Calling the Claude or OpenAI API from a script is easy. Making that call dependable inside a product or an operations workflow is where projects stall. This is the checklist we use on every integration, whichever provider sits behind it.
The 10-point checklist
Pick the model per task, then test it on your data
Accuracy, latency, cost, context length and tool-use reliability vary by task. Test two or three candidates on a sample of your real inputs before committing. See which model should power your agents.
Use structured outputs
Ask for JSON that matches a schema and validate it. Free text that downstream code has to parse is the most common source of silent failures.
Design tool calls deliberately
Give each tool a narrow purpose, clear description and typed parameters. Limit what each tool is allowed to do, and require human approval for high-impact actions.
Handle errors, retries and rate limits
Retry transient errors with backoff, respect rate limits, set timeouts, and define what happens when the call finally fails, so users never see a blank screen.
Add a fallback provider
An outage at one provider should not take your feature down. A second model behind the same interface covers resilience and gives you cost leverage.
Control cost
Set token limits, cache repeated context, choose smaller models for simple steps, and add per-customer or per-feature budgets with alerts.
Build an evaluation set before launch
Collect real cases with expected outcomes, run them after every prompt or model change, and agree pass thresholds. This is what stops regressions.
Log every call
Record prompts, tool calls, outputs, latency and cost, with sensitive data handled according to your policy, so any decision can be reviewed later.
Secure keys and data
Keep API keys in a secrets manager, never in client code. Decide what customer data is sent to a provider, and check retention and residency terms against your obligations.
Plan ownership after launch
Someone has to own prompts, evaluation and model upgrades. Hand over code, prompts and a runbook, or keep a monitoring retainer such as AgentOps.
We deliver all ten as part of a fixed-price integration. Details on the Claude and OpenAI API integration service page.
Frequently asked questions
How long does a Claude or OpenAI API integration take?
A focused integration typically takes 2–4 weeks from scope to production. Multi-system integrations take longer, and the timeline is agreed in writing.
Should we integrate Claude or OpenAI?
It depends on the task. Test candidates on your own data for accuracy, latency, cost and tool-use reliability, and consider a second provider as a fallback.
Do we need a consultant for an API integration?
Not for a prototype. For production, an experienced team saves time on error handling, evaluation, cost controls, security and hand-over, which are the parts that usually cause delays.
Related: LLM integration services · LangGraph vs custom agent loop
Want a straight answer for your project?
Book a 30-minute call. I will tell you what I would do, and a fixed price if it makes sense.