Free TierAPI Key AuthOpenAPI Spec
Banana is a serverless GPU inference platform that allows developers to deploy machine learning models with autoscaling and pay-per-use pricing. It provides APIs for running inference on various model types without managing infrastructure.
#3 of 6 in Compute · #7 of 30 in Api
Checklist Breakdown
13 of 33 checks passed.
14 unscored.
Can an agent find and understand this tool without a web search?
✓
Published OpenAPI/Swagger spec
✗
Has llms.txt or llms-full.txt
✗
Has an MCP server (official or well-maintained)
✗
MCP server listed in a public registry
✓
API reference docs are publicly accessible
✓
Docs include runnable code examples
✓
Has a public changelog or release notes
✓
Has a public status page
Can an agent create an account and get credentials without human intervention?
✗
Signup does not require CAPTCHA
✗
Signup does not require phone verification
✓
Supports API key auth (not only OAuth)
✗
API key obtainable without manual approval
✓
No mandatory billing info to start
✓
Can sign up without creating an organization
Can an agent operate autonomously without upfront payment or contracts?
✓
Has a free tier
✓
Usage-based pricing available
✓
No minimum contract or commitment
✓
Pricing page is public (no 'contact sales')
✓
Free tier sufficient for testing (not just a trial)
How well does the API work for non-human consumers?
—
SDK available in 2+ languages
—
Structured error responses (JSON with error codes)
—
Idempotency support on write endpoints
—
Pagination on list endpoints
—
Webhook/event support
—
Sandbox or test mode available
—
Rate limit headers in responses
—
Consistent REST resource naming
Does the tool fail gracefully when an agent makes a mistake?
—
Meaningful error messages (not just 500)
—
429 responses include Retry-After header
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Documented uptime SLA (99.9%+)
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Graceful degradation under rate limits
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Request IDs in responses for debugging
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API versioning supported
Reviewer Notes
Banana has a workable REST API and free tier with GPU credits, making it accessible for agents. However, account creation requires email verification and manual dashboard setup, preventing fully autonomous signup. Discovery is hampered by the lack of MCP server and llms.txt file, though OpenAPI documentation exists. The API is reasonably well-structured for model inference but lacks comprehensive error handling documentation, and reliability metrics aren't publicly transparent. The pay-per-use model with free credits is agent-friendly, but the signup friction is the primary blocker.
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