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Mintlify
✓ verifiedFreemium
AI documentation platform with clean design and dev-friendly features.
👁 718K/mo
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Watsonx.data
Freemium
Pricing
Essential: $29 per seat/mo (billed annually) + $0.99 per Fin resolution
Advanced: $85 per seat/mo (billed annually) + $0.99 per Fin resolution
Expert: $132 per seat/mo (billed annually) + $0.99 per Fin resolution
Fin AI Agent (on existing helpdesk): $0.99 per Fin resolution (50 resolutions per month minimum)
Copilot: $29 per agent/mo (billed annually)
Proactive Support Plus: $99 /mo
Starter: $0/mo (10,000 AI credits/month)
Free trial available
No public pricing
DEVELOPER: FREE
STARTER: $119 / month
GROWTH: $599 / month
ENTERPRISE: Starting at $1,800 / month
No public pricing
Core features
- ✦Fin AI Agent for automated customer support
- ✦Omnichannel support (inbox, tickets, phone, help center)
- ✦AI-enhanced inbox for agent productivity
- ✦AI Insights & Reporting for performance optimization
- ✦Workflow automation with a visual builder
- ✦AI-powered documentation enhancements
- ✦Codebase syncing and web editor
- ✦API playground
- ✦Visitor authentication
- ✦Automatic translations
- ✦Integrations with popular tools
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Developer-first platform for AI-powered integrations
- ✦Secure, isolated sandboxes for running JavaScript/Python code
- ✦Automatic management of npm/PyPI dependencies
- ✦Built-in platform plumbing: secrets, webhooks, scheduling, logs, and audit
- ✦Yep Agent (prompt → runnable processes)
- ✦MCP Server/Tools (convert code into AI agent tools)
- ✦Serverless runtime (YepCode Run) and SDK access
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Use cases
- →Providing instant customer service with Fin AI Agent
- →Managing customer inquiries across multiple channels
- →Automating support workflows to improve efficiency
- →Empowering support agents with AI-powered tools
- →Gaining insights into customer service performance
- →Creating public-facing documentation for APIs and SDKs
- →Building internal knowledge bases for teams
- →Generating documentation that converts users
- →Streamlining documentation workflows for developers and non-developers
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Building complex API integrations that require custom code and logic beyond what no-code tools offer.
- →Safely running AI-generated scripts in isolated environments with secrets management.
- →Automating workflows that require large datasets, loops, branching, or custom dependencies.
- →Connecting AI agents to external databases, APIs, and services using MCP tools.
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