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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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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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n8n
✓ verifiedFreemium
Popular source-available workflow automation platform for technical teams, blending a visual canvas, code steps and AI-agent orchestration.
6.7M visits/mo
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Pipedream
✓ verifiedFreemium
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
498K visits/mo
Pricing
No public pricing
No public pricing
Starter: €20/mo billed annually (2.5K executions)
Pro: €50/mo billed annually (10K executions)
Business: €667/mo billed annually (40K executions)
Free trial available
No public pricing
Core features
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦Build AI mini-apps from natural-language prompts
- ✦Visual editor for prompt/tool workflows
- ✦Share created apps with others
- ✦No-code AI app prototyping
- ✦Visual workflow builder with inline code (JS/Python)
- ✦500+ app and model integrations
- ✦AI agent and RAG workflow support
- ✦Self-hosting or managed cloud
- ✦Human-in-the-loop approvals and guardrails
- ✦Enterprise features: SSO, RBAC, audit logs, Git control
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
Use cases
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →Prototyping an AI workflow quickly
- →Sharing a custom AI mini-app
- →Automating a task with chained prompts
- →Building and running AI agents
- →Automating IT and security operations
- →Connecting and syncing data across apps
- →Prototyping backends and internal tools
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure
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