In this comprehensive guide, we compare Stonly 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 Stonly 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 Stonly if: you value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
Choose Supavec if: you value 100% open source with no vendor lock-in
About Stonly
Stonly is interactive knowledge base platform with enterprise ai-powered answers. Stonly is a customer support knowledge management platform with embedded AI capabilities focused on interactive step-by-step guides and help desk agent assistance. Its AI Answers feature (Enterprise-only add-on) achieves 71% self-serve success rates, but it's fundamentally a knowledge base platform with AI features—not a pure RAG-as-a-Service solution. Founded in 2017, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$249/mo
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, Stonly in overall satisfaction. From a cost perspective, Supavec offers more competitive entry pricing. The platforms also differ in their primary focus: Knowledge Management 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
Stonly
Supavec
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
PDF uploads confirmed
Public website crawling: Pages not requiring authentication
Zendesk help center content indexing
Proprietary interactive guide format as primary content model
Note: No Google Drive, Dropbox, Notion, or SharePoint integrations for data ingestion
Note: No YouTube transcript extraction (videos can be embedded but not processed)
Note: No direct Word document (.docx) or HTML file imports confirmed
Note: No automatic content syncing from external sources - updates are manual through Stonly's visual editor
Content limits by tier: Basic (5 guides, 400 views/mo), Small Business (unlimited guides, 4K views/mo), Enterprise (custom)
Content versioning: Side-by-side comparison and instant restore on Business and Enterprise plans
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.
L L M Model Options
Note: Undisclosed proprietary LLM - Stonly does not disclose the specific model powering AI Answers
Note: No model selection - users cannot choose between GPT-3.5, GPT-4, Claude, or other models
Note: No temperature controls, fine-tuning, or model routing
AI Profiles: Up to 20 per team for tone and behavior customization
Custom Instructions: Up to 100 per team defining boundaries and style
Guided AI Answers: Define specific questions that trigger predetermined answers, bypassing AI generation for sensitive scenarios
Automatic fallback: When AI confidence is low, system falls back to ML-powered search rather than forcing an answer
Knowledge-grounded approach: AI responses anchored in Stonly guides, external websites, and selected PDFs to reduce hallucinations
Model-agnostic: defaults to GPT-3.5, but switch to GPT-4 or any self-hosted model if you’d like.
No fancy toggle—just change a config or prompt path in code.
No extra prompt magic or anti-hallucination layer—plain RAG.
Quality rests on the LLM you choose and how you prompt it.
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.
Performance & Accuracy
71% self-serve success rate with AI Answers feature (company data)
70-76% support ticket reduction documented in case studies
99.9% uptime claimed but no published SLA details or response time data
Note: No published latency metrics or performance benchmarks
Note: No real-time analytics - Flow reports update every 15 minutes
Hallucination controls: Strong grounding in structured content reduces off-topic responses
Widget lazy loading: Minimizes impact on host website performance
Accuracy = GPT quality + standard RAG lift—no extra guardrails.
Postgres vector search keeps retrieval snappy, even with millions of chunks.
No public head-to-head benchmarks yet; expect “typical GPT-3.5/4 RAG” results.
If you want citations or extra checks, you’ll prompt-engineer them yourself.
Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
Independent tests rate median answer accuracy at 5/5—outpacing many alternatives.
Benchmark Results
Always cites sources so users can verify facts on the spot.
Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
Developer Experience ( A P I & S D Ks)
REST API: Supports user provisioning, content management, widget control
Mobile SDKs (Enterprise only): iOS, Android, React Native, Flutter
Note: No Python SDK or server-side Node.js SDK
Note: No GraphQL API or OpenAPI/Swagger specification
Note: Rate limits not publicly documented
Note: No API Explorer, sandbox environment, or Postman collections
Note: REST API versioning strategy unclear
Widget API: Programmatic control including opening specific content, listening for events, user identification
CSP whitelisting: Instructions documented for Content Security Policy compliance
Widget versioning documented
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.
Ships a well-documented REST API for creating agents, managing projects, ingesting data, and querying chat.
API Documentation
Quick onboarding: Users report creating guides in under 30 minutes
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.
No- Code Interface & Usability
4.8/5 ease of use rating on G2
"Ease of use" mentioned 32 times in G2 reviews
Visual drag-and-drop editor requires no coding
Small learning curve - non-technical teams productive quickly
Guide creation in under 30 minutes reported by users
Pre-built templates for common scenarios
Intuitive interface for support teams
Note: Some navigation confusion reported in admin interface
Note: Cannot edit on mobile devices
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.
R A G-as-a- Service Assessment
Note: NOT a RAG-as-a-Service platform - fundamentally a knowledge base tool with embedded AI
Data source flexibility: Limited (PDF, public web, Zendesk only) vs comprehensive RAG platforms
LLM model options: None (undisclosed proprietary model, no user selection)
API-first architecture: Weak (widget-focused, limited SDKs, no server-side SDKs)
Performance benchmarks: Not published
Self-service AI pricing: Not available (Enterprise-gated, ~$39K/year)
Help desk integration depth: Excellent (best-in-class Zendesk, Salesforce, Freshdesk)
Hallucination controls: Strong (grounded in structured content)
Best for: Customer support ticket deflection, not flexible RAG backends
Not ideal for: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning
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
Competitive Positioning
Unique strength: Interactive guide format for structured support content
vs CustomGPT: Not comparable - different product categories (knowledge base vs RAG-as-a-Service)
vs Zendesk: Lighter-weight alternative focused on self-service guides vs full customer service platform
vs traditional chatbots: Interactive guides provide structured paths vs free-form conversation
Target audience: Support teams using Zendesk/Salesforce, not developers building RAG applications
70-76% ticket reduction documented in case studies
71% self-serve success rate with AI Answers
Enterprise compliance suitable for regulated industries
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
Undisclosed Proprietary LLM: Stonly does not publicly disclose the specific model powering AI Answers feature
No Model Selection: Users cannot choose between GPT-3.5, GPT-4, Claude, Gemini, or other LLM providers
No Temperature Controls: No user-facing controls for adjusting response creativity, randomness, or formatting
No Fine-Tuning or Model Routing: Cannot customize model behavior beyond predefined AI Profiles and Custom Instructions
AI Profiles (Up to 20): Define tone, boundaries, and behavior for different use cases or audiences
Custom Instructions (Up to 100): Set specific rules and style guidelines for AI response generation
Guided AI Answers: Predefined responses for specific questions bypassing AI generation for sensitive scenarios
Automatic Fallback: Low-confidence scenarios trigger fallback to ML-powered search rather than forcing unreliable AI answer
Knowledge-Grounded Approach: AI responses anchored in Stonly guides, external websites, and PDFs to reduce hallucinations
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
AI Answers (Enterprise Add-On): Generative AI responses grounded in Stonly guides, external websites, and selected PDFs
Knowledge-Grounding: Responses anchored to structured content (interactive guides, decision trees, checklists) reducing hallucinations vs generic chatbots
Confidence-Based Fallback: Automatic switch to ML-powered search when AI confidence is low preventing unreliable answers
Multi-Source Ingestion: PDF uploads, public website crawling, Zendesk help center content indexing
Interactive Guide Format: Proprietary content model combining structured workflows with AI-generated answers
Limited Data Sources: No Google Drive, Dropbox, Notion, SharePoint, or YouTube transcript extraction
Manual Content Updates: Updates through Stonly's visual editor—no automatic syncing from external sources
71% Self-Serve Success Rate: Documented effectiveness of AI Answers in reducing support escalations
Hallucination Controls: Strong grounding in structured content vs open-ended conversational AI
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
Customer Support Ticket Deflection: 70-76% ticket reduction through interactive self-service guides and AI Answers
Help Desk Integration: Deep Zendesk, Salesforce Service Cloud, Freshdesk, ServiceNow integration for unified support workflows
Interactive Onboarding: Step-by-step guides, decision trees, and checklists for product onboarding and user education
Knowledge Base Enhancement: Augment traditional help centers with interactive guides and AI-powered search
Agent Assistance: Provide support agents with guided workflows and AI answers during live interactions
Multi-Language Support: Auto-translation on Enterprise plan for global support teams and multilingual customers
Complex Troubleshooting: Decision tree logic guides users through multi-step troubleshooting processes
Compliance & Training: Structured guides ensuring consistent information delivery for regulated industries
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)
International Data Transfers: Standard Contractual Clauses for EU compliance and data protection
Data Residency: Options not publicly documented—may limit deployment in certain jurisdictions
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
Automatic Tier Upgrades: Exceeding limits for 2 consecutive months triggers automatic upgrade and billing adjustment
Enterprise-Gated Features: AI Answers, Mobile SDKs, SAML SSO, white-labeling all require Enterprise plan
Average Enterprise Contract: ~$39,000 annually according to Vendr procurement data
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
4.8/5 G2 Rating: 132 reviews with consistently high satisfaction scores
Ease of Use Praised: "Ease of use" mentioned 32 times in G2 reviews indicating intuitive platform
Help Center Documentation: Comprehensive guides and tutorials for platform features
Email and Chat Support: Standard support channels for all paid plans
Dedicated Support (Enterprise): Priority support with dedicated account team and faster response times
Pre-Built Templates: Common support scenario templates accelerating guide creation
Quick Onboarding: Users report creating guides in under 30 minutes with small learning curve
REST API Documentation: API reference for user provisioning, content management, and widget control
Mobile SDKs (Enterprise): iOS, Android, React Native, Flutter for native app integration
Limited Developer Resources: No Python/Node.js SDKs, GraphQL, OpenAPI specs, or API Explorer/sandbox
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
No Real-Time Analytics: Flow reports update every 15 minutes—not true real-time monitoring
Limited Developer API: No Python/Node.js SDKs, GraphQL, Swagger specs, or API sandbox for testing
Overage Pricing Escalation: View limits can trigger expensive automatic upgrades after 2 consecutive months
Not Ideal For: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning, or flexible data source integration
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
Conversational AI Bot: Delivers confident answers backed by verified structured knowledge unlike generic LLMs that can hallucinate or invent answers
Knowledge-grounded responses: Provides answers backed by verified structured knowledge from guides you create preventing fabricated information
AI Agent Assist: Automatically summarizes tickets, suggests right path to resolution, and generates responses for support agents
Three core automation functions: Automatically analyzes and summarizes support ticket content, recommends most relevant Stonly guide/knowledge path to resolve issues, drafts complete responses for agents to review/edit/send
Process automation: Define processes to be followed and link them to different back-office tools to resolve customer requests before they reach support
Personalized knowledge: AI-powered solutions and process automation allow creation of guides, walkthroughs, checklists, knowledge bases adapting to each customer's needs
71% self-serve success rate: With AI Answers feature documented in company data
Hallucination reduction: Knowledge-grounding approach vs generic chatbots reduces off-topic responses
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
Additional Considerations
Limited UI customization: Limited ability to customize user interface and workflows to match specific brand requirements is primary user concern
Basic collaboration tools: Without real-time editing or advanced team management features can hinder team productivity when multiple people need to work together
No offline access: Guides unavailable without internet connectivity reducing usability in areas with unreliable internet
Performance degradation: Can degrade with very large or complex guides causing slower responsiveness indicating scalability concerns
Restricted language options: Limit efficient creation of multilingual content which may be barrier for global organizations
Mixed media support missing: Users find missing features wishing for mixed media support and enhanced reporting tools
Step ordering difficulties: Users report limitations in feature usability and difficulties with step ordering though support offers helpful workarounds
Requires coding knowledge: Unlike most competitors, doesn't advertise as no-code platform - need coding knowledge to track events, target users, stream data, and style content
Image workflow limitations: Inability to use images in base offering limits utility in some workflows with some advanced features requiring extra costs
View-based pricing: Charges additional fees based on guide views - customers exceeding 4,000 guide views/month pay extra $250-500 monthly depending on volume
Integration reliability: Users find lack of integrations limits ability to fully connect Stonly with other tools - Stonly/Zendesk integration isn't as reliable as desired (stops working every few weeks)
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.
CSS and HTML customization: Change layout and look of knowledge base with custom code capabilities
Intuitive customization tools: Easy-to-use tools that don't require code for basic customization
Layout customization: Decide how content is structured and presented with flexible options
Design controls: Manage visual components like colors, logo, or cover image for brand alignment
Personalized content: Use customer data to show personalized content from knowledge base for targeted experiences
Data-driven personalization: Customers see what they need right away when first accessing knowledge base
Analytics insights: Guide usage analytics provide insight into customer behavior for continuous improvement
Highly flexible platform: Users appreciate ability to use Stonly for knowledge bases and guided tours with target properties based on specific user needs
Rich media support: Add images, GIFs, videos, and annotations to bring knowledge base content to life
Third-party scripts: Install scripts from other tools like Google Analytics for extended functionality
Upload or overwrite docs any time—re-embeds almost instantly.
Behavior lives in your prompts; there’s no GUI for personas.
Multi-lingual works fine—just tell the LLM in your prompt.
Add metadata, tweak chunking—then build logic around it as needed.
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.
After analyzing features, pricing, performance, and user feedback, both Stonly and Supavec are capable platforms that serve different market segments and use cases effectively.
When to Choose Stonly
You value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
Deep help desk integrations - bidirectional Zendesk, Salesforce, Freshdesk, ServiceNow connections
Strong compliance - SOC 2 Type 2, GDPR, HIPAA, ISO 27001, PCI, CSA Star Level 1
Best For: Exceptional ease of use - 4.8/5 G2 rating with intuitive visual editor praised in 32 reviews
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 Stonly 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
Stonly starts at $249/month, while Supavec begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
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 Stonly 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 16, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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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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