reranking, knowledge graphs, multi-query retrieval, query expansion found in enterprise platforms
LIMITATION - No Observability Dashboard: No analytics, conversation metrics, or performance monitoring UI—must integrate external logging tools
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
Platform Type: CUSTOM AI DEVELOPMENT CONSULTANCY - not a platform but professional services firm building bespoke enterprise RAG solutions and AI agents from scratch (200+ clients served)
Core Offering: Project-based custom development of self-hosted AI agents, RAG architectures, and LLM applications tailored to exact specifications - not pre-built software or SaaS
Agent Capabilities: Build fully autonomous AI agents with planning modules, memory systems, RAG pipelines, and tool integration - proven in regulated industries like banking (Credit Agricole deployment)
Agent Services
Developer Experience: White-glove professional services with dedicated dev team, project-specific API development (JSON over HTTP), custom documentation and samples, hands-on support from kickoff through post-launch
No-Code Capabilities: NONE - everything requires custom development work. No dashboard, visual builders, or self-service tools. IT teams or bespoke admin panels handle configuration post-delivery
Target Market: Large enterprises with legacy systems needing specialized AI integration, organizations requiring on-premises deployment with complete data sovereignty, companies with unique needs that can't be met with off-the-shelf solutions
RAG Technology Approach: Best-practice retrieval with multi-index strategies, tuned prompts, fine-tuning on proprietary data to eliminate hallucinations, custom vector DB selection, and hybrid search strategies tailored to data characteristics
RAG Approach
Deployment Model: On-prem or private cloud only - complete data control with no cloud vendor dependencies, custom infrastructure managed by client, strong encryption and access controls integrated with existing security stack
Enterprise Readiness: ISO 27001 certification, GDPR and CCPA compliance, custom compliance measures for HIPAA or industry-specific requirements, AES-256 encryption, RBAC integrated with existing identity management
Pricing Model: Project-based $50K-$500K+ initial development plus optional ongoing maintenance contracts - higher upfront cost but no recurring SaaS fees, full solution ownership
Use Case Fit: Enterprises with legacy systems needing specialized AI integration, domain-tuned models with insider terminology, hybrid AI agents handling complex transactional tasks, on-premises deployment with complete data sovereignty
NOT A PLATFORM: Does not offer self-service software, API-as-a-service, or turnkey solutions - exclusively custom development consultancy requiring sales engagement and multi-month build cycles
Competitive Positioning: Competes with other AI consultancies (Azumo, internal AI teams) and enterprise RAG platforms - differentiates through 200+ client track record, regulated industry expertise (banking, legal), and complete customization
Platform Type: TRUE RAG-AS-A-SERVICE API - Lightweight MIT-licensed open-source RAG backend built on Supabase with self-hosting capability and minimal API surface
Core Mission: Provide transparent, open-source alternative to proprietary RAG services (Carbon.ai shutdown response) with full cost control and no vendor lock-in
Target Market: Developers building custom RAG applications on budget, startups minimizing costs with self-hosting, organizations requiring data sovereignty with Supabase infrastructure
RAG Implementation: Standard RAG architecture with document chunking, OpenAI embeddings, Postgres pgvector semantic search—focused on simplicity over advanced techniques
API-First Design: Pure REST API for retrieval and generation without GUI, widgets, or conversational features—intentionally minimal abstraction for developer control
Self-Hosting Advantage: MIT license enables complete on-premises deployment keeping all data on your servers—ideal for GDPR, HIPAA, data residency compliance
Managed Service Option: Cloud-hosted plans (Free: 100 calls/month, Basic: $190/year for 750 calls/month, Enterprise: $1,490/year for 5K calls/month) eliminate infrastructure management
Pricing Model: Free self-hosting (MIT license) or extremely affordable hosted plans—40-90% cheaper than commercial RAG platforms with no per-document charges or user seat fees
Data Sources: File uploads (PDF, Markdown, TXT) via REST API or raw text ingestion—NO pre-built Google Drive, Notion, or cloud storage connectors (manual scripting required)
Model Flexibility: Model-agnostic with GPT-3.5 default, GPT-4, or self-hosted LLM support—users connect own OpenAI API keys without Supavec markup on AI costs
Security Foundation: Supabase Row-Level Security (RLS) for multi-tenant data isolation, HTTPS encryption, AES-256 at-rest encryption—self-hosting enables GDPR/HIPAA compliance
Support Model: Community GitHub/Discord support for free tier, email support for paid plans—no dedicated CSMs, SLAs, or enterprise account management
Open-Source Ecosystem: Transparent code on GitHub welcoming PRs, forks, and community contributions—no proprietary components or vendor lock-in
LIMITATION - Developer-Only Platform: Requires coding skills for setup, integration, and maintenance—non-technical teams cannot use without developer support
LIMITATION - Basic RAG Features: Standard retrieval without hybrid search, reranking, knowledge graphs, multi-query fusion, or hallucination detection—advanced features require custom development
LIMITATION - No Turnkey Features: No GUI dashboard, conversation management, lead capture, analytics, or multi-channel integrations—pure RAG API requiring application layer development
Comparison Validity: Architectural comparison to full-featured chatbot platforms like CustomGPT.ai requires context—Supavec is lightweight RAG backend API vs complete no-code chatbot builder
Use Case Fit: Perfect for developers wanting lightweight RAG backend without heavy frameworks, startups minimizing costs with Supabase self-hosting, teams building custom chatbots needing simple REST API for retrieval without paying for unused dashboard features
Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat
API Documentation
Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses
Benchmark Details
Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
After analyzing features, pricing, performance, and user feedback, both Deviniti and Supavec are capable platforms that serve different market segments and use cases effectively.
When to Choose Deviniti
You value strong compliance and security focus
Self-hosted solutions for data privacy
Domain expertise in regulated industries
Best For: Strong compliance and security focus
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 Deviniti 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
Deviniti 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
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 Deviniti 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 19, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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.
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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