In this comprehensive guide, we compare Coveo 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 Coveo 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 Coveo if: you value comprehensive enterprise search capabilities
Choose SimplyRetrieve if: you value completely free and open source
About Coveo
Coveo is ai-powered search and personalization for digital experiences. Coveo is an enterprise AI platform that delivers intelligent search, recommendations, and personalization across commerce, customer service, workplace, and website applications using machine learning and behavioral analytics. Founded in 2005, headquartered in Quebec City, Canada, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
82/100
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, both platforms score similarly in overall satisfaction. From a cost perspective, pricing is comparable. The platforms also differ in their primary focus: Enterprise Search 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
Coveo
SimplyRetrieve
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Pulls content from a long list of enterprise sources—SharePoint, Salesforce, ServiceNow, Confluence, databases, file shares, Slack, websites—and merges it all into one index with native connectors.
Runs OCR and handles structured data, so it can index scanned docs, intranet pages, knowledge articles, and even multimedia.
Keeps the index fresh with incremental crawls, push APIs, and scheduled syncs—new or updated content shows up fast.
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
Ships Atomic UI components you can drop into search pages, support hubs, or commerce sites to surface generative answers.
Connects natively to platforms like Salesforce and Sitecore, letting AI answers appear right inside tools your team already uses.
Need a custom channel? Its robust REST APIs let you build bespoke chatbots or virtual assistants on top of Coveo’s retrieval engine.
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.
Fine-tune which sources and metadata the engine uses via query pipelines and filters.
Integrates with SSO/LDAP so results are tailored to each user’s permissions.
Developers can tweak prompt templates or inject business rules to shape the output.
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
Sold under enterprise licenses—pricing depends on sources, query volume, and feature set.
Scales to millions of queries with 99.999 % uptime and regional data-center options.
Usually involves annual contracts with volume tiers and optional premium support.
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
Holds ISO 27001/27018 and SOC 2 certifications, plus HIPAA-compatible deployments.
Granular access controls ensure users only see what they’re authorized to view.
Can run in private cloud or on-prem for organizations with strict data-residency needs.
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 analytics dashboard tracks query volume, engagement, and generative-answer performance.
Detailed pipeline logs can be exported for deeper analysis.
Supports A/B testing in the query pipeline to measure impact and fine-tune relevance.
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
Comes with enterprise-grade support—account managers, 24/7 help, and extensive training programs.
Large partner network and the Coveo Connect community provide docs, forums, and certified integrations.
Regular product updates and industry events keep you ahead of the curve.
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
Coveo goes beyond Q&A to power search, recommendations, and discovery for large digital experiences.
Deep integration with enterprise systems and strong permissioning make it ideal for internal knowledge management.
Powerful but best suited for organizations with an established IT team to tune and maintain it.
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
Admin console and Atomic components let you get started with minimal code.
The end-user search UI is polished, but full generative setup usually calls for developer involvement.
Great for teams that already have technical resources or use Coveo today; more complex than a pure no-code tool.
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-grade AI-powered search and discovery platform with Relevance Generative Answering (RGA) capabilities for large-scale knowledge management
Target customers: Large enterprises with complex content ecosystems (SharePoint, Salesforce, ServiceNow, Confluence), organizations needing permission-aware search, and companies requiring search + recommendations + discovery beyond simple Q&A
Key competitors: Azure AI Search, Vectara.ai, Glean, Elastic Enterprise Search, and custom Elasticsearch/OpenSearch implementations
Competitive advantages: Mature enterprise connectors to 100+ sources with incremental crawling, hybrid search (keyword + semantic) with semantic ranking, permission-aware results respecting user access controls, Atomic UI components for rapid deployment, native integrations with Salesforce/Sitecore, and 99.999% uptime SLA with regional data centers
Pricing advantage: Enterprise licensing with annual contracts typically higher than SaaS chatbot tools but competitive for comprehensive search + RAG + recommendations platform; best value for organizations needing unified search across massive content sets with millions of queries
Use case fit: Best for enterprises managing large, distributed content across multiple systems (SharePoint, databases, file shares), organizations requiring permission-aware search that respects existing access controls, and companies wanting to power internal knowledge hubs, support portals, and commerce sites with generative answers
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
Azure OpenAI GPT Models: Runs primarily on OpenAI GPT models via Azure OpenAI delivering high-quality text generation
Model Flexibility: Relevance-Augmented Passage Retrieval API lets customers plug in their own preferred LLM
Behind-the-Scenes Tuning: Handles model tuning and prompt optimization automatically without customer intervention
API Override Option: Advanced users can override default model configuration via API when needed for specific use cases
Integration with Search: LLM generation tightly integrated with Coveo's keyword + semantic search pipeline for context quality
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
Enterprise Licensing: Sold under enterprise licenses with pricing from $600 to $1,320 depending on configuration
Pro Plan: Entry-level plan with core search and RGA features for smaller enterprise deployments
Enterprise Plan: Full-featured plan with advanced capabilities, higher query volumes, and premium support
Pricing Factors: Based on number of sources, query volume per month, feature set, and integrations selected
Annual Contracts: Usually involves annual contracts with volume tiers and optional premium support packages
Consumption-Based: Consumption-based pricing model can make costs hard to predict for enterprise-scale implementations
Multiple Sites: Can power multiple sites with one Coveo license as long as they're similar use cases
Flexible Usage: Never automatically restricts service; work with customer manager to review and determine right usage level
Best Value For: Organizations needing unified search across massive content sets with millions of queries beyond simple chatbot tools
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
Enterprise-Grade Support: Account managers, 24/7 help, and extensive training programs for successful deployment
Large Partner Network: Certified integrations and implementation partners through Coveo Connect community
Documentation: Enterprise-grade docs with step-by-step guides for pipelines, index management, connector configuration
Forums and Community: Coveo Connect community provides docs, forums for peer support and knowledge sharing
Regular Updates: Regular product updates and industry events keep customers ahead of search and AI trends
Training Programs: Extensive training programs for admin console, Atomic components, and developer integration
Response Times: 24/7 enterprise support with guaranteed response times for critical issues
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
Developer Involvement Required: Full generative setup usually calls for developer involvement despite admin console
Cost Predictability: Consumption-based pricing model makes it hard to predict costs - especially for enterprise-scale implementations
Technical Team Needed: Best suited for organizations with established IT team to tune and maintain platform
More Complex Than No-Code: More complex than pure no-code chatbot tools requiring technical resources
Enterprise Focus: Powerful platform but optimized for enterprises vs. SMBs or startups
Learning Curve: Admin console and Atomic components require learning despite being developer-friendly
NOT Ideal For: Small businesses without IT resources, organizations wanting simple plug-and-play chatbot solutions, teams needing immediate deployment without technical configuration
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-5.1 and 4 series) and Anthropic (Claude, opus and sonnet 4.5) - no support for other LLM providers (Cohere, AI21, open-source models)
Pricing at scale: Flat-rate pricing may become expensive for very high-volume use cases (millions of queries/month) compared to pay-per-use models
Customization limits: While highly configurable, some advanced RAG techniques (custom reranking, hybrid search strategies) may not be exposed
Language support: Supports 90+ languages but performance may vary for less common languages or specialized domains
Real-time data: Knowledge bases require re-indexing for updates - not ideal for real-time data requirements (stock prices, live inventory)
Enterprise features: Some advanced features (custom SSO, token authentication) only available on Enterprise plan with custom pricing
Core Agent Features
Agentic AI Integration (2024-2025): Brings essential relevance to GenAI and Agentic AI with Coveo for Agentforce, expanded API suite, Agentic AI Design Partner Program
Relevance-Augmented Retrieval: Advanced hybrid retrieval and AI ranking vs basic vector databases - enterprises need this for AI, GenAI, and Agentic AI projects
API Suite for Agents: Search API (retrieve document links), Passage Retrieval API (grounding agents in contextually relevant enterprise information), Answer API (direct answers from Coveo RGA)
Coveo for Agentforce: Native integration with Salesforce Agentforce for customer service, sales, marketing agents with enterprise search capabilities
AWS Agentic AI Services: RAG-as-a-Service for AWS through Coveo-hosted MCP Server (December 2024) for Amazon Bedrock AgentCore, Amazon Bedrock Agents, Amazon Quick Suite
Four Configurable Tools: Passage Retrieval (grounding LLM prompts), Answer generation (powered by Amazon Nova), Search (ranked results), Fetch (complete document text for complex reasoning)
Security-First Design: Inherits document-level and item-level permissions automatically delivering trusted, secure, accurate answers grounded in all enterprise knowledge
Answer Optimization: Ground agents and optimize answers with retrieval steering, reasoning effort, and answer synthesis capabilities
Query Planning: Leverage knowledge bases and AI models for retrieval steering, query planning and decomposition, reranking, and answer synthesis
Early Access Program: Invitation-only early access for developers wanting to accelerate GenAI or AI Agents projects (December 2024)
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: ENTERPRISE SEARCH PLATFORM WITH RAG-AS-A-SERVICE - AI-powered search and discovery with Relevance Generative Answering capabilities
RAG-as-a-Service Launch: Announced Retrieval Augmented Generation (RAG)-as-a-Service for AWS agentic AI services December 1, 2024 as cloud-native offering
Relevance-Augmented Retrieval: Coveo's approach emphasizing need to rapidly pinpoint contextually relevant insights from vast amounts of structured and unstructured data
40% Accuracy Improvement: Studies demonstrate RAG can increase base model accuracy by 40% according to industry analysis
Hybrid Search Foundation: Combines keyword (full-text), vector, and hybrid search with sophisticated relevance tuning for improved retrieval performance
Relevance Generative Answering (RGA): Two-step retrieval plus LLM flow producing concise, source-cited answers grounded in enterprise content
Permission-Aware Retrieval: Respects permissions showing each user only content they're authorized to see with SSO/LDAP integration
Incremental Crawls: Keeps index fresh with incremental crawls, push APIs, scheduled syncs - new or updated content shows up fast
Reranking + Smart Prompts: Reranking plus smart prompts keep hallucinations low and citations precise for enterprise reliability
Scalable Architecture: Built on scalable architecture handling heavy query loads and massive content sets with 99.999% uptime
MCP Server Integration: Coveo-hosted MCP Server designed to bring more precision, security, and scalability to enterprise generative AI
Enterprise Assessment Focus: Typically adopted by organizations seeking to unify content and improve digital interactions with comprehensive search and RAG infrastructure
Best For: Enterprises managing large, distributed content across multiple systems requiring permission-aware search, unified knowledge hubs, and generative answers
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 Coveo and SimplyRetrieve are capable platforms that serve different market segments and use cases effectively.
When to Choose Coveo
You value comprehensive enterprise search capabilities
Strong e-commerce and B2B features
Deep Salesforce integration
Best For: Comprehensive enterprise search capabilities
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 Coveo 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
Coveo 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 Coveo 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 9, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
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DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
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