In this comprehensive guide, we compare Fastbots and SciPhi 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 Fastbots and SciPhi, 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 Fastbots if: you value best value for multi-llm access - $19.99/month for gpt-4, claude, and gemini (vs competitors at $50-100/month)
Choose SciPhi if: you value state-of-the-art retrieval accuracy
About Fastbots
Fastbots is ai chatbot platform with 80+ integrations and white-label agency features. Fastbots is a multi-LLM chatbot platform with 80+ native integrations, visual flow builder, and comprehensive white-labeling for agencies. It offers intelligent routing across GPT-4, Claude, and Gemini with competitive pricing starting at $19.99/month, but lacks enterprise certifications and has inconsistent performance across different LLMs. Founded in 2023, headquartered in United States, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
96/100
Starting Price
$19.99/mo
About SciPhi
SciPhi is the most advanced ai retrieval system. R2R is a production-ready AI retrieval system supporting Retrieval-Augmented Generation with advanced features including multimodal ingestion, hybrid search, knowledge graphs, and a Deep Research API for multi-step reasoning across documents and the web. Founded in 2023, headquartered in San Francisco, CA, the platform has established itself as a reliable solution in the RAG space.
Overall Rating
89/100
Starting Price
Custom
Key Differences at a Glance
In terms of user ratings, Fastbots 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
Fastbots
SciPhi
CustomGPTRECOMMENDED
Data Ingestion & Knowledge Sources
Website crawling: Enter URL and auto-extract content with configurable depth
Document upload: PDF, DOCX, TXT, CSV files
Audio and video ingestion: Upload media files for transcription and knowledge extraction
Plain text input: Paste or type content directly
Storage limits: 400K characters (Free), 11 million characters (Starter+)
Auto-retrain: Configurable schedule for knowledge base updates (daily, weekly, monthly)
Note: No native Google Drive, Dropbox, or Notion integrations - requires manual export or API setup
Note: No YouTube transcript auto-ingestion - video must be uploaded as file
Note: 11M character limit can fill quickly with comprehensive documentation (e.g., enterprise KB with 100+ articles)
Sitemap support: Bulk import from XML sitemaps
Handles 40 + formats—from PDFs and spreadsheets to audio—at massive scale
Reference.
Async ingest auto-scales, crunching millions of tokens per second—perfect for giant corpora
Benchmark details.
Ingest via code or API, so you can tap proprietary databases or custom pipelines with ease.
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.
L L M Model Options
OpenAI models: GPT-4, GPT-4 Turbo, GPT-3.5 Turbo
Anthropic Claude 3: Opus (most capable), Sonnet (balanced), Haiku (fast)
Google Gemini Pro 1.5
Meta Llama 3.1
Model selection: User chooses specific LLM per chatbot
Intelligent routing: Assign different models to different conversation scenarios (e.g., GPT-4 for complex, GPT-3.5 for simple)
Cost optimization: Route simple queries to cheaper models, complex to GPT-4
Note: Performance varies by model: Users report GPT-4 works best, Claude/Gemini show inconsistencies
No API key requirement: Models included in subscription (vs bring-your-own-key platforms)
LLM-agnostic—GPT-4, Claude, Llama 2, you choose.
Pick, fine-tune, or swap models anytime to balance cost and performance
Model options.
Taps into top models—OpenAI’s GPT-4, GPT-3.5 Turbo, and even Anthropic’s Claude for enterprise needs.
Automatically balances cost and performance by picking the right model for each request.
Model Selection Details
Uses proprietary prompt engineering and retrieval tweaks to return high-quality, citation-backed answers.
Handles all model management behind the scenes—no extra API keys or fine-tuning steps for you.
Performance & Accuracy
GPT-4 performance: Highest accuracy and consistency reported by users
Claude 3 performance: Mixed results - some users report hallucinations and off-topic responses
Gemini Pro performance: Inconsistent accuracy noted in user reviews
Overall accuracy: ~85% with optimal model selection (GPT-4)
Response time: Real-time streaming for faster perceived performance
Uptime: ~99.5% estimated from user feedback
Note: No published SLA commitments
Conversation memory: Context retention across messages within session
Hybrid search (dense + keyword) keeps retrieval fast and sharp.
Knowledge-graph boosts (HybridRAG) drive up to 150 % better accuracy
Sub-second latency—even at enterprise scale.
Delivers sub-second replies with an optimized pipeline—efficient vector search, smart chunking, and caching.
Independent tests rate median answer accuracy at 5/5—outpacing many alternatives.
Benchmark Results
Always cites sources so users can verify facts on the spot.
Maintains speed and accuracy even for massive knowledge bases with tens of millions of words.
White-label from Starter plan vs enterprise-only at competitors ($199+)
Market position: Developer-first RAG infrastructure (R2R framework) combining open-source flexibility with managed cloud service, specializing in enterprise-scale performance and advanced RAG techniques
Target customers: Development teams building high-performance RAG applications, enterprises requiring massive-scale ingestion (millions of tokens/second), and organizations wanting HybridRAG with knowledge graph capabilities for 150% accuracy improvements
Key competitors: LangChain/LangSmith, Deepset/Haystack, Pinecone Assistant, and custom RAG implementations
Competitive advantages: Async ingest auto-scaling to millions of tokens/second, 40+ format support including audio at massive scale, HybridRAG with knowledge-graph boosting (up to 150% better accuracy), sub-second latency even at enterprise scale, LLM-agnostic with easy model swapping (GPT-4, Claude, Llama 2), open-source R2R core for transparency and portability, and self-hosting options for complete control
Pricing advantage: Free tier plus $25/month Dev tier for experiments; enterprise plans with custom pricing and self-hosting; open-source foundation enables cost savings for teams with infrastructure expertise; best value for high-volume applications requiring enterprise-scale performance
Use case fit: Perfect for enterprises processing massive document volumes requiring async auto-scaling ingestion, development teams needing advanced RAG techniques (HybridRAG, knowledge graphs) for accuracy improvements, and organizations wanting open-source foundation with option to self-host for complete control and cost optimization
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
OpenAI models: GPT-4, GPT-4 Turbo, GPT-3.5 Turbo with user selection per chatbot
Anthropic Claude 3: Opus (most capable), Sonnet (balanced), Haiku (fast)
Google Gemini Pro 1.5 for multimodal capabilities
Meta Llama 3.1 open-source alternative
Intelligent routing: Assign different models to different conversation scenarios (e.g., GPT-4 for complex, GPT-3.5 for simple)
Cost optimization: Route simple queries to cheaper models (GPT-3.5), complex to premium (GPT-4)
No API key requirement: Models included in subscription vs bring-your-own-key platforms
Performance variance: User reports indicate GPT-4 works best, Claude/Gemini show inconsistencies
LLM-Agnostic Architecture: Supports GPT-4, GPT-3.5-turbo, Claude (Anthropic), Llama 2, and other open-source models
Model Flexibility: Easy model swapping to balance cost and performance without vendor lock-in
Custom Model Support: Configure any LLM via API, including fine-tuned or proprietary models
Embedding Models: Supports multiple embedding providers for semantic search and vector generation
Model Configuration: Full control over temperature, max tokens, and other generation parameters
Primary models: GPT-4, GPT-3.5 Turbo from OpenAI, and Anthropic's Claude 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
Website crawling: Auto-extract content with configurable depth from URL entry
Document upload: PDF, DOCX, TXT, CSV files with 11 million character storage limit (Starter+)
Audio and video ingestion: Upload media files for transcription and knowledge extraction
Auto-retrain scheduling: Configurable updates (daily, weekly, monthly) for knowledge base freshness
Sitemap support: Bulk import from XML sitemaps for comprehensive site coverage
Conversation memory: Context retention across messages within session
Overall accuracy: ~85% with optimal model selection (GPT-4 performs best)
Response time: Real-time streaming for faster perceived performance
Limitations: No native Google Drive, Dropbox, or Notion integrations; 11M character limit fills quickly with comprehensive documentation
HybridRAG Technology: Combines vector search with knowledge graphs for up to 150% accuracy improvement over traditional RAG
Hybrid Search: Dense vector retrieval + keyword search with reciprocal rank fusion for optimal precision
Knowledge Graph Extraction: Automatic entity and relationship mapping enriches context across documents
Agentic RAG: Reasoning agent integrated with retrieval for autonomous research across documents and web
Multimodal Ingestion: Process 40+ formats including PDFs, spreadsheets, audio files at massive scale
Async Auto-Scaling: Millions of tokens per second ingestion throughput for enterprise document volumes
Sub-Second Latency: Fast retrieval even at enterprise scale with optimized vector operations
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
E-commerce customer support: Shopify, WooCommerce, BigCommerce integrations for 24/7 product queries and order tracking
Lead generation: Custom forms with field validation, lead qualification scoring, and CRM sync (HubSpot, Salesforce, Pipedrive)
Multi-channel deployment: WhatsApp (Cloud API + 360Dialog), Facebook Messenger, Instagram DM, Telegram, Slack, Discord with unified inbox
Small business websites: JavaScript widget embedding with customization for professional appearance at $19.99/month
Agency white-label: Custom domains, remove branding from Starter plan for client deployments
Multilingual support: 95+ languages with automatic translation for global customer bases
NOT suitable for: Regulated industries (no HIPAA, SOC 2), voice/IVR use cases, enterprises requiring compliance certifications
Enterprise Knowledge Management: Process and search across millions of documents with knowledge graph relationships
Customer Support Automation: Build RAG-powered support bots with accurate, grounded responses
Research & Analysis: Agentic RAG capabilities for autonomous research across document collections and web
Compliance & Legal: Search and analyze large document repositories with precise citation tracking
Internal Documentation: Developer-focused RAG for code documentation, API references, and technical knowledge bases
Custom AI Applications: API-first architecture enables integration into any custom application or workflow
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)
Professional ($99/mo): 5 chatbots, 10K messages/month, priority support, API access, advanced analytics
Business ($399/mo): 20 chatbots, 40K messages/month, white-label, dedicated account manager
5-day trial: Test paid features before committing to subscription
Best value proposition: $19.99 for GPT-4, Claude, Gemini access vs competitors at $50-100/month
No hidden costs: LLM usage included in subscription (no per-token charges like some platforms)
Annual discount: Save 20% with yearly billing commitment
Free Tier: Generous free tier requiring no credit card for experimentation and development
Developer Plan: $25/month for individual developers and small projects
Enterprise Plans: Custom pricing based on scale, features, and support requirements
Self-Hosting: Open-source R2R available for free self-hosting (infrastructure costs only)
Managed Cloud: SciPhi handles infrastructure, deployment, scaling, updates, and maintenance
No Per-Request Fees: Flat subscription pricing without per-query or per-document charges
Cost Optimization: Self-hosting option enables cost savings for teams with infrastructure expertise
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
4.9/5 customer support rating on G2 (exceptional for pricing tier)
Email support: Available on all plans including free tier
Priority support: Professional and Business plans with faster response times
Dedicated account manager: Business plan ($399/month) includes personal contact
Knowledge base: Comprehensive help center with guides and tutorials
Video tutorials: Step-by-step implementation guides for common scenarios
Community: User community for best practices sharing and tips
Live chat support: Available during business hours for quick questions
Response time: Fast responses noted by users (typically within hours, not days)
Limitations: No 24/7 support on lower tiers, no SLA guarantees on response times
Comprehensive Documentation: Detailed docs at r2r-docs.sciphi.ai covering all features and API endpoints
GitHub Repository: Active open-source development at github.com/SciPhi-AI/R2R with code examples
Community Support: Discord community and GitHub issues for peer support and troubleshooting
Enterprise Support: Dedicated support channels for enterprise customers with SLAs
Code Examples: Python client (R2RClient) with extensive examples and starter code
API Reference: Complete REST API documentation with curl examples and authentication guides
Developer Dashboard: Real-time logs, latency monitoring, and retrieval quality metrics
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
Deep Research API: Multi-step reasoning system fetching data from knowledgebase and/or internet for rich, context-aware answers to complex queries
Tool Orchestration: Dynamic tool invocation with intelligent routing based on query characteristics and context requirements
Citation Transparency: Detailed responses with citations to source material for fact-checking and verification
LIMITATION - No Pre-Built Chat UI: API-first platform requiring developers to build custom conversational interfaces - not a turnkey chatbot solution
LIMITATION - No Lead Capture/Analytics: Focuses on knowledge retrieval infrastructure - lead generation, dashboards, and human handoff must be implemented at application layer
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: CONVERSATIONAL AI PLATFORM WITH RAG (not pure RAG-as-a-Service) - chatbot builder with integrated knowledge retrieval
Data source flexibility: Good - Website crawling with configurable depth, document upload (PDF, DOCX, TXT, CSV), audio/video ingestion, plain text input, sitemap support
LLM model options: Excellent - OpenAI (GPT-4, GPT-4 Turbo, GPT-3.5 Turbo), Anthropic Claude 3 (Opus, Sonnet, Haiku), Google Gemini Pro 1.5, Meta Llama 3.1 with user selection per chatbot
Knowledge base management: 11M character storage limit (Starter+), auto-retrain scheduling (daily, weekly, monthly), conversation memory for context retention
API-first architecture: Weak - REST API available on Professional ($99/mo) and above, no official SDKs, basic documentation, no Swagger/OpenAPI spec
Performance benchmarks: ~85% accuracy with optimal model selection (GPT-4), real-time streaming responses, ~99.5% uptime estimated from user feedback (no published SLA)
RAG accuracy: GPT-4 highest accuracy/consistency, Claude 3/Gemini Pro show mixed results with inconsistencies noted in user reviews
Self-service AI pricing: Excellent - $19.99/month for GPT-4, Claude, Gemini access (best value in market vs competitors at $50-100/month)
Compliance & certifications: Poor - GDPR/CCPA compliant, data encryption, SSL/TLS but NO SOC 2, HIPAA, ISO 27001, PCI DSS, FedRAMP
Integration ecosystem: Excellent - 80+ native integrations (no Zapier/Make required) including WhatsApp, Messenger, Instagram, Shopify, Stripe, HubSpot, Salesforce
Best for: SMBs, agencies, e-commerce stores prioritizing value, multi-LLM access, and native integrations over enterprise RAG features and certifications
Not suitable for: Regulated industries (healthcare, finance), enterprises requiring certifications, advanced RAG parameter controls, voice/IVR use cases
Platform Type: HYBRID RAG-AS-A-SERVICE - combines open-source R2R framework with SciPhi Cloud managed service for enterprise deployments
Core Mission: Bridge gap between experimental RAG models and production-ready systems with straightforward path to deploy, adapt, and maintain RAG pipelines
Developer Target Market: Built by and for OSS community to help startups and enterprises quickly build with RAG - emphasizes developer flexibility and control
Deployment Flexibility: Free tier + $25/month Dev tier, Enterprise plans with custom pricing and self-hosting options - unique among RAG platforms for offering both managed and on-premise
RAG Technology Leadership: HybridRAG (knowledge graph boosting for 150% accuracy improvement), async auto-scaling to millions of tokens/second, 40+ format support including audio at massive scale, sub-second latency
Open-Source Advantage: Complete transparency with R2R core on GitHub, enables customization and portability, avoids vendor lock-in while offering managed cloud option
Enterprise Features: Multimodal ingestion, agentic RAG with reasoning agents, document-level security, comprehensive observability, customer-managed encryption for self-hosted deployments
API-First Architecture: REST API + Python client (R2RClient) with extensive documentation, sample code, GitHub repos for deep integration control
LIMITATION vs No-Code Platforms: NO native chat widgets, Slack/WhatsApp integrations, visual agent builders, or pre-built analytics dashboards - developer-first approach requires technical resources
Comparison Validity: Architectural comparison to CustomGPT.ai is VALID but highlights different priorities - SciPhi developer infrastructure with self-hosting vs CustomGPT likely more accessible no-code deployment
Use Case Fit: Enterprises processing massive document volumes requiring async auto-scaling, development teams needing advanced RAG (HybridRAG, knowledge graphs) for accuracy improvements, organizations wanting open-source foundation with self-hosting for complete control
NOT Ideal For: Non-technical teams requiring no-code chatbot builders, businesses needing immediate deployment without developer involvement, organizations seeking turnkey UI widgets and integrations
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
Additional Considerations
Free plan limitations: Only 50 messages per month suitable for testing rather than real-world production use
Not suitable for complex flows: Limited ability for intricate multi-step "if-this-then-that" logic like classic Messenger marketing bots
Training time investment: Bot training and customization take time to master for optimal performance
Limited Meta integration: Limited ability to integrate with Meta (Facebook) content lessens overall tool value for social media marketing
Company maturity: Founded in 2022, still building long-term enterprise track record vs more established players - consideration for very large corporations
Scalability evaluation: Businesses should evaluate whether pricing model accommodates growth without becoming prohibitively expensive
Custom plans available: Enterprise needs can be accommodated with custom pricing and fully managed services
Managed services offering: For large teams with advanced needs, FastBots offers fully managed services handling strategy, setup, training, and ongoing improvements
Strategic advantage: Unmatched flexibility with choice of LLMs and data sources distinguishes from competitors with locked-in models
Advanced extras like GraphRAG and agentic flows push beyond basic Q&A
Great fit for enterprises needing deeply customized, fully integrated AI solutions.
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.
Visual flow builder: Drag-and-drop conversation design with no coding required for creating chatbot workflows
Tone and personality: Configurable via system prompts to match brand voice and communication style
Greeting messages: Customize initial bot message and icebreakers for welcoming user experience
Multi-language support: 95+ languages with automatic translation for global customer bases
Knowledge source control: Decide what chatbot knows - uploaded information (files, docs, brand tone), ChatGPT general knowledge, or live internet search for real-time info
Auto-retrain scheduling: Configurable daily, weekly, or monthly knowledge base updates for content freshness
Conversation flow builder: Visual drag-and-drop interface for designing conversation paths
Custom forms: Lead capture with custom fields and field validation for data collection
Lead qualification: Score and route leads based on responses for sales prioritization
Intelligent routing: Assign different models to different conversation scenarios (GPT-4 for complex, GPT-3.5 for simple) for cost optimization
Military-grade encryption: All uploaded data secured with military-grade encryption for data protection
Add new sources, tweak retrieval, mix collections—everything’s programmable.
Chain API calls, re-rank docs, or build full agentic flows
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.
After analyzing features, pricing, performance, and user feedback, both Fastbots and SciPhi are capable platforms that serve different market segments and use cases effectively.
When to Choose Fastbots
You value best value for multi-llm access - $19.99/month for gpt-4, claude, and gemini (vs competitors at $50-100/month)
80+ native integrations eliminate need for Zapier/Make middleware (saves $20-50/month)
Exceptional customer support - 4.9/5 rating with fast response times
Best For: Best value for multi-LLM access - $19.99/month for GPT-4, Claude, and Gemini (vs competitors at $50-100/month)
When to Choose SciPhi
You value state-of-the-art retrieval accuracy
Open-source with strong community
Production-ready with proven scalability
Best For: State-of-the-art retrieval accuracy
Migration & Switching Considerations
Switching between Fastbots and SciPhi 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
Fastbots starts at $19.99/month, while SciPhi 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 Fastbots and SciPhi 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 4, 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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