Guide
Complete guide to Huddle01 AI inference, endpoint setup, supported workflows, and next steps.
Huddle01 AI Inference gives you one OpenAI-compatible endpoint for multiple model providers.
What is inference?
Inference means sending input (messages, prompt, or multimodal data) to a model and getting generated output in response.
With Huddle01:
- You use one API key.
- You hit one base URL.
- You can switch models by changing only the
modelfield.
Endpoint and auth
- Base URL:
https://gru.huddle01.io/v1 - Auth header:
Authorization: Bearer <HUDDLE_API_KEY> - Compatibility: OpenAI-compatible SDKs and HTTP APIs
Quick navigation
API Examples
Connection setup, OpenAPI-style schema, and SDK request examples.
Pricing and Models
Model-wise input/output pricing and capabilities matrix.
Request Lifecycle
Understand what happens from API request to model response.
Best Practices
Production tips for reliability, security, and cost control.
Quick start flow
- Create or copy your Huddle01 API key from dashboard.
- Set your client
base_urltohttps://gru.huddle01.io/v1. - Call
chat/completionswith one of the supported model IDs. - Read usage and spend from your existing dashboard/billing views.
Request lifecycle
- Your app sends a request to
POST /chat/completions. - Huddle01 authenticates the API key and validates request shape.
- The gateway routes your request to the selected model/provider.
- Response is normalized into OpenAI-compatible format.
- You receive generated output and usage metadata.
What you can build
- AI chat and assistant experiences
- Content generation and summarization
- Code generation and developer tools
- Workflow automations with structured prompting
- Multi-model routing with one integration surface
Compatibility
- Works with OpenAI-compatible SDKs
- Works with direct HTTP calls (
curl, backend services, serverless functions) - Supports model switching without client rewrites
Best practices
- Keep API keys on the server side only.
- Use environment variables and secret managers, never hardcode keys.
- Start with lower-cost models for non-critical traffic.
- Add retry + timeout handling in production clients.
- Log
model, latency, token usage, and request ID for debugging.
Production tip
Use one default model for most traffic and route only high-complexity requests to premium models. This keeps latency and cost predictable.
Common mistakes to avoid
- Using the wrong base URL (must be
https://gru.huddle01.io/v1) - Sending API key in custom headers instead of
Authorization: Bearer ... - Assuming every model has identical behavior/capabilities
- Not handling transient upstream/network failures
Next steps
- Go to API Examples for concrete API requests and SDK examples.
- Check Pricing to pick the right models for your workloads.