In this comprehensive guide, we compare RAGFlow and Stonly 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 RAGFlow and Stonly, 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 RAGFlow if: you value truly open-source (apache 2.0) with 68k+ github stars - vibrant community
Choose Stonly if: you value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
About RAGFlow
RAGFlow is open-source rag orchestration engine for document ai. Open-source RAG engine with deep document understanding, hybrid retrieval, and template-based chunking for extracting knowledge from complex formatted data. Founded in 2024, headquartered in Global (Open Source), the platform has established itself as a reliable solution in the RAG space.
Overall Rating
80/100
Starting Price
Custom
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
Key Differences at a Glance
In terms of user ratings, Stonly in overall satisfaction. From a cost perspective, RAGFlow starts at a lower price point. The platforms also differ in their primary focus: RAG Platform versus Knowledge Management. 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
RAGFlow
Stonly
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Supported Formats: PDFs, Word documents (.docx), Excel spreadsheets, PowerPoint slides, plain text, images, scanned PDFs with OCR
Deep Document Understanding: Template-based chunking with layout recognition model preserving document structure, sections, headings, and formatting
External Data Connectors: Confluence pages, AWS S3 buckets, Google Drive folders, Notion workspaces, Discord channels
Scheduled Syncing: Automated refresh frequencies for continuous data ingestion from external sources
Scalability: Built on Elasticsearch/Infinity vector store - handles virtually unlimited tokens and millions of documents
Manual Upload: Via Admin UI or API for individual file ingestion
Complex Format Support: Advanced parsing for richly formatted documents, scanned PDFs, and image-based content
Self-Hosted Infrastructure: User manages scaling by allocating sufficient servers/cluster resources
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
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
Native Integrations: None - no pre-built connectors for Slack, Teams, WhatsApp, Telegram
Multi-Lingual Support: Depends on chosen LLM - language-agnostic retrieval engine. Chinese UI supported natively
Conversation Context: Session-based conversation API (v0.22+) maintains multi-turn dialogue context
Grounded Citations: Answers backed by source citations with reduced hallucinations
Lead Capture: Not built-in - would require custom implementation in frontend
Analytics Dashboard: Not provided out-of-box - developers must build or integrate external tools
Human Handoff: Not native - custom logic required to detect low-confidence answers and redirect to human agents
Q&A Foundation: Core focus on accurate retrieval-augmented answers with source transparency
Customer Engagement: Business features (lead capture, handoff, analytics) left to user implementation
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
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
Customization & Branding
UI Customization: Full control via source code modification - Admin UI can be styled/rebranded
Scalability Model: Horizontal (add servers) and vertical (upgrade hardware) scaling
Database Backend: Elasticsearch or Infinity vector store for document indexing
Resource Management: User provisions CPU, memory, storage, GPU (for local models)
No SaaS Option: Self-hosted only - no managed cloud service available
High Availability: User configures load balancing, redundancy, failover
Maintenance Burden: User handles updates, patches, monitoring, backups
Enterprise Capability: Can scale to enterprise demands with proper infrastructure investment
N/A
N/A
Additional Considerations
Platform Type Clarity: TRUE RAG PLATFORM (Open-Source Engine) - self-hosted infrastructure platform, NOT SaaS - requires DevOps expertise for deployment and maintenance
Target Audience: Developer teams, enterprises with DevOps capabilities, research organizations requiring complete control and customization vs turnkey SaaS solutions
Primary Strength: Open-source freedom with zero licensing costs, complete customization, cutting-edge RAG innovation (GraphRAG, RAPTOR, agentic workflows) often implemented before commercial platforms
State-of-the-Art RAG Capabilities: Hybrid retrieval (full-text + vector + re-ranking) with deep document understanding, layout recognition, structure preservation, multiple recall strategies, and grounded citations
Complete Data Control: Self-hosted architecture means data never leaves your infrastructure - suitable for government/corporate secrets, strict data governance, air-gapped operation with local LLMs
CRITICAL LIMITATION - DevOps Expertise Required: Not suitable for teams without technical infrastructure and container orchestration skills - steep learning curve for setup, maintenance, scaling, and monitoring
CRITICAL LIMITATION - No Managed Service: Self-hosted only with NO SaaS option for teams wanting turnkey deployment without infrastructure management - ongoing operational overhead
CRITICAL LIMITATION - Maintenance Burden: User handles Docker updates, security patches, monitoring, backups, disaster recovery, and scaling - continuous hands-on technical work required
Business Feature Gaps: Lead capture, human handoff, sentiment analysis, analytics dashboards not built-in - custom development required for customer engagement features
Infrastructure Costs Variability: Cloud hosting, storage, bandwidth, and engineering costs can exceed SaaS pricing for smaller deployments - unpredictable vs fixed subscriptions
No Commercial SLA: Community support without guaranteed response times or uptime commitments - not suitable for mission-critical 24/7 requirements requiring formal support agreements
Production Readiness Effort: Requires significant effort to operationalize with monitoring, logging, alerting, security hardening, disaster recovery vs instant SaaS deployment
Use Case Fit: Ideal for enterprises prioritizing control, compliance, and customization over convenience; poor fit for non-technical teams or rapid deployment needs
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)
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.
Core Chatbot Features
Q&A Foundation: Core focus on accurate retrieval-augmented answers with source transparency and grounded citations reducing hallucinations
Multi-Lingual Support: Depends on chosen LLM - language-agnostic retrieval engine with Chinese UI supported natively for Asian markets
Conversation Context: Session-based conversation API (v0.22+) maintains multi-turn dialogue context and conversation history across interactions
Reference Chat UI: Demo interface included in repository - can be embedded or customized as starting point for custom implementations
Grounded Citations: Answers backed by source citations with specific text chunks dramatically reducing hallucinations through evidence transparency
Lead Capture: Not built-in - would require custom implementation in frontend application layer vs native platform features
Analytics Dashboard: Not provided out-of-box - developers must build or integrate external tools (Prometheus, Grafana, Datadog) for metrics
Human Handoff: Not native - custom logic required to detect low-confidence answers and redirect to human agents with context transfer
Customer Engagement Features: Business features (lead capture, handoff, analytics, sentiment tracking) left to user implementation vs turnkey chatbot platforms
Developer-First Philosophy: Provides building blocks (APIs, libraries, retrieval engine) but no turnkey channel deployment or business user dashboards
Knowledge Updates: Add/remove files anytime via Admin UI or API - continuous indexing without downtime for always-current knowledge bases
External Sync: Automated data source refresh from Google Drive, S3, Confluence, Notion with near real-time updates eliminating manual re-uploads
Behavior Customization: Edit prompt templates and system logic for tone, personality, response handling through configuration files or code modifications
Chunking Strategies: Template-based chunking configurable per document type - paragraph-sized for FAQs, larger with overlap for narratives preserving context
No GUI Toggles: Customization requires editing config files or source code vs point-and-click dashboards - technical expertise assumed
Ultimate Freedom: Integrate translation services, custom re-ranking algorithms, specialized embeddings, or proprietary retrieval mechanisms through code modifications
Deep Tuning Potential: Modify retrieval pipeline, add custom modules, extend functionality at source code level - complete architectural flexibility
Developer Dependency: Specialized behavior changes assume technical expertise and comfort with Python, Docker, API development, and system architecture
Admin UI (v0.22+): Basic graphical interface for file upload, dataset management, data source connections - power users can maintain content after developer setup
No Role-Based Access: Single admin login by default - multi-user management and role-based access control require custom implementation
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
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.
Community & Innovation
GitHub Stars: 68,000+ stars - top open-source RAG project
Growth Recognition: GitHub Octoverse 2024 - fastest-growing open-source AI project
Active Development: Frequent releases, rapid feature additions, responsive maintainers
Community Contributions: Plugins, integrations, tutorials from global developer community
Innovation Leadership: Introduces cutting-edge RAG techniques (hybrid retrieval, deep parsing) early
Transparency: Open-source codebase enables full audit and understanding of retrieval logic
Learning Resource: Serves as reference implementation for RAG best practices
Ecosystem Growth: Third-party tools, wrappers, and integrations emerging from community
Research Alignment: Implements latest academic RAG research in production-ready form
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
Primary Advantage: Open-source freedom with zero licensing costs and complete customization
Technical Superiority: State-of-the-art hybrid retrieval often exceeds commercial RAG accuracy
Data Sovereignty: Self-hosted deployment ensures complete data control and privacy
Innovation Speed: Cutting-edge features (GraphRAG, agentic workflows) before many commercial platforms
Primary Challenge: Requires DevOps expertise - not suitable for teams without technical resources
Cost Trade-Off: No license fees but infrastructure and engineering costs can be significant
Market Position: Developer-first alternative to SaaS RAG platforms for technical organizations
Use Case Fit: Ideal for enterprises prioritizing control, compliance, and customization over convenience
Community Strength: Largest open-source RAG community provides validation and ongoing innovation
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: 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
OpenAI Models: Full support for GPT-4, GPT-4o, GPT-4o-mini, GPT-3.5-turbo, and all OpenAI API-compatible models
Anthropic Claude: Native integration with Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku through dedicated provider
Google Gemini: Support for Gemini Pro and Gemini Ultra via Google Cloud integration
Local Model Deployment: Deploy locally using Ollama, Xinference, IPEX-LLM, or Jina for complete offline operation
Popular Open-Source Models: Embed Llama 2, Llama 3, Mistral, DeepSeek, WizardLM, Vicuna, and other Hugging Face models
Elasticsearch Backend: Production-grade vector store handling virtually unlimited tokens and millions of documents
Infinity Vector Store: Alternative vector storage option for massive-scale document indexing
Multi-Repository Federation: Unified retrieval across multiple data sources with comprehensive context assembly
Cutting-Edge Research: Implements latest academic RAG techniques in production-ready form before commercial platforms
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
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Use Cases
Enterprise Document Analysis: Financial risk analysis, fraud detection, investment research by retrieving and analyzing reports, financial statements, and regulatory documents with verifiable insights
Customer Support Chatbots: Accurate, citation-backed responses for customer inquiries - integrate into virtual assistants to reduce dependency on human agents while improving satisfaction
Legal Document Processing: Complex legal document analysis with structure preservation, citation tracking, and relationship mapping across case law and statutes
Healthcare Documentation: Medical literature review, clinical decision support, patient record analysis with strict data privacy through self-hosted deployment
Research & Development: Scientific paper analysis, patent research, literature review with relationship extraction and knowledge graph construction
Internal Knowledge Management: Enterprise-level low-code tool for managing personal and organizational data with integration into company knowledge bases
Compliance & Regulatory: Compliance document tracking, regulatory analysis, audit support with complete data control and citation trails
Financial Services: Investment research, market analysis, risk assessment by querying vast financial data repositories with accuracy
Technical Documentation: API documentation, product manuals, troubleshooting guides with structure-aware retrieval for developers
Education & Training: Course material organization, student question answering, academic research support with multi-turn dialogue capabilities
Government & Defense: Classified document analysis, intelligence gathering, policy research with complete on-premise deployment and air-gapped operation
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
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)
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
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
Community Support: Very active GitHub community (68,000+ stars) with discussions, issues, and community contributions
Discord Server: Active Discord community for real-time help, discussions, and troubleshooting from users and maintainers
Official Documentation: Comprehensive guides at ragflow.io/docs covering Get Started, configuration, deployment, API reference
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
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 Knowledge Base Features
N/A
Interactive step-by-step guides with visual flow builder
After analyzing features, pricing, performance, and user feedback, both RAGFlow and Stonly are capable platforms that serve different market segments and use cases effectively.
When to Choose RAGFlow
You value truly open-source (apache 2.0) with 68k+ github stars - vibrant community
State-of-the-art hybrid retrieval with multiple recall + fused re-ranking
Deep document understanding extracts knowledge from complex formats (OCR, layouts)
Best For: Truly open-source (Apache 2.0) with 68K+ GitHub stars - vibrant community
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
Migration & Switching Considerations
Switching between RAGFlow and Stonly 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
RAGFlow starts at custom pricing, while Stonly begins at $249/month. 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 RAGFlow and Stonly 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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