In this comprehensive guide, we compare SimplyRetrieve and Stonly across various parameters including features, pricing, performance, and customer support to help you make the best decision for your business needs.
Overview
When choosing between SimplyRetrieve and Stonly, understanding their unique strengths and architectural differences is crucial for making an informed decision. Both platforms serve the RAG (Retrieval-Augmented Generation) space but cater to different use cases and organizational needs.
Quick Decision Guide
Choose SimplyRetrieve if: you value completely free and open source
Choose Stonly if: you value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
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
About Stonly
Stonly is interactive knowledge base platform with enterprise ai-powered answers. Stonly is a customer support knowledge management platform with embedded AI capabilities focused on interactive step-by-step guides and help desk agent assistance. Its AI Answers feature (Enterprise-only add-on) achieves 71% self-serve success rates, but it's fundamentally a knowledge base platform with AI features—not a pure RAG-as-a-Service solution. Founded in 2017, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$249/mo
Key Differences at a Glance
In terms of user ratings, Stonly in overall satisfaction. From a cost perspective, SimplyRetrieve starts at a lower price point. The platforms also differ in their primary focus: RAG Platform versus Knowledge Management. These differences make each platform better suited for specific use cases and organizational requirements.
⚠️ What This Comparison Covers
We'll analyze features, pricing, performance benchmarks, security compliance, integration capabilities, and real-world use cases to help you determine which platform best fits your organization's needs. All data is independently verified from official documentation and third-party review platforms.
Detailed Feature Comparison
SimplyRetrieve
Stonly
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
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.
PDF uploads confirmed
Public website crawling: Pages not requiring authentication
Zendesk help center content indexing
Proprietary interactive guide format as primary content model
Note: No Google Drive, Dropbox, Notion, or SharePoint integrations for data ingestion
Note: No YouTube transcript extraction (videos can be embedded but not processed)
Note: No direct Word document (.docx) or HTML file imports confirmed
Note: No automatic content syncing from external sources - updates are manual through Stonly's visual editor
Content limits by tier: Basic (5 guides, 400 views/mo), Small Business (unlimited guides, 4K views/mo), Enterprise (custom)
Content versioning: Side-by-side comparison and instant restore on Business and Enterprise plans
Lets you ingest more than 1,400 file formats—PDF, DOCX, TXT, Markdown, HTML, and many more—via simple drag-and-drop or API.
Crawls entire sites through sitemaps and URLs, automatically indexing public help-desk articles, FAQs, and docs.
Turns multimedia into text on the fly: YouTube videos, podcasts, and other media are auto-transcribed with built-in OCR and speech-to-text.
View Transcription Guide
Connects to Google Drive, SharePoint, Notion, Confluence, HubSpot, and more through API connectors or Zapier.
See Zapier Connectors
Supports both manual uploads and auto-sync retraining, so your knowledge base always stays up to date.
Integrations & Channels
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.
Deep help desk integrations: Zendesk, Salesforce Service Cloud, Freshdesk, ServiceNow
Zendesk features: Update tickets from guides, preserve guide progress in tickets, launch Zendesk Chat from widget
Zapier integration: Webhook triggers for form submissions and guide completions
Analytics integrations: Segment, Google Analytics
Embedding options: JavaScript widget, iframe, API deployment
Note: No native Slack, WhatsApp, Telegram, or Microsoft Teams integrations (confirmed by multiple user reviews)
Note: No omnichannel messaging support
Website embedding: All plans support JS widget and iframe embedding
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.
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.
CSS and HTML customization: Change layout and look of knowledge base with custom code capabilities
Intuitive customization tools: Easy-to-use tools that don't require code for basic customization
Layout customization: Decide how content is structured and presented with flexible options
Design controls: Manage visual components like colors, logo, or cover image for brand alignment
Personalized content: Use customer data to show personalized content from knowledge base for targeted experiences
Data-driven personalization: Customers see what they need right away when first accessing knowledge base
Analytics insights: Guide usage analytics provide insight into customer behavior for continuous improvement
Highly flexible platform: Users appreciate ability to use Stonly for knowledge bases and guided tours with target properties based on specific user needs
Rich media support: Add images, GIFs, videos, and annotations to bring knowledge base content to life
Third-party scripts: Install scripts from other tools like Google Analytics for extended functionality
Lets you add, remove, or tweak content on the fly—automatic re-indexing keeps everything current.
Shapes agent behavior through system prompts and sample Q&A, ensuring a consistent voice and focus.
Learn How to Update Sources
Supports multiple agents per account, so different teams can have their own bots.
Balances hands-on control with smart defaults—no deep ML expertise required to get tailored behavior.
Pricing & Scalability
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.
Basic (Free): 5 guides, 400 views/month, 1 seat, single language
Small Business ($249/mo, $199/mo annual): Unlimited guides, 4,000 views/month, 5 seats, 3 knowledge bases, CSS customization, Zapier, NPS surveys
Enterprise (Custom, ~$39K/year avg): Custom views, unlimited seats, AI Answers add-on, Mobile SDKs, SAML SSO, white-label, auto-translation, CSAT/CES surveys
Quick onboarding: Users report creating guides in under 30 minutes
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 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.
Limited UI customization: Limited ability to customize user interface and workflows to match specific brand requirements is primary user concern
Basic collaboration tools: Without real-time editing or advanced team management features can hinder team productivity when multiple people need to work together
No offline access: Guides unavailable without internet connectivity reducing usability in areas with unreliable internet
Performance degradation: Can degrade with very large or complex guides causing slower responsiveness indicating scalability concerns
Restricted language options: Limit efficient creation of multilingual content which may be barrier for global organizations
Mixed media support missing: Users find missing features wishing for mixed media support and enhanced reporting tools
Step ordering difficulties: Users report limitations in feature usability and difficulties with step ordering though support offers helpful workarounds
Requires coding knowledge: Unlike most competitors, doesn't advertise as no-code platform - need coding knowledge to track events, target users, stream data, and style content
Image workflow limitations: Inability to use images in base offering limits utility in some workflows with some advanced features requiring extra costs
View-based pricing: Charges additional fees based on guide views - customers exceeding 4,000 guide views/month pay extra $250-500 monthly depending on volume
Integration reliability: Users find lack of integrations limits ability to fully connect Stonly with other tools - Stonly/Zendesk integration isn't as reliable as desired (stops working every few weeks)
Slashes engineering overhead with an all-in-one RAG platform—no in-house ML team required.
Gets you to value quickly: launch a functional AI assistant in minutes.
Stays current with ongoing GPT and retrieval improvements, so you’re always on the latest tech.
Balances top-tier accuracy with ease of use, perfect for customer-facing or internal knowledge projects.
No- Code Interface & Usability
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.
4.8/5 ease of use rating on G2
"Ease of use" mentioned 32 times in G2 reviews
Visual drag-and-drop editor requires no coding
Small learning curve - non-technical teams productive quickly
Guide creation in under 30 minutes reported by users
Pre-built templates for common scenarios
Intuitive interface for support teams
Note: Some navigation confusion reported in admin interface
Note: Cannot edit on mobile devices
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: 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
Unique strength: Interactive guide format for structured support content
vs CustomGPT: Not comparable - different product categories (knowledge base vs RAG-as-a-Service)
vs Zendesk: Lighter-weight alternative focused on self-service guides vs full customer service platform
vs traditional chatbots: Interactive guides provide structured paths vs free-form conversation
Target audience: Support teams using Zendesk/Salesforce, not developers building RAG applications
70-76% ticket reduction documented in case studies
71% self-serve success rate with AI Answers
Enterprise compliance suitable for regulated industries
Market position: Leading all-in-one RAG platform balancing enterprise-grade accuracy with developer-friendly APIs and no-code usability for rapid deployment
Target customers: Mid-market to enterprise organizations needing production-ready AI assistants, development teams wanting robust APIs without building RAG infrastructure, and businesses requiring 1,400+ file format support with auto-transcription (YouTube, podcasts)
Key competitors: OpenAI Assistants API, Botsonic, Chatbase.co, Azure AI, and custom RAG implementations using LangChain
Competitive advantages: Industry-leading answer accuracy (median 5/5 benchmarked), 1,400+ file format support with auto-transcription, SOC 2 Type II + GDPR compliance, full white-labeling included, OpenAI API endpoint compatibility, hosted MCP Server support (Claude, Cursor, ChatGPT), generous data limits (60M words Standard, 300M Premium), and flat monthly pricing without per-query charges
Pricing advantage: Transparent flat-rate pricing at $99/month (Standard) and $449/month (Premium) with generous included limits; no hidden costs for API access, branding removal, or basic features; best value for teams needing both no-code dashboard and developer APIs in one platform
Use case fit: Ideal for businesses needing both rapid no-code deployment and robust API capabilities, organizations handling diverse content types (1,400+ formats, multimedia transcription), teams requiring white-label chatbots with source citations for customer-facing or internal knowledge projects, and companies wanting all-in-one RAG without managing ML infrastructure
Hugging Face Compatibility: Swap in any Hugging Face model with sufficient GPU resources (Llama 2, Falcon, Mistral, etc.)
Full Local Control: Models run entirely on-premises with no external API calls or cloud dependencies
Embedding Model: Default multilingual-e5-base for retrieval with option to swap for other embedding models
Model Customization: Fine-tune or quantize models for specific use cases and hardware constraints
No Vendor Lock-In: Complete flexibility to use any open-source LLM without subscription fees or API limits
GPU Requirements: Smaller models may not match GPT-4 depth but enable complete data isolation and zero operational costs
Undisclosed Proprietary LLM: Stonly does not publicly disclose the specific model powering AI Answers feature
No Model Selection: Users cannot choose between GPT-3.5, GPT-4, Claude, Gemini, or other LLM providers
No Temperature Controls: No user-facing controls for adjusting response creativity, randomness, or formatting
No Fine-Tuning or Model Routing: Cannot customize model behavior beyond predefined AI Profiles and Custom Instructions
AI Profiles (Up to 20): Define tone, boundaries, and behavior for different use cases or audiences
Custom Instructions (Up to 100): Set specific rules and style guidelines for AI response generation
Guided AI Answers: Predefined responses for specific questions bypassing AI generation for sensitive scenarios
Automatic Fallback: Low-confidence scenarios trigger fallback to ML-powered search rather than forcing unreliable AI answer
Knowledge-Grounded Approach: AI responses anchored in Stonly guides, external websites, and PDFs to reduce hallucinations
Primary models: GPT-5.1 and 4 series from OpenAI, and Anthropic's Claude 4.5 (opus and sonnet) for enterprise needs
Automatic model selection: Balances cost and performance by automatically selecting the appropriate model for each request
Model Selection Details
Proprietary optimizations: Custom prompt engineering and retrieval enhancements for high-quality, citation-backed answers
Managed infrastructure: All model management handled behind the scenes - no API keys or fine-tuning required from users
Anti-hallucination technology: Advanced mechanisms ensure chatbot only answers based on provided content, improving trust and factual accuracy
R A G Capabilities
Retrieval-Centric Generation (RCG): Research-backed approach explicitly separating LLM roles from knowledge memorization for more efficient implementation
Retrieval Tuning Module: Transparency into answer generation showing which documents were retrieved and how queries were built
Mixtures-of-Knowledge-Bases (MoKB): Multiple selectable knowledge bases with intelligent routing between knowledge sources
Explicit Prompt-Weighting (EPW): Control over retrieved knowledge base weighting in final answer generation
FAISS Vector Search: Fast approximate nearest neighbor search using Facebook's FAISS library for efficient retrieval
On-the-Fly Knowledge Base Creation: Drag-and-drop documents in GUI to create knowledge bases without manual preprocessing
Analysis Tab: Visual debugging showing document retrieval process and query construction for transparency
Multiple Document Support: Handles PDFs, text files, DOCX, PPTX, HTML, and other common formats
AI Answers (Enterprise Add-On): Generative AI responses grounded in Stonly guides, external websites, and selected PDFs
Knowledge-Grounding: Responses anchored to structured content (interactive guides, decision trees, checklists) reducing hallucinations vs generic chatbots
Confidence-Based Fallback: Automatic switch to ML-powered search when AI confidence is low preventing unreliable answers
Multi-Source Ingestion: PDF uploads, public website crawling, Zendesk help center content indexing
Interactive Guide Format: Proprietary content model combining structured workflows with AI-generated answers
Limited Data Sources: No Google Drive, Dropbox, Notion, SharePoint, or YouTube transcript extraction
Manual Content Updates: Updates through Stonly's visual editor—no automatic syncing from external sources
71% Self-Serve Success Rate: Documented effectiveness of AI Answers in reducing support escalations
Hallucination Controls: Strong grounding in structured content vs open-ended conversational AI
Core architecture: GPT-4 combined with Retrieval-Augmented Generation (RAG) technology, outperforming OpenAI in RAG benchmarks
RAG Performance
Anti-hallucination technology: Advanced mechanisms reduce hallucinations and ensure responses are grounded in provided content
Benchmark Details
Automatic citations: Each response includes clickable citations pointing to original source documents for transparency and verification
Optimized pipeline: Efficient vector search, smart chunking, and caching for sub-second reply times
Scalability: Maintains speed and accuracy for massive knowledge bases with tens of millions of words
Context-aware conversations: Multi-turn conversations with persistent history and comprehensive conversation management
Source verification: Always cites sources so users can verify facts on the spot
Use Cases
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 Ticket Deflection: 70-76% ticket reduction through interactive self-service guides and AI Answers
Help Desk Integration: Deep Zendesk, Salesforce Service Cloud, Freshdesk, ServiceNow integration for unified support workflows
Interactive Onboarding: Step-by-step guides, decision trees, and checklists for product onboarding and user education
Knowledge Base Enhancement: Augment traditional help centers with interactive guides and AI-powered search
Agent Assistance: Provide support agents with guided workflows and AI answers during live interactions
Multi-Language Support: Auto-translation on Enterprise plan for global support teams and multilingual customers
Complex Troubleshooting: Decision tree logic guides users through multi-step troubleshooting processes
Compliance & Training: Structured guides ensuring consistent information delivery for regulated industries
Customer support automation: AI assistants handling common queries, reducing support ticket volume, providing 24/7 instant responses with source citations
Internal knowledge management: Employee self-service for HR policies, technical documentation, onboarding materials, company procedures across 1,400+ file formats
Sales enablement: Product information chatbots, lead qualification, customer education with white-labeled widgets on websites and apps
Documentation assistance: Technical docs, help centers, FAQs with automatic website crawling and sitemap indexing
Educational platforms: Course materials, research assistance, student support with multimedia content (YouTube transcriptions, podcasts)
Healthcare information: Patient education, medical knowledge bases (SOC 2 Type II compliant for sensitive data)
Automatic Tier Upgrades: Exceeding limits for 2 consecutive months triggers automatic upgrade and billing adjustment
Enterprise-Gated Features: AI Answers, Mobile SDKs, SAML SSO, white-labeling all require Enterprise plan
Average Enterprise Contract: ~$39,000 annually according to Vendr procurement data
Standard Plan: $99/month or $89/month annual - 10 custom chatbots, 5,000 items per chatbot, 60 million words per bot, basic helpdesk support, standard security
View Pricing
Premium Plan: $499/month or $449/month annual - 100 custom chatbots, 20,000 items per chatbot, 300 million words per bot, advanced support, enhanced security, additional customization
Enterprise Plan: Custom pricing - Comprehensive AI solutions, highest security and compliance, dedicated account managers, custom SSO, token authentication, priority support with faster SLAs
Enterprise Solutions
7-Day Free Trial: Full access to Standard features without charges - available to all users
Annual billing discount: Save 10% by paying upfront annually ($89/mo Standard, $449/mo Premium)
Flat monthly rates: No per-query charges, no hidden costs for API access or white-labeling (included in all plans)
Managed infrastructure: Auto-scaling cloud infrastructure included - no additional hosting or scaling fees
Support & Documentation
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
4.8/5 G2 Rating: 132 reviews with consistently high satisfaction scores
Ease of Use Praised: "Ease of use" mentioned 32 times in G2 reviews indicating intuitive platform
Help Center Documentation: Comprehensive guides and tutorials for platform features
Email and Chat Support: Standard support channels for all paid plans
Dedicated Support (Enterprise): Priority support with dedicated account team and faster response times
Pre-Built Templates: Common support scenario templates accelerating guide creation
Quick Onboarding: Users report creating guides in under 30 minutes with small learning curve
REST API Documentation: API reference for user provisioning, content management, and widget control
Mobile SDKs (Enterprise): iOS, Android, React Native, Flutter for native app integration
Limited Developer Resources: No Python/Node.js SDKs, GraphQL, OpenAPI specs, or API Explorer/sandbox
Documentation hub: Rich docs, tutorials, cookbooks, FAQs, API references for rapid onboarding
Developer Docs
Email and in-app support: Quick support via email and in-app chat for all users
Premium support: Premium and Enterprise plans include dedicated account managers and faster SLAs
Code samples: Cookbooks, step-by-step guides, and examples for every skill level
API Documentation
No Real-Time Analytics: Flow reports update every 15 minutes—not true real-time monitoring
Limited Developer API: No Python/Node.js SDKs, GraphQL, Swagger specs, or API sandbox for testing
Overage Pricing Escalation: View limits can trigger expensive automatic upgrades after 2 consecutive months
Not Ideal For: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning, or flexible data source integration
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
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
Conversational AI Bot: Delivers confident answers backed by verified structured knowledge unlike generic LLMs that can hallucinate or invent answers
Knowledge-grounded responses: Provides answers backed by verified structured knowledge from guides you create preventing fabricated information
AI Agent Assist: Automatically summarizes tickets, suggests right path to resolution, and generates responses for support agents
Three core automation functions: Automatically analyzes and summarizes support ticket content, recommends most relevant Stonly guide/knowledge path to resolve issues, drafts complete responses for agents to review/edit/send
Process automation: Define processes to be followed and link them to different back-office tools to resolve customer requests before they reach support
Personalized knowledge: AI-powered solutions and process automation allow creation of guides, walkthroughs, checklists, knowledge bases adapting to each customer's needs
71% self-serve success rate: With AI Answers feature documented in company data
Hallucination reduction: Knowledge-grounding approach vs generic chatbots reduces off-topic responses
Custom AI Agents: Build autonomous agents powered by GPT-4 and Claude that can perform tasks independently and make real-time decisions based on business knowledge
Decision-Support Capabilities: AI agents analyze proprietary data to provide insights, recommendations, and actionable responses specific to your business domain
Multi-Agent Systems: Deploy multiple specialized AI agents that can collaborate and optimize workflows in areas like customer support, sales, and internal knowledge management
Memory & Context Management: Agents maintain conversation history and persistent context for coherent multi-turn interactions
View Agent Documentation
Tool Integration: Agents can trigger actions, integrate with external APIs via webhooks, and connect to 5,000+ apps through Zapier for automated workflows
Hyper-Accurate Responses: Leverages advanced RAG technology and retrieval mechanisms to deliver context-aware, citation-backed responses grounded in your knowledge base
Continuous Learning: Agents improve over time through automatic re-indexing of knowledge sources and integration of new data without manual retraining
R A G-as-a- Service Assessment
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
Note: NOT a RAG-as-a-Service platform - fundamentally a knowledge base tool with embedded AI
Data source flexibility: Limited (PDF, public web, Zendesk only) vs comprehensive RAG platforms
LLM model options: None (undisclosed proprietary model, no user selection)
API-first architecture: Weak (widget-focused, limited SDKs, no server-side SDKs)
Performance benchmarks: Not published
Self-service AI pricing: Not available (Enterprise-gated, ~$39K/year)
Help desk integration depth: Excellent (best-in-class Zendesk, Salesforce, Freshdesk)
Hallucination controls: Strong (grounded in structured content)
Best for: Customer support ticket deflection, not flexible RAG backends
Not ideal for: Developers seeking pure RAG API, multi-tenant SaaS RAG backends, use cases needing model selection/fine-tuning
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
Core Knowledge Base Features
N/A
Interactive step-by-step guides with visual flow builder
After analyzing features, pricing, performance, and user feedback, both SimplyRetrieve and Stonly are capable platforms that serve different market segments and use cases effectively.
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
When to Choose Stonly
You value exceptional ease of use - 4.8/5 g2 rating with intuitive visual editor praised in 32 reviews
Deep help desk integrations - bidirectional Zendesk, Salesforce, Freshdesk, ServiceNow connections
Strong compliance - SOC 2 Type 2, GDPR, HIPAA, ISO 27001, PCI, CSA Star Level 1
Best For: Exceptional ease of use - 4.8/5 G2 rating with intuitive visual editor praised in 32 reviews
Migration & Switching Considerations
Switching between SimplyRetrieve and Stonly requires careful planning. Consider data export capabilities, API compatibility, and integration complexity. Both platforms offer migration support, but expect 2-4 weeks for complete transition including testing and team training.
Pricing Comparison Summary
SimplyRetrieve starts at custom pricing, while Stonly begins at $249/month. Total cost of ownership should factor in implementation time, training requirements, API usage fees, and ongoing support. Enterprise deployments typically see annual costs ranging from $10,000 to $500,000+ depending on scale and requirements.
Our Recommendation Process
Start with a free trial - Both platforms offer trial periods to test with your actual data
Define success metrics - Response accuracy, latency, user satisfaction, cost per query
Test with real use cases - Don't rely on generic demos; use your production data
Evaluate total cost - Factor in implementation time, training, and ongoing maintenance
Check vendor stability - Review roadmap transparency, update frequency, and support quality
For most organizations, the decision between SimplyRetrieve and Stonly comes down to specific requirements rather than overall superiority. Evaluate both platforms with your actual data during trial periods, focusing on accuracy, latency, ease of integration, and total cost of ownership.
📚 Next Steps
Ready to make your decision? We recommend starting with a hands-on evaluation of both platforms using your specific use case and data.
• Review: Check the detailed feature comparison table above
• Test: Sign up for free trials and test with real queries
• Calculate: Estimate your monthly costs based on expected usage
• Decide: Choose the platform that best aligns with your requirements
Last updated: December 13, 2025 | This comparison is regularly reviewed and updated to reflect the latest platform capabilities, pricing, and user feedback.
The most accurate RAG-as-a-Service API. Deliver production-ready reliable RAG applications faster. Benchmarked #1 in accuracy and hallucinations for fully managed RAG-as-a-Service API.
DevRel at CustomGPT.ai. Passionate about AI and its applications. Here to help you navigate the world of AI tools and make informed decisions for your business.
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