How to Load Test an Inference API Before Launch

TL;DR: Load-test an inference API with the application’s real prompt lengths, outputs, concurrency, cache behavior, and routing path. Establish a serial baseline, ramp sustained load, inject bursts and failures, test limits and fallback, validate quality, and record p50, p95, completion, retries, throughput, and cost per accepted interaction.

A launch test should answer whether the product remains usable when traffic changes, not how many identical requests a script can send. Consumer AI workloads include long and short prompts, repeated context, several calls per interaction, bursts, and users who abandon slow responses.

What should the test dataset contain?

Build a request corpus from production or realistic preproduction traces. Preserve the distribution of input length, output length, tools, structured outputs, languages, modalities, and model routes. Remove sensitive data while keeping token and structural characteristics.

Scenario	Purpose
Short prompt, short output	Baseline service overhead
Long context, short reply	Consumer conversation prefill
Long output	Decode and streaming capacity
Repeated prefix	Warm-cache behavior
Unique prompt	Cold-cache behavior
Tool or schema request	Application compatibility
Multi-call interaction	End-to-end product cost and latency

Pin model versions and request settings. Random prompts can produce misleadingly easy or hard workloads.

How should the baseline be established?

Run 20 or more serial requests from the application’s backend region. Record DNS and connection behavior separately from provider processing where possible. Report TTFT, time per output token, total duration, tokens, errors, and billed usage for every request.

Separate cold starts and cold caches from warm behavior. If the provider includes a gateway or routing hop, test the complete path the application will use. The baseline becomes the reference for concurrency, optimization, and fallback tests.

Check output quality and schema conformance before increasing load. A test that produces invalid responses faster is not a successful baseline.

How should concurrency be increased?

Ramp concurrent requests in defined steps and hold each step long enough to observe queueing, autoscaling, and steady-state behavior. Record offered load and completed throughput separately. Stop when latency, error rate, or quality crosses the product threshold.

Run the serial baseline.
Test 2, 5, 10, and higher concurrent requests appropriate to the plan.
Hold each level for several minutes or a fixed request count.
Repeat with short, long, cold, and warm prompts.
Run a sustained soak at expected peak.
Inject a burst above peak and observe recovery.

Review documented rate and concurrency limits first. A 429 at the stated plan limit is a capacity-planning result, not necessarily a reliability defect.

How should failures and fallback be tested?

Trigger provider errors, latency timeouts, rate limits, malformed streams, and unavailable models in a controlled environment. Confirm whether the router retries, falls back, fails fast, or returns a partial response. Record every attempt and total user-visible delay.

Fallback models must pass the application’s tools, schemas, safety, and quality evaluations. A transport-successful response can still break the product if its structure or behavior differs. Test rollback to the known-good route and verify alerting.

For regional or dedicated deployments, simulate loss of a region or capacity pool when the provider supports that exercise.

Which metrics should be reported?

Metric	Report
TTFT	p50, p95, worst observed
Streaming pace	TPOT or inter-token latency
Throughput	Completed requests and tokens per second
Reliability	Completion, timeout, error, capacity, retry rates
Cache	Cold/warm split and reused input tokens
Quality	Acceptance and schema pass rate
Cost	Per request and completed interaction

Graph metrics against concurrency. An average hides the point at which the service begins queueing or failing.

What is the production launch gate?

Set pass/fail thresholds before the final test. Define acceptable p95 TTFT, completion rate, quality pass rate, peak concurrency, cost, and recovery time. Confirm dashboards, alerts, support contacts, status pages, credits, quotas, retention, regions, and rollback.

Expected peak passes for the full soak duration.
Burst traffic recovers without sustained backlog.
Fallback preserves required output behavior.
Cost remains inside the peak scenario.
Operators can identify and reverse a bad route or model.

Repeat the test after model, provider, routing, or major prompt changes. Launch readiness is configuration-specific.

Related Guides

Key Takeaways

Use the real prompt and output distribution, not identical synthetic requests.
Separate cold and warm behavior and report the complete routing path.
Ramp concurrency, hold steady state, run a soak, and inject a burst.
Test errors and fallback with quality and schema validation.
Define launch thresholds before the final run.

Frequently Asked Questions

How many requests are needed for a load test?

The count depends on variability and test goals. Use at least 20 serial requests per scenario for a basic distribution, then enough requests at each concurrency tier to reach steady state. Production decisions should include sustained soak and burst tests rather than relying on one short run.

What is a good requests-per-second result?

There is no universal target. The service must sustain the application’s expected peak with acceptable p95 latency, completion, quality, and cost. A higher raw RPS can be irrelevant when prompt lengths or model quality differ from production.

Should load tests run against production?

Begin in a controlled environment or dedicated test workspace. A limited production canary can validate real network and provider behavior after safeguards are in place. Coordinate high-load tests with the provider, respect limits, isolate customer data, and maintain a stop condition.