In this comprehensive guide, we compare Supavec and Yellow.ai 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 Supavec and Yellow.ai, 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 Supavec if: you value 100% open source with no vendor lock-in
Choose Yellow.ai if: you value genuinely comprehensive 35+ channel coverage: whatsapp bsp, messenger, instagram, telegram, slack, teams, voice, sms
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
About Yellow.ai
Yellow.ai is enterprise conversational ai platform with multi-llm orchestration. Enterprise conversational AI platform with embedded RAG capabilities processing 16 billion+ conversations annually. Multi-LLM orchestration across 35+ channels and 135+ languages with proprietary YellowG LLM claiming <1% hallucination rates. Founded in 2016, headquartered in San Mateo, CA, USA / Bengaluru, India, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
85/100
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 Conversational AI. 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.
Automatic Synchronization: Configurable intervals - hourly, daily, weekly for external knowledge base updates
Website Crawling: URL ingestion and sitemap.xml parsing for structured site content extraction
Missing Integrations: No Google Drive, Dropbox, or Notion support - significant gap vs competitors
YouTube Limitation: Transcript ingestion not natively supported
API Gap: No programmatic document upload or knowledge base management via API
Q&A Extraction: T5 model-based question-answer pair generation from ingested documents
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
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.
Just the essentials: retrieve chunks + LLM answer. Calls are stateless, no baked-in chat history.
No lead capture or human handoff—add those in your own layer.
Pulls the right text fast, then lets your LLM craft the reply.
Perfect if you only need raw RAG and will build the conversation bits yourself.
Multi-Turn Conversations: Super Agent maintains conversation context across turns with intent detection, entity extraction, slot filling, and dialogue state management
150+ Language Support: Automatic language detection with native multilingual processing across all 150+ supported languages reducing accuracy loss vs translation-based systems
Human Handoff: Configurable escalation triggers with full conversation history transfer, agent workload balancing, queue management, and SLA tracking
Analytics & Insights: Comprehensive dashboards with containment rates, CSAT scores, conversation flows, drop-off points, user journey analytics, and business KPI tracking
Agent Performance Monitoring: Bot accuracy scoring, user satisfaction metrics, conversation success rates, A/B testing capabilities for continuous improvement
Voice AI Capabilities: Real-time voice agents in 50+ languages with sentiment analysis during calls, IVR integration, call deflection, automated transcription
Lead Capture & Qualification: Real-time lead scoring, CRM integration (Salesforce, HubSpot, Zoho), automatic contact creation, lead routing based on firmographics
Safety & Conduct Controls: Configurable filters ensuring ethical communication, avoiding harmful topics, handling sensitive data responsibly with compliance guardrails
Conversational Behavior Rules: Define conversation rules guiding agent responses in different situations ensuring consistent interactions across channels and use cases
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Customization & Branding
No pre-made UI, no theming—branding lives in whatever front-end you create.
Open source means zero “Supavec” label to hide—your app, your look.
Add domain checks or auth however you like in your code.
It’s “white-label” by default because Supavec is API-only.
Visual Studio: Drag-and-drop conversation flow builder with no-code interface for business users
White-Labeling: Custom branding, domains, widget appearance on Enterprise plan
Komodo-7B: Indonesia-focused with 11+ regional language variants for Southeast Asian market
T5 Fine-Tuned: SQuAD/TriviaQA training for Document Cognition Q&A extraction (75-85% accuracy)
GPT Integration: GPT-3 and GPT-3.5 integrations documented in platform materials
GPT-4/Claude: Support not explicitly confirmed in documentation - unclear availability
Dynamic Model Routing: Automatic selection via Dynamic AI Agent based on query complexity and context requirements
Enterprise Tuning: Proprietary models trained on anonymized customer interactions with PII masking at data layer
Focus: Enterprise-specific tuning prioritized over raw model access and flexibility
Abstracted Selection: Model routing handled automatically - minimal user control over specific model choice
Taps into top models—OpenAI’s GPT-5.1 series, GPT-4 series, and even Anthropic’s Claude for enterprise needs (4.5 opus and sonnet, etc ).
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Developer Experience ( A P I & S D Ks)
Straightforward REST endpoints for file uploads, text uploads, and search.
[Examples]
No official SDKs—use fetch/axios or roll your own wrapper.
Docs are concise with JS snippets; Postman collection included.
Full source is on GitHub, welcoming community tweaks.
Platform-First Architecture: Designed for UI-based development with APIs serving supplementary functions (not primary access)
Available via API: User management (create/update/delete/list), event pushing for custom triggers, outbound notifications, webhook integrations
NOT Available via API: Bot/agent creation or management, document upload, knowledge base management, direct RAG query endpoints, embedding/vector store access, analytics data export
Mobile SDKs: Well-documented Android (Java), iOS (Swift), React Native, Flutter, Cordova with complete code examples, Postman collections, demo applications
Python SDK: Does not exist - major limitation for backend developers and data science teams
Web SDK: Script tag injection only (no npm package) - documentation criticized as incomplete by G2 reviewers
Rate Limits: Not publicly documented - no transparency for production capacity planning
OpenAPI Spec: Not published - no Swagger documentation for API exploration
Critical Limitation: Cannot use Yellow.ai as RAG backend - queries must flow through platform conversation flows vs direct API calls
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Welcome Messages & Greetings: Personalized welcome messages for different channels, user segments, and conversation contexts with dynamic variable substitution
Fallback Behaviors: Configurable responses for knowledge gaps, API failures, validation errors, low-confidence scenarios with escalation path options
Multi-KB Support: Multiple knowledge bases per organization with role-based access, departmental segregation, and cross-KB search capabilities
Auto-Reindexing: Automatic knowledge base refresh when source content changes in connected systems ensuring always-current information
Dynamic Prompt Engineering: Custom system prompts, temperature controls, response length limits, creativity settings configurable per use case
Channel-Specific Customization: Different agent behaviors, response formats, media handling per channel (WhatsApp, voice, web, email)
CRITICAL LIMITATION - Opaque RAG Implementation: Retrieval mechanisms, embedding models, chunking strategies, similarity thresholds not exposed for developer configuration
CRITICAL LIMITATION - NO Programmatic Knowledge API: Knowledge base management requires UI interaction - no API for document upload, embedding updates, or retrieval tuning
Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
Supports multiple agents per account, so different teams can have their own bots.
Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
MIT-licensed open source: self-host for free (pay your own infra).
Channel-Specific Metrics: Performance tracking across messaging, voice, web, mobile channels independently
User Engagement Tracking: MTU (Monthly Transacting Users) monitoring and conversation volume analytics
API Analytics: Not publicly documented - no programmatic access to analytics data
Export Limitation: Analytics data export via API not available - UI-based reporting only
Real-Time Monitoring: Live dashboard visibility but specific alerting capabilities not emphasized
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
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.
Multi-Channel Support: Email, chat, phone support with tier-based access levels
Enterprise Support: Dedicated customer success managers, priority support, SLA guarantees on Enterprise plan
Implementation Services: Professional services included with typical 4-month deployment timeline
Documentation: Available at docs.yellow.ai with API references, mobile SDK guides, Postman collections
Training & Onboarding: Included in enterprise packages with dedicated resources
Community Forums: Available for peer support and knowledge sharing
G2 Feedback: Mixed support quality post-onboarding noted by reviewers, documentation gaps cited
Gartner Recognition: Magic Quadrant 'Challenger' status (2023/2025) provides analyst validation
Customer Base: Enterprise brands including Sony, Domino's, Hyundai, Volkswagen, Ferrellgas across 85+ countries
Learning Curve: Steep curve noted - one G2 reviewer: "Setup felt akin to solving a Rubik's cube blindfolded"
Developer Resources: Mobile SDK documentation praised, web SDK documentation criticized as incomplete
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
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.
Platform Classification: ENTERPRISE CONVERSATIONAL AI PLATFORM with RAG capabilities, NOT a pure RAG-as-a-Service API platform - emphasis on multi-channel automation and workflow orchestration
Target Audience: Mid-market to enterprise organizations (1,000+ employees) with complex conversational workflows vs individual developers or SMBs requiring simple knowledge retrieval
Primary Strength: Exceptional for enterprise-grade conversational AI across 35+ channels (WhatsApp, voice, web, social) with 150+ language support and 60%+ automation rates in regulated industries
Vertical Expertise: 50% customer concentration in financial services with deep BFSI (Banking, Financial Services, Insurance) domain knowledge and compliance capabilities (PCI DSS, SOC 2, ISO 27001, GDPR, HIPAA)
Voice AI Excellence: Real-time voice agents in 50+ languages with sentiment analysis, IVR integration, call center deflection capabilities differentiate from text-only RAG platforms
CRITICAL LIMITATION - Enterprise Sales Motion: Custom pricing requires sales engagement (2-6 week cycle) with no self-serve option - unsuitable for quick testing or developer experimentation
CRITICAL LIMITATION - Pricing Opacity: No published pricing, user reviews report costs 'much higher than competitors', estimated $1,500-$3,500/month minimum vs $99-$299 in RAG platforms
CRITICAL LIMITATION - Implementation Complexity: 8-12 week implementation timelines common with mandatory professional services vs instant deployment in self-serve platforms
Developer API Limitations: APIs oriented toward conversation orchestration vs programmatic RAG operations (semantic search, embedding controls, retrieval configuration)
Lock-In Concerns: Heavy professional services dependency and complex multi-system integrations create significant switching costs vs API-first RAG platforms
Use Case Mismatch: Exceptional for large-scale enterprise conversational AI deployments across multiple channels; inappropriate for simple document Q&A or developer-centric RAG use cases
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 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.
Visual Studio: Drag-and-drop conversation flow builder positioned as "no-code" platform
Dynamic AI Agent: Zero-training deployment with automatic model routing reduces manual configuration
Multi-Intent Detection: Automatic handling of complex queries without manual flow definition
Pre-Built Templates: Industry-specific conversation templates for faster deployment
Channel Configuration: Guided setup for 35+ messaging and voice channel integrations
Knowledge Management UI: Manual document upload and external system connection configuration
Policy Builder: Visual configuration for multi-checkpoint validation rules and guardrails
RBAC Management: Six permission levels with team access control configuration
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: 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
Primary Advantage: Complete enterprise conversational AI platform with unmatched 35+ channel coverage and 135+ language support
Compliance Leadership: SOC 2, ISO 27001/27018/27701, HIPAA, GDPR, PCI DSS, FedRAMP exceeds most AI platform competitors
Proprietary Innovation: YellowG LLM claims <1% hallucination rate, Komodo-7B for Indonesia, 0.6s response times (vendor benchmarks)
Proven Scale: 16 billion+ conversations annually, customers include Sony, Domino's, Hyundai, Volkswagen across 85+ countries
Regional Strength: Multi-region data centers (US, EU, Singapore, India, Indonesia, UAE) with Komodo-7B for Southeast Asia
Primary Challenge: NOT a RAG-as-a-Service platform - embedded RAG within closed conversational system blocks API-first use cases
Developer Friction: No Python SDK, no knowledge base API, no dedicated RAG endpoints, web SDK documentation gaps
Pricing Barrier: ~$10K-$25K annual minimum with 4-month implementation vs competitors with sub-$100/month self-service tiers
Learning Curve: G2 reviews cite steep complexity - "setup felt akin to solving a Rubik's cube blindfolded"
Market Position: Competes with enterprise CX platforms (Genesys, Twilio, LivePerson) vs RAG API services (CustomGPT.ai, Pinecone Assistant)
Use Case Fit: Exceptional for enterprises needing omnichannel CX automation at scale; poor fit for developers seeking programmable RAG capabilities
Architectural Mismatch: Platform-first vs API-first design makes direct RAG platform comparison fundamentally misleading
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 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
Proprietary YellowG LLM: Custom-trained model with vendor-claimed <1% hallucination rate vs GPT-3's 22.7%, 0.6-second average response time
Komodo-7B: Specialized Indonesia-focused model supporting 11+ regional language variants for Southeast Asian market dominance
Orchestrator LLM: Context switching and multi-intent detection engine with zero-training deployment capability
T5 Fine-Tuned: SQuAD/TriviaQA trained model for Document Cognition with 75-85% accuracy depending on complexity
GPT-3 & GPT-3.5: Integration documented for supplemental processing and model routing
15+ LLM Models: Multi-model architecture combining proprietary and third-party models for optimal task routing
Dynamic Model Routing: Automatic selection based on query complexity, language requirements, and performance optimization
Note: GPT-4/Claude support not explicitly confirmed - availability unclear in documentation
Enterprise Training: Models trained on 16 billion+ anonymized customer conversations with PII masking at data layer
Limited Flexibility: Users cannot manually select models - system handles routing automatically without direct control
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
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
Agentic RAG Architecture: Multi-checkpoint validation combining intelligent retrieval with reasoning and action - Yellow.ai's AI Agents don't just retrieve, they think, act, and learn
Document Cognition (DocCog): T5 model-based Q&A extraction with 75-85% accuracy depending on document complexity
Hallucination Prevention: Proprietary YellowG LLM approach with vendor-claimed <1% rate vs industry averages through training optimization
Automatic Guardrails: Policy compliance and response filtering from deployment without manual configuration requirements
Knowledge Synchronization: Configurable intervals (hourly, daily, weekly) for external sources including Salesforce, ServiceNow, Confluence, SharePoint
Website Crawling: URL ingestion and sitemap.xml parsing for structured site content extraction and Q&A generation
Enterprise Integrations: Bi-directional sync with AWS S3, Prismic, and major enterprise knowledge bases
Note: Closed Architecture: RAG embedded within platform - no direct endpoints, embedding customization, or vector store API access for developers
Note: No API Upload: Document upload requires manual platform UI interaction - cannot programmatically manage knowledge base
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
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 Service Automation: 90% query automation across 35+ channels with 60% operational cost reduction - handles 16 billion+ conversations annually
Employee Experience (EX): IT support automation (password resets, hardware requests), HR policy FAQs, leave applications, pay slip access, conference room bookings with rapid response delivery even in low bandwidth environments
24/7 Support Operations: Minimal human involvement for routine queries, autonomous account issue resolution, transaction execution, multi-department coordination with full context preservation
E-commerce & Retail: Personal shopping assistance (inventory browsing, price comparison, order placement, returns handling), real-time transaction monitoring with suspicious activity blocking
Travel & Hospitality: Booking management for travel, hotels, restaurants with automatic rebooking during disruptions and 24/7 availability
Financial Services: Fraud detection workflows with automated investigation initiation and PCI DSS compliance for payment transactions
Healthcare: HIPAA-compliant patient engagement and support with protected health information handling capabilities
Government & Federal: FedRAMP authorized platform for US federal deployments with complete compliance and security requirements
Real-World Results: Lulu Hypermarket 3M+ unique users in 4 weeks, Sony 21,000+ voice calls in 2 months, Lion Parcel 85% automation rate, AirAsia employee experience transformation
Enterprise Scale: Customers include Sony, Domino's, Hyundai, Volkswagen, Ferrellgas across 85+ countries with billion+ conversation processing
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)
E-commerce: Product recommendations, order assistance, customer inquiries with API integration to 5,000+ apps via Zapier
SaaS onboarding: User guides, feature explanations, troubleshooting with multi-agent support for different teams
Security & Compliance
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
SOC 2 Type II: Independently audited security controls and compliance certification with annual penetration testing validation
ISO Certifications: ISO 27001 (Information Security Management), ISO 27018 (Cloud Privacy Controls), ISO 27701 (Privacy Information Management)
HIPAA Compliant: Healthcare industry ready for protected health information (PHI) handling with Business Associate Agreement support
GDPR Compliant: European data protection and privacy rights with regional data centers in EU for data residency requirements
PCI DSS Certified: Payment Card Industry Data Security Standard Level 1 compliance for financial transaction security
FedRAMP Authorized: Federal Risk and Authorization Management Program certification for US government cloud deployments
Encryption Standards: AES-256 encryption at rest, TLS 1.3 for data in transit exceeding industry baseline requirements
Regional Data Centers: 6 global regions (US, EU, Singapore, India, Indonesia, UAE) with customer-selected data residency for compliance and latency optimization
Enterprise Identity Management: SSO/SAML integration with Google, Microsoft, Azure AD, LDAP for unified access control
RBAC Controls: Six permission levels for granular team access control with IP whitelisting for network-level security
Audit Logs: 15-day API activity retention for compliance reporting and security monitoring
On-Premise Options: Private cloud and complete on-premise deployment available for air-gapped environments and complete data sovereignty
AI Training Privacy: Models trained on anonymized customer interactions with PII masking at data layer before processing
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
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
Basic Plan (AWS Marketplace): ~$10,000/year minimum for single use case implementation with limited channel access
Standard Plan: ~$25,000/year for up to 4 use cases with expanded capabilities and additional channels
Enterprise Plan: Custom pricing requiring sales engagement - unlimited bots, channels, integrations with dedicated support and SLA guarantees
Implementation Timeline: Typically 4 months from contract to full deployment with professional services included (G2 user data)
Additional Costs: Voice AI features and advanced generative AI capabilities incur separate charges beyond base platform subscription
Sales-Led Process: All paid plans beyond free tier require sales contact - no self-service purchasing or transparent public pricing
Payment Terms: Annual contracts standard for commercial plans with monthly billing unavailable for most tiers
Entry Barrier: $10K minimum annual spend creates significant barrier for small businesses, startups, and individual developers
On-Premise Pricing: Custom enterprise pricing for private cloud and on-premise deployments with additional implementation costs
Regional Variations: Pricing may vary by selected data center region and compliance requirements
Scale Justification: 16 billion+ conversations annually and enterprise customer base (Sony, Domino's, Hyundai) validates high-end positioning
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
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
Multi-Channel Support: Email, live chat, phone support with tier-based response time guarantees
Enterprise Support: Dedicated customer success managers, priority support queues, SLA guarantees with 1-hour response times on critical issues
Professional Services: Implementation services included in enterprise packages with typical 4-month deployment timeline and project management
Documentation Portal: Available at docs.yellow.ai with API references, integration guides, mobile SDK documentation with code examples
Mobile SDK Resources: Comprehensive Android, iOS, React Native, Flutter, Cordova documentation with complete code examples, Postman collections, demo applications
Training & Onboarding: Included in enterprise packages with dedicated training resources and guided implementation support
Community Forums: Available for peer support, knowledge sharing, and best practices discussion among Yellow.ai users
Gartner Recognition: Magic Quadrant 'Challenger' status (2023/2025) provides third-party analyst validation and market positioning
Customer Base: Enterprise brands including Sony, Domino's, Hyundai, Volkswagen, Ferrellgas deployed across 85+ countries
G2 Feedback: 4.4/5 overall (106 reviews) with 9.3/10 customization, 9.2/10 proactive engagement - mixed post-onboarding support quality noted
Documentation Gaps: Web SDK documentation criticized as "hit and miss" by reviewers - mobile SDKs better documented than web integration
Learning Curve: Steep complexity curve noted by users - G2 reviewer: "Setup felt akin to solving a Rubik's cube blindfolded"
Developer Resources: Strong mobile SDK documentation, weak Python SDK (doesn't exist), limited API cookbook/advanced tutorial content
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
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
NOT a RAG-as-a-Service Platform: Full-stack enterprise conversational AI with embedded RAG - cannot use Yellow.ai purely as knowledge/RAG backend for custom applications
No API-First Development: Cannot programmatically create bots/agents, upload documents, manage knowledge bases, or directly query RAG endpoints - platform-centric architecture
Missing Developer Tools: No Python SDK (major gap for backend developers), no npm package for web SDK (script tag injection only), no OpenAPI specification published
Knowledge Ingestion Gaps: No Google Drive, Dropbox, Notion integration support - significant gap vs competitors like CustomGPT and YourGPT
YouTube & Audio Limitations: No YouTube transcript ingestion, no native audio/video file processing support
High Entry Barrier: $10K-$25K annual minimum with 4-month implementation timeline vs competitors offering $19-99/month self-service tiers
Use Case Mismatch: Excellent for enterprises needing omnichannel CX automation; poor fit for developers seeking programmable RAG APIs or simple chatbot embedding
Vendor Lock-In Risk: Proprietary platform with limited portability - difficult to migrate conversation flows, knowledge bases, and integrations to alternative solutions
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
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
Massive Scale: 16 billion+ conversations processed annually across enterprise deployments
Multi-Lingual: 135+ languages supported with regional variants (Komodo-7B for 11+ Indonesian languages)
Hallucination Prevention: YellowG LLM claims <1% hallucination rate vs GPT-3's 22.7% in vendor benchmarks
Dynamic AI Agent: Zero-training deployment with automatic model routing and next-action determination
Multi-Intent Detection: Handles complex user queries with context-aware orchestration across conversation turns
Response Speed: 0.6-second average response time (YellowG LLM performance claim)
Automatic Guardrails: Policy compliance and response relevance filtering from deployment without manual configuration
Case Study Performance: Lulu Hypermarket 3M+ unique users in 4 weeks, Sony 21,000+ voice calls in 2 months
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: 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
Platform Type: NOT A RAG-AS-A-SERVICE PLATFORM - Full-stack enterprise conversational AI with embedded RAG
Critical Distinction: RAG functions as embedded feature, not exposed API service - cannot use Yellow.ai purely as knowledge/RAG backend
Document Cognition: 75-85% accuracy with T5 model fine-tuned on SQuAD/TriviaQA for Q&A extraction
Knowledge Architecture: Closed system - no direct RAG query endpoints, embedding access, or vector store API
API Limitations: No programmatic document upload, knowledge base management, or direct retrieval capabilities
Query Flow: Queries must flow through platform conversation flows vs direct API calls to knowledge backend
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
Customization & Flexibility
N/A
Knowledge Updates: Manual via UI only - no API for programmatic document upload or management
After analyzing features, pricing, performance, and user feedback, both Supavec and Yellow.ai are capable platforms that serve different market segments and use cases effectively.
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
When to Choose Yellow.ai
You value genuinely comprehensive 35+ channel coverage: whatsapp bsp, messenger, instagram, telegram, slack, teams, voice, sms
Switching between Supavec and Yellow.ai 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
Supavec starts at custom pricing, while Yellow.ai 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 Supavec and Yellow.ai 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 11, 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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