Build Guides
DeepSeek API Integration: Endpoints, Configuration, and Production Checklist
A practical checklist for integrating a DeepSeek model through an OpenAI-compatible gateway and preparing it for production.
What you will learn
- Use the live model contract, not stale provider assumptions.
- Keep key, base URL, and model configurable on the server.
- Test usage, streaming, limits, failures, and rollback before launch.
Before you start
- Basic HTTP and API knowledge
Leave with a concrete implementation checklist and a testable starting point.
Key takeaways
- Use the live model contract, not stale provider assumptions.
- Keep key, base URL, and model configurable on the server.
- Test usage, streaming, limits, failures, and rollback before launch.
Start with the live model contract
Before coding, confirm the exact model name, enabled endpoint, context expectations, usage fields, and current account-group price. Do not hard-code assumptions from an old screenshot or blog post.
Treat the live EasyAI model page and Quickstart documentation as the source of truth for the account you will deploy.
Configuration and secrets
Keep the base URL, API key, and model name in server-side environment variables. Rotate keys through the console and make the model setting deploy-time configurable.
Add request timeouts, bounded retries, and structured logging before connecting production traffic.
Production checks
Test normal responses, streaming if used, long context, rate limits, invalid requests, and upstream failures. Record token usage and latency for a representative prompt set.
Promote gradually and define a rollback model before the first release.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Create and store an EasyAI API key securely.
- 2
Confirm the enabled DeepSeek model and endpoint.
- 3
Run representative staging requests.
- 4
Release gradually with rollback criteria.
Copy-ready example
const client = new OpenAI({
apiKey: process.env.EASYAI_API_KEY,
baseURL: process.env.EASYAI_BASE_URL,
});
const model = process.env.EASYAI_MODEL ?? "deepseek-chat";Frequently asked questions
Is the model name always the same?
No. Use the current enabled model name shown by the EasyAI catalog for your account.
Sources
- DeepSeek official documentationSource checked 2026-08-27