In this comprehensive guide, we compare Contextual AI and SimplyRetrieve 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 Contextual AI and SimplyRetrieve, 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 Contextual AI if: you value invented by the original creator of rag technology
Choose SimplyRetrieve if: you value completely free and open source
About Contextual AI
Contextual AI is rag 2.0 platform for enterprise-grade specialized ai agents. Contextual AI is an enterprise platform that pioneered RAG 2.0 technology, enabling organizations to build specialized RAG agents with exceptional accuracy for complex, knowledge-intensive workloads through end-to-end optimized systems. Founded in 2023, headquartered in Mountain View, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
91/100
Starting Price
Custom
About SimplyRetrieve
SimplyRetrieve is lightweight retrieval-centric generative ai platform. SimplyRetrieve is an open-source tool providing a fully localized, lightweight, and user-friendly GUI and API platform for Retrieval-Centric Generation (RCG). It emphasizes privacy and can run on a single GPU while maintaining clear separation between LLM context interpretation and knowledge memorization. Founded in 2019, headquartered in Tokyo, Japan, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
82/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, Contextual AI in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: RAG Platform 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
Contextual AI
SimplyRetrieve
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Easily brings in both unstructured files (PDFs, HTML, images, charts) and structured data (databases, spreadsheets) through ready-made connectors.
Does multimodal retrieval—turns images and charts into embeddings so everything is searchable together. Source
Hooks into popular SaaS tools like Slack, GitHub, and Google Drive for seamless data flow.
Uses a hands-on, file-based flow: drop PDFs, text, DOCX, PPTX, HTML, etc. into a folder and run a script to embed them.
A new GUI Knowledge-Base editor lets you add docs on the fly, but there’s no web crawler or auto-refresh yet.
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
Built for API integration first—no plug-and-play web widget included.
Enterprise-grade endpoints and a Snowflake Native App option make tight data integration straightforward. Source
Ships with a local Gradio GUI and Python scripts for queries—no out-of-the-box Slack or site widget.
Want other channels? Write a small wrapper that forwards messages to your local chatbot.
Embeds easily—a lightweight script or iframe drops the chat widget into any website or mobile app.
Offers ready-made hooks for Slack, Zendesk, Confluence, YouTube, Sharepoint, 100+ more.
Explore API Integrations
Connects with 5,000+ apps via Zapier and webhooks to automate your workflows.
Supports secure deployments with domain allowlisting and a ChatGPT Plugin for private use cases.
Hosted CustomGPT.ai offers hosted MCP Server with support for Claude Web, Claude Desktop, Cursor, ChatGPT, Windsurf, Trae, etc.
Read more here.
Create multiple datastores and link them to agents by role or permission for fine-grained access.
Tune the LLM on your own data, add guardrails, and embed custom logic as needed. Source
Lets you tweak everything—KnowledgeBase weight, retrieval params, system prompts—for deep control.
Encourages devs to swap embedding models or hack the pipeline code 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.
Pricing & Scalability
Usage-based pricing tailored for enterprises—cost scales with agent capacity, data size, and query load. Source
Standalone component APIs are priced per token, letting you mix and match pieces as you grow.
Free, MIT-licensed open source—no fees, but you supply the GPUs or cloud servers.
Scaling means spinning up more hardware and managing it yourself.
Runs on straightforward subscriptions: Standard (~$99/mo), Premium (~$449/mo), and customizable Enterprise plans.
Gives generous limits—Standard covers up to 60 million words per bot, Premium up to 300 million—all at flat monthly rates.
View Pricing
Handles scaling for you: the managed cloud infra auto-scales with demand, keeping things fast and available.
Security & Privacy
SOC 2 compliant with encryption in transit and at rest; deploy on-prem or in a VPC for full sovereignty. Source
Implements role-based permissions and query-time access checks to keep data secure.
Entirely local: all docs and chat data stay on your own machine—great for sensitive use cases.
No built-in auth or enterprise security—lock things down in your own deployment setup.
Protects data in transit with SSL/TLS and at rest with 256-bit AES encryption.
Holds SOC 2 Type II certification and complies with GDPR, so your data stays isolated and private.
Security Certifications
Offers fine-grained access controls—RBAC, two-factor auth, and SSO integration—so only the right people get in.
Observability & Monitoring
Built-in evaluation shows groundedness scores, retrieval metrics, and logs every step. Source
Plugs into external monitoring tools and supports detailed logging for audits and troubleshooting.
An “Analysis” tab shows which docs were pulled and how the query was built; logs print to the console.
No fancy dashboard—add your own logging or monitoring if you need broader stats.
Comes with a real-time analytics dashboard tracking query volumes, token usage, and indexing status.
Lets you export logs and metrics via API to plug into third-party monitoring or BI tools.
Analytics API
Provides detailed insights for troubleshooting and ongoing optimization.
Support & Ecosystem
High-touch enterprise support with solution engineers and technical account managers.
Grows its ecosystem via partnerships (e.g., Snowflake) and industry thought leadership. Source
Open-source on GitHub; support is community-driven via issues and lightweight docs.
Smaller ecosystem: you’re free to fork or extend, but there’s no paid SLA or enterprise help desk.
Supplies rich docs, tutorials, cookbooks, and FAQs to get you started fast.
Developer Docs
Offers quick email and in-app chat support—Premium and Enterprise plans add dedicated managers and faster SLAs.
Enterprise Solutions
Benefits from an active user community plus integrations through Zapier and GitHub resources.
Additional Considerations
Great for mission-critical apps that need multimodal retrieval and advanced reasoning.
Requires more up-front setup and technical know-how than no-code tools—best for teams with ML expertise.
Handles complex needs like role-based data access and evolving multimodal content. Source
Great for offline / on-prem labs where data never leaves the server—perfect for tinkering.
Takes more hands-on upkeep and won’t match proprietary giants in sheer capability out of the box.
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
Web console helps manage agents, but there's no drag-and-drop chatbot builder.
UI integration is a coding project—APIs are powerful, but non-tech users will need developer help.
Basic Gradio UI is developer-focused; non-tech users might find the settings overwhelming.
No slick, no-code admin—if you need polish or branding, you'll build your own front end.
Offers a wizard-style web dashboard so non-devs can upload content, brand the widget, and monitor performance.
Supports drag-and-drop uploads, visual theme editing, and in-browser chatbot testing.
User Experience Review
Uses role-based access so business users and devs can collaborate smoothly.
Competitive Positioning
Market position: Enterprise RAG 2.0 platform with proprietary Grounded Language Model (GLM) optimized for factual accuracy and multimodal retrieval capabilities
Target customers: Large enterprises and ML teams requiring mission-critical AI applications with advanced reasoning, multimodal content handling (images, charts), and strict accuracy requirements (88% factual accuracy benchmarked)
Key competitors: OpenAI Enterprise, Azure AI, Deepset, Vectara.ai, and custom-built RAG solutions using LangChain/Haystack
Competitive advantages: Proprietary GLM model with superior RAG performance, multimodal retrieval (images/charts), SOC 2 compliance with VPC/on-prem deployment options, Snowflake Native App integration, groundedness scoring with "Instant Viewer" for source attribution, and multi-hop retrieval with chain-of-thought reasoning
Pricing advantage: Usage-based enterprise pricing with standalone component APIs (reranker, generator) priced per token; flexible for organizations that want to mix and match components; best value for high-accuracy, high-volume use cases
Use case fit: Ideal for mission-critical enterprise applications requiring multimodal retrieval (technical documentation with diagrams), domain-specific AI agents with advanced reasoning, and organizations needing role-based data access with query-time permission checks
Market position: MIT-licensed open-source local RAG solution running entirely on-premises with open-source LLMs (no cloud dependency), designed for developers and tinkerers
Target customers: Developers experimenting with RAG locally, organizations with strict data isolation requirements (healthcare, government, defense), and teams wanting complete control without cloud costs or vendor dependencies
Key competitors: LangChain/LlamaIndex (frameworks), PrivateGPT, LocalGPT, and cloud RAG platforms for teams needing simplicity
Competitive advantages: Completely free and open-source (MIT license) with no fees or subscriptions, 100% local execution keeping all data on-premises, full control over model choice (any Hugging Face model), Python-based with full source code access for customization, "Retrieval Tuning Module" for transparency into answer generation, and zero external dependencies beyond local compute
Pricing advantage: Completely free with MIT license; only cost is GPU hardware or cloud compute; best value for teams with existing GPU infrastructure wanting to avoid subscription costs; requires technical expertise and hands-on maintenance
Use case fit: Ideal for offline/air-gapped environments requiring complete data isolation (defense, healthcare with strict PHI requirements), developers learning RAG internals and experimenting locally, and organizations with GPU infrastructure wanting zero cloud costs and complete control over LLM stack without vendor dependencies
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
Grounded Language Model (GLM): Proprietary model tuned specifically for RAG with ~88% factual accuracy on FACTS benchmark
Industry-Leading Groundedness: GLM achieves 88% vs. Gemini 2.0 Flash (84.6%), Claude 3.5 Sonnet (79.4%), GPT-4o (78.8%) on factuality benchmarks
Inline Attribution: Model provides citations showing exact source documents for each part of response as generated
Standalone APIs: Exposes separate reranker and generator APIs with simple token-based pricing for flexible integration
Model-Agnostic Option: Platform supports integration with other LLMs if needed for specific use cases
Optimized for RAG: GLM specifically designed for retrieval-augmented generation scenarios vs. general-purpose LLMs
Mission-Critical Applications: Applications where factual accuracy is paramount and hallucinations must be minimized
Multimodal Use Cases: Technical documentation with diagrams, charts in business documents, visual content requiring understanding
Domain-Specific AI Agents: Custom agents requiring advanced reasoning with access to structured and unstructured data
Role-Based Access: Organizations needing fine-grained data access control with query-time permission enforcement
Team Sizes: Large enterprises and ML teams with technical expertise for integration and deployment
Air-Gapped Environments: Defense, classified research, and secure facilities requiring complete offline operation without external connectivity
Healthcare PHI Compliance: HIPAA-regulated organizations needing 100% data isolation for protected health information
RAG Research & Education: Developers learning RAG internals with full visibility into retrieval and generation processes
Local Experimentation: Prototype RAG applications locally before committing to cloud infrastructure and subscription costs
Data Sovereignty: Organizations with strict data residency requirements preventing cloud storage or processing
Zero-Cost RAG: Teams with existing GPU infrastructure wanting to avoid subscription fees for RAG capabilities
Custom Model Development: Research teams fine-tuning and testing custom LLMs and embedding models for specific domains
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)
Compliance Flexibility: Can be configured to meet HIPAA, FedRAMP, GDPR, or other regulatory requirements through deployment architecture
Encryption: SSL/TLS for data in transit, 256-bit AES encryption for data at rest
SOC 2 Type II certification: Industry-leading security standards with regular third-party audits
Security Certifications
GDPR compliance: Full compliance with European data protection regulations, ensuring data privacy and user rights
Access controls: Role-based access control (RBAC), two-factor authentication (2FA), SSO integration for enterprise security
Data isolation: Customer data stays isolated and private - platform never trains on user data
Domain allowlisting: Ensures chatbot appears only on approved sites for security and brand protection
Secure deployments: ChatGPT Plugin support for private use cases with controlled access
Pricing & Plans
Free Tier: Credits for first 1M input and 1M output tokens to evaluate platform capabilities
Usage-Based Pricing: Enterprise pricing tailored by agent capacity, data size, and query load for scalability
Token-Based Components: Standalone component APIs (reranker, generator) priced per token for flexible mix-and-match
Enterprise Custom Pricing: Pricing details require sales engagement for production deployments and dedicated instances
Buy Additional Credits: Users can purchase credits as needs grow beyond free tier allocation
Best Value For: High-accuracy, high-volume enterprise use cases requiring multimodal retrieval and advanced reasoning
Completely Free: MIT open-source license with no subscription fees, API charges, or usage limits
Infrastructure Costs Only: GPU hardware or cloud compute (AWS/GCP/Azure GPU instances) are the only expenses
No Per-Query Charges: Unlimited queries without per-request pricing or rate limits
No Vendor Fees: Zero payments to SaaS providers or LLM API vendors (OpenAI, Anthropic, etc.)
GPU Requirements: Single GPU sufficient for development; scale hardware based on throughput needs
Open-Source Ecosystem: Leverage free Hugging Face models, FAISS library, and PyTorch without licensing costs
Best Value For: Teams with existing GPU infrastructure or ability to provision cloud GPU instances economically
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
High-Touch Enterprise Support: Solution engineers and technical account managers for dedicated customer success
API Documentation: Solid REST APIs and Python SDK documentation for managing agents, ingesting data, and querying
Endpoint Coverage: APIs for tuning, evaluation, standalone components with clear, token-based pricing transparency
Partnership Ecosystem: Grows via partnerships (Snowflake) and industry thought leadership for enterprise integration
Learning Resources: Technical documentation and integration guides for ML teams and developers
Response Times: Enterprise support includes dedicated resources for onboarding and technical assistance
GitHub Repository: Open-source at github.com/RCGAI/SimplyRetrieve with code, documentation, and examples
Research Paper: Academic publication on arXiv (2308.03983) explaining RCG approach and architecture
Community Support: GitHub Issues for bug reports, feature requests, and community troubleshooting
Lightweight Documentation: README and docs directory with setup instructions and usage examples
No Paid Support: Community-driven support only; no SLAs or enterprise help desk available
Code Examples: Example scripts and Jupyter notebooks demonstrating core functionality
Academic Background: Built on established libraries (Hugging Face, Gradio, PyTorch, FAISS) with extensive external documentation
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
Active community: User community plus 5,000+ app integrations through Zapier ecosystem
Regular updates: Platform stays current with ongoing GPT and retrieval improvements automatically
Limitations & Considerations
Technical Expertise Required: Best for teams with ML expertise - more up-front setup and technical know-how than no-code tools
NO Drag-and-Drop Builder: Web console helps manage agents, but no drag-and-drop chatbot builder for non-technical users
UI Integration is Coding Project: APIs are powerful, but non-tech users will need developer help for implementation
Learning Curve: Platform requires understanding of RAG concepts, embeddings, and AI agent architecture
NO Pre-Built UI: No out-of-the-box UI builder; customers embed in their own branded front end
API-First Platform: Built for API integration first - no plug-and-play web widget included
Enterprise Focus: Pricing and features target large enterprises vs. SMBs or individual developers
NOT Ideal For: Small teams without ML/AI expertise, organizations wanting no-code deployment, businesses needing immediate plug-and-play solutions
Developer-Only Tool: Requires Python expertise, GPU knowledge, and technical setup—not suitable for non-technical users
GPU Infrastructure Required: Needs dedicated GPU hardware or cloud GPU instances with associated costs and management overhead
Basic UI: Gradio interface is functional but not polished—requires custom front-end development for production use
Limited Scalability: Scaling requires manual infrastructure management and load balancing vs auto-scaling cloud platforms
No Enterprise Features: Missing multi-tenancy, user management, advanced analytics, and production-grade monitoring
Slower Inference: Open-source models on single GPU (few to 10+ seconds per reply) vs sub-second cloud API responses
Manual Knowledge Base Updates: No automatic web crawling, syncing, or scheduled reindexing capabilities
No Pre-Built Integrations: Requires custom development to integrate with Slack, websites, or support platforms
Limited Context Memory: Primarily single-turn Q&A with minimal conversation history retention
Maintenance Burden: User responsible for updates, model management, troubleshooting, and infrastructure maintenance
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-4, GPT-3.5) and Anthropic (Claude) - 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
RAG 2.0 Agents: Specialized RAG agents for expert knowledge work with advanced contextual understanding and multi-hop retrieval capabilities
Multi-Hop Retrieval: Advanced RAG agents execute multi-hop retrieval and chain-of-thought reasoning for tough, complex questions
Task-Oriented Assistants: Domain-specific AI agents designed for mission-critical applications requiring high accuracy and minimal hallucinations
Multiple Datastore Support: Create multiple datastores and link them to agents by role or permission for fine-grained access control
Custom Logic Integration: Tune LLM on your own data, add guardrails, and embed custom logic as needed for specialized workflows
Agent APIs: Programmatic agent creation, management, and querying through comprehensive REST APIs and Python SDK
Grounded Generation: Inline citations showing exact document spans that informed each response part with built-in hallucination reduction
Document-Level Security: Enterprise controls for access permissions on sensitive data with query-time access validation
Platform Generally Available (January 2025): Helping enterprises build specialized RAG agents to support expert knowledge work
State-of-the-Art Performance: Each component achieves state-of-the-art benchmarks on BIRD (structured reasoning), RAG-QA Arena (end-to-end RAG), OmniDocBench (document understanding)
Retrieval-Centric Generation (RCG): Research-backed approach separating LLM reasoning capabilities from knowledge memorization—more efficient than traditional RAG architectures
Retrieval Tuning Module: Developer-focused transparency layer showing which documents were retrieved, how queries were constructed, and how answers were generated
Knowledge Base Mixing (MoKB): Route queries across multiple selectable knowledge bases with intelligent source selection and weighting
Explicit Prompt Weighting (EPW): Fine-grained control over retrieved knowledge base influence in final answer generation
Single-Turn Q&A Focus: Primarily designed for single-turn question answering—limited multi-turn conversation and context memory
Analysis Tab Transparency: Visual debugging interface showing document retrieval process and query construction for answer inspection
Local Agent Execution: All agent processing happens on-premises with zero external API calls—complete control over agent behavior and data
LIMITATION - No Chatbot UI: Gradio interface for developers only—no polished conversational interface for end users or production deployment
LIMITATION - No Lead Capture: No built-in lead generation, email collection, or CRM integration capabilities—manual implementation required
LIMITATION - No Human Handoff: No escalation workflows, live agent transfer, or fallback mechanisms for complex queries—developer must build these features
LIMITATION - No Multi-Channel Support: No native integrations with Slack, Teams, WhatsApp, or website widgets—requires custom wrapper development
LIMITATION - No Session Management: Stateless interactions without conversation history tracking or multi-turn context retention
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
Platform Type: TRUE ENTERPRISE RAG 2.0 PLATFORM - Proprietary Grounded Language Model (GLM) optimized for factual accuracy and multimodal retrieval
RAG 2.0 Architecture: Advanced approach tops industry benchmarks for document understanding and factuality with multi-hop retrieval (announced general availability January 2025)
Proprietary GLM Model: ~88% factual accuracy on FACTS benchmark outperforming Gemini 2.0 Flash (84.6%), Claude 3.5 Sonnet (79.4%), GPT-4o (78.8%)
Built-in Evaluation Tools: Assess generated responses for equivalence and groundedness with comprehensive evaluation across every critical component
Multimodal Retrieval: Turns images and charts into embeddings for unified search across text and visual content in technical documentation
Groundedness Scoring: Built-in scoring with "Instant Viewer" highlighting exact source text backing each answer part for transparency
Reranker + Scoring: Uses reranker plus groundedness scoring for factual answers with precise attribution and hallucination reduction
Handles Noisy Datasets: Robust reranking and retrieval for large, noisy datasets with multiple datastores by role or permission
Production-Grade Accuracy: Delivers production-grade accuracy for specialized knowledge tasks with enterprise security, audit trails, high availability, scalability, compliance
Joint Tuning Capability: Retrieval and generation components can be jointly tuned by providing sample queries, gold-standard responses, supporting evidence
Comprehensive Assessment: Measures end-to-end RAG performance, multi-modal document understanding, structured data retrieval, and grounded language generation
Target Market: Large enterprises and ML teams requiring mission-critical AI applications with advanced reasoning and strict accuracy requirements
Use Case Fit: Ideal for mission-critical enterprise applications requiring multimodal retrieval, domain-specific AI agents, and role-based data access with query-time permission checks
Platform Type: NOT A RAG-AS-A-SERVICE PLATFORM - Open-source academic research project for local Retrieval-Centric Generation experimentation and learning
Core Mission: Provide localized, lightweight, user-friendly interface to Retrieval-Centric Generation (RCG) approach for machine learning community exploration and research
Academic Foundation: Published research tool from RCGAI with arXiv paper (2308.03983) explaining RCG methodology and architectural design decisions
Target Market: Researchers, developers, and organizations experimenting with RAG locally without cloud dependencies—NOT commercial service users
Self-Hosted Infrastructure: MIT-licensed tool requiring user-managed GPU hardware or cloud compute—no managed infrastructure, APIs, or service-level agreements
Developer-First Design: Python-based with Gradio GUI and script execution—intended for technical users comfortable with GPU infrastructure and model management
RAG Implementation: Retrieval-Centric Generation (RCG) philosophy emphasizing retrieval over memorization—FAISS vector search with open-source LLMs (WizardVicuna-13B default, any Hugging Face model supported)
API Availability: NO formal REST API or SDKs—interaction via Python scripts and local Gradio interface requiring subprocess calls or custom wrappers
Data Privacy Advantage: 100% local execution with zero external transmission—ideal for classified, PHI, PII, or confidential data requiring air-gapped processing
Pricing Model: Completely free (MIT license) with no subscription fees—only cost is GPU hardware or cloud compute infrastructure
Support Model: Community-driven GitHub Issues and lightweight documentation—no paid support, SLAs, or customer success teams
LIMITATION vs Managed Services: NO managed infrastructure, automatic scaling, production-grade monitoring, enterprise security controls, or commercial support—users responsible for all operational aspects
LIMITATION - No Service Features: NO authentication systems, multi-tenancy, user management, analytics dashboards, or SaaS conveniences—pure research/development tool
Comparison Validity: Architectural comparison to commercial RAG-as-a-Service platforms like CustomGPT.ai is MISLEADING—SimplyRetrieve is open-source research tool for on-premises experimentation, not production service
Use Case Fit: Perfect for offline/air-gapped RAG research, developers learning RAG internals with full transparency, organizations with strict data isolation requirements (defense, healthcare PHI compliance), and teams wanting zero cloud costs with existing GPU infrastructure
Core Architecture: Serverless RAG infrastructure with automatic embedding generation, vector search optimization, and LLM orchestration fully managed behind API endpoints
API-First Design: Comprehensive REST API with well-documented endpoints for creating agents, managing projects, ingesting data (1,400+ formats), and querying chat
API Documentation
Developer Experience: Open-source Python SDK (customgpt-client), Postman collections, OpenAI API endpoint compatibility, and extensive cookbooks for rapid integration
No-Code Alternative: Wizard-style web dashboard enables non-developers to upload content, brand widgets, and deploy chatbots without touching code
Hybrid Target Market: Serves both developer teams wanting robust APIs AND business users seeking no-code RAG deployment - unique positioning vs pure API platforms (Cohere) or pure no-code tools (Jotform)
RAG Technology Leadership: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, proprietary anti-hallucination mechanisms, and citation-backed responses
Benchmark Details
Deployment Flexibility: Cloud-hosted SaaS with auto-scaling, API integrations, embedded chat widgets, ChatGPT Plugin support, and hosted MCP Server for Claude/Cursor/ChatGPT
Enterprise Readiness: SOC 2 Type II + GDPR compliance, full white-labeling, domain allowlisting, RBAC with 2FA/SSO, and flat-rate pricing without per-query charges
Use Case Fit: Ideal for organizations needing both rapid no-code deployment AND robust API capabilities, teams handling diverse content types (1,400+ formats, multimedia transcription), and businesses requiring production-ready RAG without building ML infrastructure from scratch
Competitive Positioning: Bridges the gap between developer-first platforms (Cohere, Deepset) requiring heavy coding and no-code chatbot builders (Jotform, Kommunicate) lacking API depth - offers best of both worlds
After analyzing features, pricing, performance, and user feedback, both Contextual AI and SimplyRetrieve are capable platforms that serve different market segments and use cases effectively.
When to Choose Contextual AI
You value invented by the original creator of rag technology
Best-in-class accuracy on RAG benchmarks
End-to-end optimized system vs cobbled together solutions
Best For: Invented by the original creator of RAG technology
When to Choose SimplyRetrieve
You value completely free and open source
Strong privacy focus - fully localized
Lightweight - runs on single GPU
Best For: Completely free and open source
Migration & Switching Considerations
Switching between Contextual AI and SimplyRetrieve 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
Contextual AI starts at custom pricing, while SimplyRetrieve 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 Contextual AI and SimplyRetrieve 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 7, 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.
People Also Compare
Explore more AI tool comparisons to find the perfect solution for your needs
Join the Discussion
Loading comments...