OpenAI API Integration Checklist: Cost, Latency, Security, and Evals

An OpenAI API integration looks easy on day one. Then you ship it and discover the real work: cost control, privacy, prompt injection, evals, and the weird days where everything slows down.

If you’re integrating the OpenAI API into a real product, this is the checklist I use so the feature stays predictable under load and under change.

0) Define the feature (not “add AI”)

Before you write code, write one sentence:

“When the user does X, the system returns Y in Z seconds, and if it can’t, it does W.”

That sentence forces:

an input/output contract
a latency budget
a fallback path

If you can’t write it, you can’t ship it reliably.

1) Put the API key behind your server

Do not ship API keys in the client.

Even if you “hide” them, they leak.

Minimum setup:

server-side proxy endpoint
environment-managed secrets
per-user authentication/authorization before calling the model

2) Treat user input as hostile (prompt injection is real)

If your model sees user-provided text, assume it can be malicious.

Mitigations that actually help:

strict system instructions (clear, short, non-negotiable)
isolate tool instructions from user content
never “execute” user-provided instructions as policy
sanitize and delimit user content (so it’s clearly “data”)

If your assistant can call tools (read data, write data), add a permission layer between the model and the tool.

3) Decide what the model is allowed to do

Define boundaries:

can it only answer questions?
can it draft text for approval?
can it take actions directly?

If it can take actions, you need:

approvals (human-in-the-loop for risky actions)
idempotency (retries don’t duplicate)
audit logs (what happened and why)

“Agent” features fail when the system can’t explain itself.

4) Define your output contract (and validate it)

If your product needs structured output (fields, JSON), don’t “hope” the model follows the format.

Do this instead:

define a schema (even informal at first)
parse the output
validate it
on failure: retry with a constrained repair prompt, or fall back

Most production failures are “format drift,” not “model intelligence.”

5) If the feature depends on your data, do retrieval (RAG) early

If you need answers grounded in internal docs or private customer data, add retrieval:

choose sources (docs, tickets, KB, database rows)
chunk and index them
retrieve relevant context at runtime
cite sources in the output (even if you don’t show citations to the user)

If you’re debating “RAG vs fine‑tuning,” start here: /writing/posts/rag-vs-fine-tuning/

6) Build evals before you scale usage

Evals are how you avoid shipping regressions every time you tweak a prompt.

Start small:

25–50 representative inputs
expected outputs (or expected traits)
pass/fail rules

Then run evals:

before deploying prompt changes
when switching model versions
when changing retrieval settings

If you don’t have evals, your “improvements” are guesses.

7) Put a cost budget in code (not in hope)

Token usage is not abstract. It’s a line item.

Put guardrails in place:

per-request token limits
per-user and per-workspace quotas
caching for repeat queries (when safe)
cheaper model for low-risk tasks
avoid sending unnecessary context

If you can’t explain what drives cost, you can’t control it.

8) Engineer for latency (UX is part of the feature)

Latency is a product problem.

Practical tactics:

stream responses for chat UIs
set timeouts with clear UI fallback
debounce or batch user input
cache retrieval results where appropriate
precompute embeddings and summaries offline

If the user feels like the system is “thinking forever,” trust drops fast.

9) Reliability: retries, backoff, and circuit breakers

Production reality:

requests fail
rate limits happen
upstreams slow down

You need:

retry with exponential backoff (for retryable errors)
idempotency keys for action-taking workflows
circuit breakers (so your app degrades instead of cascading)
graceful degradation: “AI unavailable, continue without it”

10) Observability: log what matters (and redact what doesn’t)

You’ll want to debug:

why an answer was wrong
what context was retrieved
what tool calls ran
how long it took
how much it cost

Log:

request id
user/workspace id
model + settings
token usage + latency
retrieval source ids
refusal/unknown rates

Redact:

secrets
raw PII (unless you have a strong reason and a policy)

If you can’t inspect the system under stress, you don’t own it.

A simple definition of “production-ready”

An OpenAI API integration is production-ready when:

it has a narrow, testable feature definition
it has eval coverage for the important cases
it has cost budgets and quotas
it has safe fallbacks
it logs enough to debug failures quickly

Want this shipped into your product?

If you want an OpenAI API integration that’s more than a demo, I can build it into your app with:

retrieval where needed
evals and guardrails
cost and latency budgets

Use the call template: /call/ or email [email protected].