What are the main differences between Deviniti and RAGFlow?

Deviniti excels in Strong compliance and security focus and Self-hosted solutions for data privacy, scoring 77/100 in user ratings with 81% accuracy. RAGFlow leads in Truly open-source (Apache 2.0) with 68K+ GitHub stars - vibrant community and State-of-the-art hybrid retrieval with multiple recall + fused re-ranking with 80/100 overall rating and N/A% accuracy. Key technical difference: Deviniti focuses on Custom AI agent development while RAGFlow emphasizes Deep document understanding with template-based chunking.

Which is better for enterprises: Deviniti or RAGFlow?

Both platforms provide enterprise-grade security. HIPAA compliance is available in Deviniti. For scalability, both offer cloud-based deployment options. RAGFlow leads with 80/100 enterprise readiness score.

How much does Deviniti cost vs RAGFlow?

Deviniti starts at custom pricing/custom (enterprise model), while RAGFlow begins at contact sales/month (open-source model). RAGFlow provides a free tier. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Deviniti to RAGFlow?

Migration from Deviniti to RAGFlow typically requires API-based data export/import, which RAGFlow provides. RAGFlow provides comprehensive API access excellent documentation for the migration process. Estimated migration time: 1-2 weeks for full deployment. Contact RAGFlow support for migration assistance and best practices.

What integrations do Deviniti and RAGFlow support?

Deviniti integrates with Atlassian suite, Enterprise systems, Custom APIs, Banking systems, Legal platforms (7 integrations total). RAGFlow supports RESTful APIs for conversation, query, and document management, Python interfaces for programmatic control, Reference chat demo UI included in repository, Developer-driven integrations (no pre-built channel connectors), Requires custom coding for Slack, Teams, WhatsApp, etc. (5 integrations available). RAGFlow offers API capabilities. Deviniti offers 40% more integrations, with webhook support for real-time data sync.

What are the main differences between Deviniti and RAGFlow?

Deviniti offers strong capabilities, scoring 0/100 in user ratings. RAGFlow provides unique strengths with 0/100 overall rating. Key technical difference: Deviniti focuses on advanced features while RAGFlow emphasizes specialized capabilities.

Which is better for enterprises: Deviniti or RAGFlow?

Both platforms provide enterprise-grade security. HIPAA compliance varies - contact vendors for details. For scalability, both offer cloud-based deployment options. RAGFlow leads with strong/100 enterprise readiness score.

How much does Deviniti cost vs RAGFlow?

Deviniti starts at custom pricing/month (subscription model), while RAGFlow begins at contact sales/month (subscription model). Both require paid plans. Pricing is relatively comparable. See our detailed pricing breakdown for complete tier comparisons.

Can I migrate from Deviniti to RAGFlow?

Migration from Deviniti to RAGFlow typically requires manual data transfer since API access may be limited. RAGFlow offers standard documentation for the migration process. Estimated migration time: 1-2 days for cloud setup. Contact RAGFlow support for migration assistance and best practices.

What integrations do Deviniti and RAGFlow support?

Deviniti integrates with (multiple platforms). RAGFlow supports (various tools). API availability varies by plan. Integration coverage is comparable.

Deviniti vs RAGFlow

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

More about RAGFlow More about Deviniti Get Started Today

Priyansh KhodiyarDevRel at CustomGPT.ai

Fact checked and reviewed by Bill Cava

Published: 01.04.2025Updated: 25.04.2025

In this comprehensive guide, we compare Deviniti and RAGFlow across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.

Overview

When choosing between Deviniti and RAGFlow, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.

Quick Decision Guide

Choose Deviniti if: you value strong compliance and security focus
Choose RAGFlow if: you value truly open-source (apache 2.0) with 68k+ github stars - vibrant community

About Deviniti

Deviniti is self-hosted genai solutions for compliance-critical industries. Deviniti is an AI development company specializing in secure, self-hosted AI agents and LLM solutions for highly regulated industries like finance, healthcare, and legal, with expertise in RAG architecture and custom AI development. Founded in 2010, headquartered in Kraków, Poland, the platform has established itself as a reliable solution in the RAG space.

Overall Rating

77/100

Starting Price

Custom

About 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

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: AI Development versus RAG Platform. These differences make each platform better suited for specific use cases and organizational requirements.

⚠️ What This Comparison Covers

We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.

Detailed Feature Comparison

Deviniti	RAGFlow	CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Custom pipelines – Ingest any source: docs, APIs, databases, proprietary systems Case study Format support – PDF, DOCX, and uncommon formats as needed Scalable infrastructure – Automated pipelines for huge datasets with fresh indexing Learn more ⚠️ Custom build required – No pre-built connectors or templates	Deep document parsing – PDFs, Word, Excel, PowerPoint, images, scanned PDFs with OCR Layout recognition – Template-based chunking preserving structure, sections, headings External connectors – Confluence, AWS S3, Google Drive, Notion, Discord channels Scheduled sync – Automated refresh for continuous ingestion from external sources Elasticsearch backend – Handles unlimited tokens and millions of documents	1,400+ file formats – PDF, DOCX, Excel, PowerPoint, Markdown, HTML + auto-extraction from ZIP/RAR/7Z archives Website crawling – Sitemap indexing with configurable depth for help docs, FAQs, and public content Multimedia transcription – AI Vision, OCR, YouTube/Vimeo/podcast speech-to-text built-in Cloud integrations – Google Drive, SharePoint, OneDrive, Dropbox, Notion with auto-sync Knowledge platforms – Zendesk, Freshdesk, HubSpot, Confluence, Shopify connectors Massive scale – 60M words (Standard) / 300M words (Premium) per bot with no performance degradation
Integrations & Channels
Multi-channel deployment – Web, mobile, Slack, Teams, or legacy apps Custom APIs – Webhooks for CRMs, ERPs, ITSM with dev work Integration approach ✅ Tailored to stack – Fits exact enterprise architecture requirements ⚠️ Dev effort needed – Each integration requires custom development sprint	⚠️ No native integrations – No pre-built Slack, Teams, WhatsApp, Telegram API-driven – RESTful conversation/query APIs for custom integrations Reference chat UI – Demo interface included, can be embedded or customized Ultimate flexibility – Integrate with any platform via API with engineering work	Website embedding – Lightweight JS widget or iframe with customizable positioning CMS plugins – WordPress, WIX, Webflow, Framer, SquareSpace native support 5,000+ app ecosystem – Zapier connects CRMs, marketing, e-commerce tools MCP Server – Integrate with Claude Desktop, Cursor, ChatGPT, Windsurf OpenAI SDK compatible – Drop-in replacement for OpenAI API endpoints LiveChat + Slack – Native chat widgets with human handoff capabilities
Core Chatbot Features
Domain-tuned AI – Multi-turn memory, context, any language, local LLMs Workflow automation – Lead capture, human handoff, IT tickets Case study ✅ Exact specifications – Built precisely to your requirements	N/A	✅ #1 accuracy – Median 5/5 in independent benchmarks, 10% lower hallucination than OpenAI ✅ Source citations – Every response includes clickable links to original documents ✅ 93% resolution rate – Handles queries autonomously, reducing human workload ✅ 92 languages – Native multilingual support without per-language config ✅ Lead capture – Built-in email collection, custom forms, real-time notifications ✅ Human handoff – Escalation with full conversation context preserved
Customization & Branding
Fully bespoke – UI, tone, flows match brand perfectly Custom approach Domain-specific dialogs – Custom styling and terminology for your industry ⚠️ Changes require dev – Updates need development effort, not self-service	Full source access – Modify Admin UI, styling, behavior at code level White-labeling – Complete branding removal via code editing Custom frontend – Build entirely custom chat using RAGFlow as backend ⚠️ No point-and-click – UI changes require config/code editing	Full white-labeling included – Colors, logos, CSS, custom domains at no extra cost 2-minute setup – No-code wizard with drag-and-drop interface Persona customization – Control AI personality, tone, response style via pre-prompts Visual theme editor – Real-time preview of branding changes Domain allowlisting – Restrict embedding to approved sites only
L L M Model Options
Model-agnostic – GPT-4, Claude, Llama 2, Falcon, any model Services Fine-tuning – Train on proprietary data for insider terminology Local deployment – On-prem hosting for complete data sovereignty ⚠️ Model swaps – Require new build/deploy cycle	Model agnostic – OpenAI GPT-4/3.5, Claude 3, Gemini, Llama, Mistral Local deployment – Ollama, Xinference, IPEX-LLM for complete offline Chinese LLMs – Baichuan, Tencent Hunyuan, Baidu Yiyan, XunFei Spark OpenAI-compatible – Any model with compatible API endpoints ✅ No vendor lock-in – Swap providers freely	GPT-5.1 models – Latest thinking models (Optimal & Smart variants) GPT-4 series – GPT-4, GPT-4 Turbo, GPT-4o available Claude 4.5 – Anthropic's Opus available for Enterprise Auto model routing – Balances cost/performance automatically Zero API key management – All models managed behind the scenes
Developer Experience ( A P I & S D Ks)
Project-specific API – JSON over HTTP tailored to endpoints Example Custom docs – Documentation and samples from Deviniti engineers ✅ Direct support – Access to dev team, not generic docs ⚠️ No public SDK – Everything custom-built for your project	RESTful APIs – Document upload, parsing, datasets, conversation queries Python interfaces – Library calls for programmatic control Extensive docs – ragflow.io/docs with guides and examples ⚠️ No packaged SDK – HTTP requests or direct module calls ⚠️ Docker required – Self-hosted setup with technical expertise	REST API – Full-featured for agents, projects, data ingestion, chat queries Python SDK – Open-source `customgpt-client` with full API coverage Postman collections – Pre-built requests for rapid prototyping Webhooks – Real-time event notifications for conversations and leads OpenAI compatible – Use existing OpenAI SDK code with minimal changes
Performance & Accuracy
Best-practice retrieval – Multi-index, tuned prompts for precision Approach Hallucination reduction – Fine-tune on your data for accuracy ⚠️ Ongoing refinement – Perfecting accuracy needs iterative tweaks	Hybrid retrieval – Full-text + vector + multiple recall with fused re-ranking Grounded citations – Reduces hallucinations with source transparency Deep document parsing – Layout recognition improves retrieval precision Production-grade – Elasticsearch-backed for large datasets and fast queries ✅ Community validated – 68K+ stars, many production deployments	Sub-second responses – Optimized RAG with vector search and multi-layer caching Benchmark-proven – 13% higher accuracy, 34% faster than OpenAI Assistants API Anti-hallucination tech – Responses grounded only in your provided content OpenGraph citations – Rich visual cards with titles, descriptions, images 99.9% uptime – Auto-scaling infrastructure handles traffic spikes
Customization & Flexibility ( Behavior & Knowledge)
Total control – Add sources, tweak tone, inject APIs Details Unlimited flexibility – Dream it, Deviniti builds it ⚠️ Dev sprints – Updates usually require quick development work	N/A	Live content updates – Add/remove content with automatic re-indexing System prompts – Shape agent behavior and voice through instructions Multi-agent support – Different bots for different teams Smart defaults – No ML expertise required for custom behavior
Pricing & Scalability
Project-based – $50K-$500K+ initial, optional maintenance Portfolio Scale to millions – Infrastructure handles huge query volumes ✅ No subscription fees – Own outright without recurring costs ⚠️ High upfront – Much costlier than $29-$999/mo SaaS solutions	N/A	Standard: $99/mo – 60M words, 10 bots Premium: $449/mo – 300M words, 100 bots Auto-scaling – Managed cloud scales with demand Flat rates – No per-query charges
Security & Privacy
On-prem/private cloud – Full data control and compliance Security Strong encryption – AES-256 at rest, TLS 1.3 in transit Security stack integration – Hooks into SIEM, monitoring, access controls ✅ Data sovereignty – No third-party sharing or cloud vendor dependencies	N/A	SOC 2 Type II + GDPR – Third-party audited compliance Encryption – 256-bit AES at rest, SSL/TLS in transit Access controls – RBAC, 2FA, SSO, domain allowlisting Data isolation – Never trains on your data
Observability & Monitoring
Custom monitoring – CloudWatch, Prometheus integration Info Admin dashboards – Real-time analytics, alerts, SIEM feeds ✅ Enterprise tools – Integrates with existing monitoring infrastructure	⚠️ No built-in analytics – Basic admin stats only (doc counts, query history) Logs – Console and file logs for operations and errors External integration – Prometheus, Grafana, Datadog, Splunk compatible Ultimate flexibility – Instrument with any monitoring stack	Real-time dashboard – Query volumes, token usage, response times Customer Intelligence – User behavior patterns, popular queries, knowledge gaps Conversation analytics – Full transcripts, resolution rates, common questions Export capabilities – API export to BI tools and data warehouses
Support & Ecosystem
White-glove support – Direct dev team access, kickoff through post-launch Services Stack-specific docs – Training and documentation tailored to your infrastructure ✅ 200+ clients – Proven track record with Fortune 500 enterprises	68K+ GitHub stars – Largest open-source RAG community Active Discord – Real-time help from users and maintainers Rapid releases – Modern features often before commercial platforms ⚠️ No SLA – Community support, no guaranteed response times	Comprehensive docs – Tutorials, cookbooks, API references Email + in-app support – Under 24hr response time Premium support – Dedicated account managers for Premium/Enterprise Open-source SDK – Python SDK, Postman, GitHub examples 5,000+ Zapier apps – CRMs, e-commerce, marketing integrations
Additional Considerations
Hybrid agents – Complex transactional tasks beyond Q&A Custom governance End-to-end ownership – Own and evolve solution as AI advances ✅ Future-proof – Complete control over technology roadmap	✅ Open-source freedom – Zero licensing, complete customization ✅ Modern RAG features – GraphRAG, RAPTOR, agentic workflows ✅ Data sovereignty – Self-hosted, air-gapped operation possible ⚠️ DevOps expertise required – Docker, infrastructure management ⚠️ Maintenance burden – Updates, patches, monitoring, backups on user ⚠️ No commercial SLA – Community support only	Time-to-value – 2-minute deployment vs weeks with DIY Always current – Auto-updates to latest GPT models Proven scale – 6,000+ organizations, millions of queries Multi-LLM – OpenAI + Claude reduces vendor lock-in
No- Code Interface & Usability
⚠️ No no-code tools – IT or admin panels handle configuration User experience – End users chat; tech team manages tweaks	Admin UI (v0.22+) – Basic file upload, dataset management, connections ⚠️ Not true no-code – Docker, OAuth config requires technical setup Power user access – Analysts can maintain after developer setup ⚠️ Single admin login – No RBAC by default, requires custom implementation	2-minute deployment – Fastest time-to-value in the industry Wizard interface – Step-by-step with visual previews Drag-and-drop – Upload files, paste URLs, connect cloud storage In-browser testing – Test before deploying to production Zero learning curve – Productive on day one
Competitive Positioning
Market position – Custom AI agency (200+ clients), enterprise RAG specialist Target customers – Large enterprises needing custom solutions, legacy system integration Key competitors – Azumo, internal AI teams, Contextual.ai, AI consultancies Advantages – Proven track record, model-agnostic, on-prem deployment, solution ownership Pricing advantage – Higher upfront, no subscriptions; best for unique needs Use case fit – Legacy systems, domain-tuned models, hybrid agents, data sovereignty	Open-source freedom – Zero licensing costs, complete customization Technical superiority – Hybrid retrieval often exceeds commercial accuracy Data sovereignty – Self-hosted ensures complete data control Innovation speed – GraphRAG, agentic workflows before many commercial platforms ⚠️ DevOps required – Not for teams without technical resources	Market position – Leading RAG platform balancing enterprise accuracy with no-code usability. Trusted by 6,000+ orgs including Adobe, MIT, Dropbox. Key differentiators – #1 benchmarked accuracy • 1,400+ formats • Full white-labeling included • Flat-rate pricing vs OpenAI – 10% lower hallucination, 13% higher accuracy, 34% faster vs Botsonic/Chatbase – More file formats, source citations, no hidden costs vs LangChain – Production-ready in 2 min vs weeks of development
A I Models
Model-agnostic – GPT-4, Claude, Llama 2, Falcon, Cohere, custom models Fine-tuning – Proprietary data training for domain-specific terminology Local LLMs – On-prem hosting for sovereignty and offline operation Multiple models – Different models for different use cases ⚠️ Model swaps – Require build/deploy cycle for changes	OpenAI – GPT-4, GPT-4o, GPT-4o-mini, GPT-3.5-turbo and all compatible Anthropic – Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku Google – Gemini Pro and Gemini Ultra via Cloud integration Local models – Ollama, Xinference, IPEX-LLM for complete offline Open-source – Llama 2/3, Mistral, DeepSeek, WizardLM, Vicuna	OpenAI – GPT-5.1 (Optimal/Smart), GPT-4 series Anthropic – Claude 4.5 Opus/Sonnet (Enterprise) Auto-routing – Intelligent model selection for cost/performance Managed – No API keys or fine-tuning required
R A G Capabilities
Custom RAG – Multi-index strategies, tuned prompts for precision Domain fine-tuning – Eliminate hallucinations with proprietary data training Hybrid search – Semantic and keyword strategies tailored to data Source attribution – Full citations with confidence scores ⚠️ Ongoing tweaks – Perfecting retrieval accuracy takes time	Hybrid retrieval – Full-text + vector + multiple recall with fused re-ranking GraphRAG – Relationship-aware knowledge extraction across entities RAPTOR – Hierarchical tree-organized retrieval structures Template-based chunking – Document-type-specific strategies preserving structure Code sandbox – Safe execution for complex analytical tasks	GPT-4 + RAG – Outperforms OpenAI in independent benchmarks Anti-hallucination – Responses grounded in your content only Automatic citations – Clickable source links in every response Sub-second latency – Optimized vector search and caching Scale to 300M words – No performance degradation at scale
Use Cases
Enterprise knowledge bases – Self-hosted chatbots with custom internal docs Legacy integration – AI agents for ERPs, CRMs, ITSM tools Regulated industries – On-prem for healthcare, finance, government compliance Multi-lingual support – Any language with local LLM deployment Hybrid agents – Transactional workflows: IT tickets, approvals, automation	Enterprise document analysis – Financial risk, fraud detection, investment research Legal document processing – Structure preservation, citation tracking Healthcare – Clinical decision support with strict data privacy Government/defense – Classified analysis with air-gapped deployment Research & development – Scientific papers, patents, literature review	Customer support – 24/7 AI handling common queries with citations Internal knowledge – HR policies, onboarding, technical docs Sales enablement – Product info, lead qualification, education Documentation – Help centers, FAQs with auto-crawling E-commerce – Product recommendations, order assistance
Security & Compliance
On-prem deployment – Air-gapped environments, complete data control Custom compliance – HIPAA, GDPR, SOC 2, industry-specific measures Encryption – AES-256 at rest, TLS 1.3 in transit RBAC – Integrated with existing identity management systems ✅ Data residency – Full control over storage location (US, EU, on-prem)	Complete data control – Self-hosted, data never leaves your infrastructure On-premise deployment – Suitable for government/corporate secrets Air-gapped option – Local LLMs eliminate external API exposure User-configured encryption – TLS, VPN, OS-level disk encryption ⚠️ No formal certifications – SOC 2, ISO 27001 via deployment config	SOC 2 Type II + GDPR – Regular third-party audits, full EU compliance 256-bit AES encryption – Data at rest; SSL/TLS in transit SSO + 2FA + RBAC – Enterprise access controls with role-based permissions Data isolation – Never trains on customer data Domain allowlisting – Restrict chatbot to approved domains
Pricing & Plans
Project pricing – $50K-$500K+ based on scope and complexity No subscriptions – Own solution outright without recurring fees Optional maintenance – Ongoing support contracts available post-launch ✅ 200+ clients – Fortune 500 and mid-market proven track record ⚠️ High upfront – Much costlier than $29-$999/mo SaaS platforms	License: $0 – Apache 2.0 open-source, free to use and modify Infrastructure costs – Cloud VMs, storage, networking paid by user LLM API costs – Separate charges for OpenAI/Anthropic (eliminable with local) Engineering costs – DevOps for installation, maintenance, updates ⚠️ TCO variability – Can exceed SaaS for smaller deployments	Standard: $99/mo – 10 chatbots, 60M words, 5K items/bot Premium: $449/mo – 100 chatbots, 300M words, 20K items/bot Enterprise: Custom – SSO, dedicated support, custom SLAs 7-day free trial – Full Standard access, no charges Flat-rate pricing – No per-query charges, no hidden costs
Support & Documentation
White-glove support – Direct dev team access throughout lifecycle Custom documentation – Tailored to your implementation and tech stack Training programs – IT teams and end users trained on solution Knowledge transfer – Complete handoff: code, architecture, runbooks ✅ Enterprise focus – Proven with large-scale, complex deployments	N/A	Documentation hub – Docs, tutorials, API references Support channels – Email, in-app chat, dedicated managers (Premium+) Open-source – Python SDK, Postman, GitHub examples Community – User community + 5,000 Zapier integrations
Limitations & Considerations
⚠️ High upfront cost – $50K-$500K+ vs $29-$999/month SaaS ⚠️ Long time-to-value – 2-6 month build vs instant SaaS deployment ⚠️ Custom maintenance – Updates need dev work, no self-service ⚠️ No templates – Everything built from scratch, no no-code tools ⚠️ IT expertise required – Team needed for infrastructure and management Best for unique needs – Only justified when off-the-shelf fails	⚠️ DevOps expertise required – Not for teams without container orchestration skills ⚠️ No managed service – Self-hosted only, no SaaS option available ⚠️ Maintenance burden – Docker updates, security patches, monitoring on user ⚠️ No native channel integrations – API-driven custom development required ⚠️ No built-in analytics – External tools (Prometheus, Grafana) required Best for – Enterprises with DevOps; poor fit for rapid deployment needs	Managed service – Less control over RAG pipeline vs build-your-own Model selection – OpenAI + Anthropic only; no Cohere, AI21, open-source Real-time data – Requires re-indexing; not ideal for live inventory/prices Enterprise features – Custom SSO only on Enterprise plan
Core Agent Features
Autonomous agents – Planning modules, memory, RAG pipelines Agent Development Planning module – Task decomposition for multi-step autonomous workflows Memory system – Retains interactions for consistent long-running workflows Tool integration – CRMs, ERPs, ITSM, APIs, legacy systems RAG Implementation ✅ Proven deployment – Credit Agricole bank customer service automation	Multi-turn context – Session-based conversation API (v0.22+) Grounded citations – Answers backed by source text chunks Multi-lingual – Depends on chosen LLM, Chinese UI native ⚠️ No lead capture – Requires custom frontend implementation ⚠️ No analytics dashboard – Must integrate external tools	Custom AI Agents – Autonomous GPT-4/Claude agents for business tasks Multi-Agent Systems – Specialized agents for support, sales, knowledge Memory & Context – Persistent conversation history across sessions Tool Integration – Webhooks + 5,000 Zapier apps for automation Continuous Learning – Auto re-indexing without manual retraining
R A G-as-a- Service Assessment
Platform type – CUSTOM AI CONSULTANCY (not SaaS/platform) Core offering – Bespoke enterprise RAG and AI agents (200+ clients) Agent capabilities – Autonomous agents with planning, memory, tool integration Agent Services Developer experience – White-glove services, project-specific APIs, custom docs ⚠️ No no-code – Zero self-service, everything needs custom dev Deployment – On-prem/private cloud only, complete data sovereignty ✅ Enterprise ready – ISO 27001, GDPR/CCPA, custom HIPAA compliance ⚠️ NOT A PLATFORM – Exclusively custom consultancy, multi-month builds	Platform type – TRUE RAG PLATFORM (Open-Source Engine), NOT SaaS Hybrid retrieval – Full-text + vector + re-ranking with deep document parsing Model agnostic – Any LLM (OpenAI, local, custom) without vendor lock-in Target users – Developer teams, enterprises with DevOps capabilities ⚠️ Not for non-technical – Requires Docker, infrastructure management	Platform type – TRUE RAG-AS-A-SERVICE with managed infrastructure API-first – REST API, Python SDK, OpenAI compatibility, MCP Server No-code option – 2-minute wizard deployment for non-developers Hybrid positioning – Serves both dev teams (APIs) and business users (no-code) Enterprise ready – SOC 2 Type II, GDPR, WCAG 2.0, flat-rate pricing
Advanced R A G ( Core Differentiator)
N/A	GraphRAG – Graph-based retrieval for relationship-aware knowledge extraction RAPTOR – Recursive abstractive processing for tree-organized retrieval Agentic workflows – Multi-step reasoning, tool use, code execution in sandbox Hybrid search – Full-text + vector + ML re-ranking combined ✅ 68K+ GitHub stars – Fastest-growing open-source RAG project (Octoverse 2024)	N/A

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Final Thoughts

Final Verdict: Deviniti vs RAGFlow

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

When to Choose Deviniti

You value strong compliance and security focus
Self-hosted solutions for data privacy
Domain expertise in regulated industries

Best For: Strong compliance and security focus

When to Choose 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

Migration & Switching Considerations

Switching between Deviniti and RAGFlow requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.

Pricing Comparison Summary

Deviniti starts at custom pricing, while RAGFlow begins at custom pricing. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.

Our Recommendation Process

Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality

For most organizations, the decision between Deviniti and RAGFlow 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: February 24, 2026 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.

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Priyansh Khodiyar

DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.

Deviniti vs RAGFlow