LLM API Integration Patterns

The Real Integration Problem

Calling an LLM API is easy. Operating a product that depends on one is the harder part.

Reliability Patterns

• Set connect and read timeouts.
• Retry only when the error is retryable.
• Use exponential backoff with jitter.
• Track provider, model, latency, token usage, and error class.
• Add a fallback or graceful degradation path.
• Cache deterministic or expensive results when product behavior allows it.

Cost Controls

• Set per-request token budgets.
• Log prompt and completion token counts without storing sensitive content.
• Add account, workflow, or job-level quotas.
• Prefer smaller models for routing, extraction, and validation when quality is sufficient.

Security

• Never log API keys.
• Avoid sending private user data unless the product explicitly requires it.
• Separate system prompts from user content.
• Treat model output as untrusted text.

Media Pipeline Fit

For animation or creative AI workflows, model APIs often sit inside a larger job system:

1. Validate input.
2. Create a job.
3. Call provider.
4. Store artifacts.
5. Run post-processing.
6. Notify or expose job status.