In this comprehensive guide, we compare Botpress 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 Botpress 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 Botpress if: you value visual drag-and-drop builder with extensive code extensibility via execute code cards
Choose SimplyRetrieve if: you value completely free and open source
About Botpress
Botpress is enterprise ai agent platform with visual bot building and omnichannel deployment. Enterprise AI agent platform with visual bot building, omnichannel deployment, and RAG capabilities. 750,000+ active bots processing 1 billion+ messages with extensive channel support and no-code/low-code development. Founded in 2016, headquartered in Montreal, Quebec, Canada, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
85/100
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: Chatbot 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
Botpress
SimplyRetrieve
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Supported Formats: PDF, Word (DOC/DOCX), HTML, TXT, Markdown files via Studio UI and Files API
Website Crawling: Firecrawl integration for HTML-to-Markdown conversion with automatic sitemap detection
Real-Time Search: "Search The Web" feature using Bing API for queries when sitemaps unavailable
Cloud Integrations: Google Drive (OAuth sync with file upload/download triggers), Notion (database queries, page management)
Missing Integrations: No native Dropbox or Salesforce document ingestion
YouTube Limitation: No transcript ingestion support - requires manual transcription and text upload (Apify workaround exists but manual)
Automatic Retraining: Website sources sync regularly, file uploads managed dynamically through Files API
File Management: Replacing files automatically removes old content and indexes new content without downtime
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
Native Channels: WhatsApp (Meta Business API), Slack (OAuth + Bot Framework), Microsoft Teams (Azure portal), Telegram (BotFather), Messenger, Instagram
SMS Support: Twilio and Vonage integrations for text messaging
Web Widget: JavaScript widget (recommended), DOM element mounting, full React component library for SPAs
Mobile Integration: React Native SDK (BpWidget, BpIncomingMessagesListener) for iOS/Android cross-platform support
Webhook Support: Unique webhook URL per bot with optional x-bp-secret header authentication and CORS configuration
Automation Platforms: Zapier integration (partially in beta - some features require manual activation)
Conversational AI: Multi-turn dialogue with context retention across conversation sessions
Multi-Lingual: 100+ languages supported via Translator Agent with automatic translation
Knowledge Base Integration: RAG-powered answers grounded in uploaded documents and websites
Policy Agent: Customizable guardrails filtering outputs against defined policies for brand safety
Knowledge Agent: Structured retrieval before generation to reduce hallucinations
HITL Agent: Human-in-the-loop takeover when bot cannot answer (requires Team plan $495/month)
Personality Agent: Rewrites all bot messages to match defined persona (friendly, professional, casual, custom)
Autonomous Nodes: LLM decides which actions to execute based on conversation context
Performance Claims: "Zero hallucinations in 100,000 conversations" for health coaching client, 65% ticket deflection (no RAGAS scores or latency benchmarks published)
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
Customization & Branding
Webchat Customization: Full CSS override via external stylesheet URL, custom colors/fonts/button styles/chat bubbles
Branding Control: Custom bot name and avatar, proactive greeting messages via JavaScript, configurable placement and sizing
White-Labeling: Remove "Powered by Botpress" watermark (requires Plus plan $89/month minimum)
Personality Configuration: Personality Agent defines bot persona with variable expressions for dynamic context
Persona Disable: Can be disabled at node level for specific interactions requiring different tone
Backend Branding: Admin dashboard remains Botpress-branded (no full white-label backend)
Multi-Tenant Limitation: No agency dashboard for managing multiple client bots under one interface
Real-Time Updates: Knowledge sources update via Files API without bot republishing for Table-based sources
Versioning Gap: No native versioning system - file replacement is manual with external version control required for rollback
Default Gradio interface is pretty plain, with minimal theming.
For a branded UI you’ll tweak source code or build your own front end.
Fully white-labels the widget—colors, logos, icons, CSS, everything can match your brand.
White-label Options
Provides a no-code dashboard to set welcome messages, bot names, and visual themes.
Lets you shape the AI’s persona and tone using pre-prompts and system instructions.
Uses domain allowlisting to ensure the chatbot appears only on approved sites.
No Python SDK: Significant limitation for data science teams - other languages must use direct REST API access
Authentication: Three token types - Personal Access Token (PAT) for full access, Bot Access Key (BAK) for runtime, Integration Access Key (IAK) for integration-specific actions
Rate Limits: Exist but specifics not publicly documented - Studio limits lower than production bot limits (acknowledged by staff)
Documentation: Well-organized at botpress.com/docs with API references, video tutorials, "Ask AI" feature
Training Resources: Botpress Academy offers free courses
RAG Focus: RAG is one feature within comprehensive conversational AI platform, not standalone RAG API
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
Competitive Positioning
Primary Advantage: Visual bot building with code extensibility - accessible to non-developers, powerful for developers
Scale Validation: 750,000+ active bots and 1 billion+ messages processed prove production reliability at massive scale
Omnichannel Strength: Comprehensive native support for WhatsApp, Slack, Teams, Telegram, Messenger, SMS, web, mobile
Community Power: 31,000+ Discord members provide peer support, troubleshooting, best practices, feature validation
Primary Challenge: SOC 2 not certified, no EU data residency - critical gaps for enterprise buyers with compliance needs
Security Gap: Not HIPAA compliant, no ISO 27001 - blocks regulated industry adoption (healthcare, finance)
Cost Trade-Off: Free tier available but AI Spend unpredictability + feature paywalls (RBAC at $495/month) add complexity
Market Position: Conversational AI platform competing with Dialogflow, Rasa, Microsoft Bot Framework vs. pure RAG services
Use Case Fit: Ideal for teams needing visual bot building + multi-channel deployment vs. pure RAG API integrations
Platform vs. API: Full development environment with Studio, not lightweight RAG API - different target audience than CustomGPT
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
Native OpenAI Support: GPT-4o, GPT-4o mini, GPT-4 Turbo with in-Studio presets ("Best Model" and "Fast Model" for quick selection)
Claude Models: Claude 4 Sonnet, Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 4.5 Sonnet accessible via custom integrations or Execute Code cards
Google Gemini: Gemini Pro, Gemini 2.5 Flash available through external API calls in custom integrations
Open Source Options: LLaMA, DeepSeek accessible via Execute Code cards with external API integration
Model Access within Days: Platform provides access to latest LLMs within days of release for every chatbot built on Botpress
No Automatic Routing: Deliberately avoided for "concerns about unpredictability and latency" - users manually select models per task
LLMz Engine: Proprietary inference layer with claimed improvements - better tool calling, token efficiency, TypeScript type definitions, V8 isolate execution
AI Spend Pricing: Charged at-cost with no Botpress markup on OpenAI tokens; option to use Botpress-managed credits or BYOK (bring your own key)
No Fine-Tuning: RAG recommended as primary approach, supplemented by "learnings" system providing relevant examples at prompt-time
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
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
Use Cases
Customer Support: Most popular use case with 98% of chats resolved without human intervention (Ruby Labs: 4 million support chats monthly)
Sales Automation: Majority of deployed bots part of sales process - appointment scheduling, lead nurturing, product suggestions, competitive comparisons, automated follow-ups
Sales Impact: Businesses report average 67% sales increase using chatbots, projected $112 billion in retail sales for 2024
Enterprise Internal Use: HR chatbots for vacation requests, IT chatbots for employee tech troubleshooting, repetitive high-volume task automation
Lead Generation: AI lead generation qualifies leads through conversational engagement, needs assessment, information gathering, automated follow-up
Cost Savings: One bank saved €530,000 by deploying chatbot, demonstrating measurable enterprise ROI
Multi-Channel Engagement: WhatsApp Business API, Slack, Microsoft Teams, Telegram, Messenger, Instagram, SMS (Twilio/Vonage) for comprehensive reach
Scale Validation: 750,000+ active bots, 1 billion+ messages processed provide real-world production reliability proof
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)
Enterprise Contracts: May require multi-year commitments (3-year contracts mentioned in reviews)
Enterprise SLA: 99.8% uptime guarantee with service credits (5-25% depending on severity), maximum monthly credit 50% of charges
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
Free Plan Support: Community only - Discord (31,000+ members), documentation, forums - no direct support
Plus Plan Support: Live chat with Botpress engineers ($89/month) for direct technical assistance
Team Plan Support: Advanced support + solution engineering ($495/month) for complex implementations
Enterprise Support: Named support manager, SLA-backed response times (2 hours to 2 business days), ~$2,000+/month
Discord Community: 31,000+ highly active members with daily discussions, feature requests, troubleshooting - praised as "best Discord experience"
Documentation: Comprehensive docs at botpress.com/docs with API references, video tutorials, "Ask AI" feature for guided help
Botpress Academy: Free training courses covering bot development, best practices, advanced features
Response Time SLAs: 2 business days (standard Level 1) to 2 hours (premium Level 1) for Enterprise customers
Service Credits: 99.8% uptime SLA with credits for downtime, includes OpenAI unavailability (notable external dependency caveat)
Support Limitation: Non-Enterprise users lack formal ticketing system, may experience wait times for complex 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
Additional Considerations
High learning curve: Platform highly flexible but non-technical users struggle with advanced flow builder and developer-oriented features
Developer dependency: No quick copy-and-paste solution for real enterprise - company needs long-term employees ready to see it through with recommended 1-2 developers and 1-2 business-side employees per project
Performance under load: Live users report latency and webhook timeout issues under spiky high-concurrency loads - high-traffic teams should stress-test with projected peak traffic
Self-hosting complexity: For enterprise deployments with large numbers of bots or conversations self-hosting might be required shifting maintenance and scaling challenges to your team
Technical requirements: Configuring Docker, Kubernetes, databases, and certificates can become roadblock - requires skills in JavaScript, API integration, NLP, state management
DevOps investment needed: Teams should be prepared for additional DevOps investment for autoscaling, database sharding, and backup strategies
Unpredictable AI usage costs: Every message, retrieval, or workflow call consumes tokens making monthly bills swing dramatically depending on traffic and complexity
Hidden expenses: Third-party services like WhatsApp, SMS, voice integrations billed separately - advanced use cases often require engineering hours, enterprise deployments may require onboarding packages, compliance audits, or custom module builds costing thousands
Scaling costs: Growing from 5,000 to 20,000 MAUs means moving from $495/month to much higher custom enterprise price - multiple bots, custom integrations, or premium add-ons can push monthly spend well past initial plan quote
Resource-heavy features: Botpress LLM features can be resource-heavy requiring wise CPU/memory allocation planning
Commercial license threshold: Planning more than 150K interactions per month requires commercial license
Ongoing maintenance: Deployment is just start - bots must be continuously monitored, tested, and iterated to stay effective and aligned with evolving business goals
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.
Core Chatbot Features
Advanced AI capabilities: Extremely advanced AI with multiple sophisticated AI agents - automatic translation, conversation summarization, Vision Agent for image understanding
LLMz custom inference engine: Core of every Botpress agent with proprietary engine for enhanced performance
Conversational memory: Rich conversational memory maintaining context across long interactions, understanding complex multi-turn queries, and generating human-like responses
User memory across sessions: Agent remembers conversation history of specific users across different times - recalls user preferences, where they left off, and preferred tone of voice
Visual flow builder: Drag-and-drop interface for designing complex conversational flows without coding
Built-in AI features: Intent recognition, entity extraction, knowledge base integration, and AI agents
Custom data training: Train chatbot on custom data like website and documents
Multi-channel deployment: Create and launch chatbots on many channels including website, Facebook, WhatsApp, Slack, Instagram and more platforms
API integrations: Integrates with APIs, CRMs, databases, and other business applications
Automatic translation: Over 100 languages for global reach
AI Swarms/Teams (2025): Platform transformed into mature "AI workforce deployment and management center" with AI team collaboration capabilities
Live Database Connectors: Breakthrough feature allowing direct secure connection to SQL or NoSQL database in addition to traditional API connections
Open-source flexibility: Users have access to application source code and can contribute to development - skilled developers can push envelope to tailor to unique needs
Runs a retrieval-augmented chatbot on open-source LLMs, streaming tokens live in the Gradio UI.
Primarily single-turn Q&A; long-term memory is limited in this release.
Includes a “Retrieval Tuning Module” so you can see—and tweak—how answers are built from the data.
Reduces hallucinations by grounding replies in your data and adding source citations for transparency.
Benchmark Details
Handles multi-turn, context-aware chats with persistent history and solid conversation management.
Speaks 90+ languages, making global rollouts straightforward.
Includes extras like lead capture (email collection) and smooth handoff to a human when needed.
Knowledge Bases: Upload in variety of formats ranging from website or document to custom text file or Table
Knowledge Base scoping: Scope which Knowledge Bases Autonomous Node searches by organizing documents into folders limiting availability to certain workflows
Search field configuration: Configure search fields such as name, description, power, price to refine bot responses
Dynamic management: Programmatically manage Knowledge Base files with Botpress API to dynamically add, update, or remove content in real time keeping AI agent knowledge current
Behavior customization: Define specific behaviors in instructions to avoid unintended outputs - specify prices are final and include all discounts to prevent bot from fabricating discounts
Custom responses: Program custom response by adding Transition Card in Autonomous Node and handle transition however wanted with custom error messages
Bot templates: Pre-configured projects containing predefined conversational flows, Knowledge Bases, and responses serving as starting point - easily customized and extended to meet specific requirements with full developer control
Visual customization: Give bot name, store avatar URL for custom icon, provide general description, formulate placeholder text displayed before user enters first text
ChatGPT consultation: Customize bot behavior deciding when to consult ChatGPT based on knowledge base responses
Highly customizable workflows: Unlimited variables and open-source flexibility for advanced customization
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.
Limitations & Considerations
Steep Learning Curve: Platform highly flexible but non-technical users struggle with advanced flow builder and developer-oriented features
Developer Dependency: Requires developer involvement making it less suitable for small businesses needing quick setup
Bug Disruptions: Various bugs may disrupt workflows and cause functionality problems requiring troubleshooting
Missing Features: White-labeling, global compliance, seamless live support require heavy effort or unavailable, slowing adoption
Data Visibility Gap: Cannot see user variables (name, email, custom fields) in chatbot conversations - limits analytics capabilities
Cost for SMBs: Enterprise-level security, compliance, dedicated support cost prohibitive for smaller teams ($495-$2,000+/month)
Resource Requirements: Self-hosted deployment requires IT resources for deployment and ongoing management
Complex Setup: Publishing on Facebook/Instagram technically complex, live chat only available on higher-priced plans
Limited Analytics: Standard plans offer limited analytical capabilities - advanced analytics require Team plan ($495/month)
LLM Provider Dependency: Reliance on third-party LLM providers (primarily OpenAI) impacts operational costs, scalability, and control
Complex Issue Handling: Chatbots may struggle with handling complex, nuanced customer issues requiring human judgment
Multi-Instance Challenges: Setting up multiple instances from one installation proven difficult for some enterprise users
Compliance Gaps: SOC 2 incomplete, no HIPAA, no ISO 27001, US-only data residency blocks regulated industries and EU enterprises
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
After analyzing features, pricing, performance, and user feedback, both Botpress and SimplyRetrieve are capable platforms that serve different market segments and use cases effectively.
When to Choose Botpress
You value visual drag-and-drop builder with extensive code extensibility via execute code cards
Massive scale validation: 750,000+ active bots, 1 billion+ messages processed
Comprehensive omnichannel support: WhatsApp, Slack, Teams, Telegram, Messenger, SMS, web
Best For: Visual drag-and-drop builder with extensive code extensibility via Execute Code cards
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 Botpress 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
Botpress 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 Botpress 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 11, 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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