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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.
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DHTMLX Chatbot
✓ verifiedFree
Lightweight MIT-licensed JavaScript chatbot UI widget for building support-agent chat interfaces on top of any LLM.
92K visits/mo
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Tinybird
✓ verifiedFreemium
Managed ClickHouse platform giving developers hosted ingestion and query APIs to ship real-time analytics products fast.
104K visits/mo
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Google Opal
✓ verifiedFree
Google Labs experiment for building and sharing AI mini-apps from natural-language prompts, no coding required.
2.1M visits/mo
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Windsurf Editor
✓ verifiedFree trial
AI-powered code editor with agentic workflows for developers.
3.3M visits/mo
Pricing
No public pricing
Free: $0/mo (0.25 vCPUs, 1k requests/day, 10GB storage)
Developer: $49/mo (0.5 vCPUs, 25GB storage, 2 replicas)
No public pricing
No public pricing
Core features
- ✦MIT-licensed and free to use
- ✦Lightweight (~65kb) JavaScript widget
- ✦4 message display modes plus typewriter effect
- ✦Markdown support and chat history
- ✦Backend hook to integrate any LLM
- ✦Responsive, mobile-friendly, read-only mode
- ✦Managed, hosted ClickHouse database
- ✦Sub-second SQL query APIs
- ✦Kafka and other data connectors
- ✦Safe, zero-downtime schema migrations
- ✦Git-based branching with zero-copy prod data
- ✦SOC 2 Type II enterprise security
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦AI-powered code completion and suggestions
- ✦Automated lint fixing
- ✦Cascade agent for advanced coding assistance
- ✦Integrated app building and deployment
- ✦MCP server support for custom tools
- ✦Terminal command integration
- ✦Memory of codebase structure and workflow
Use cases
- →Adding an AI chat UI to web apps
- →Building LLM-powered support agents
- →Embedding chat widgets in existing products
- →Prototyping chatbot interfaces
- →Building real-time analytics dashboards
- →Powering in-app usage or event analytics
- →Streaming Kafka topics into query-ready tables
- →Letting AI coding agents build and deploy analytics
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →Accelerating software development by automating repetitive tasks
- →Reducing onboarding time for new developers
- →Improving code quality and reducing tech debt
- →Streamlining the app building and deployment process
- →Enhancing developer productivity by keeping them in a state of flow
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