How to load test an LLM API (with code examples)

LLM-powered features are showing up everywhere — chatbots, search, code assistants, document summarizers. But the APIs behind them don't behave like traditional REST endpoints. Response times are unpredictable, costs scale with token volume, and a single streaming request can hold a connection open for seconds.

If you're responsible for an application that depends on LLM inference, you need to know how it performs under real user load — before your users find out for you. That's where load testing comes in.

This guide walks you through everything you need to load test an LLM API: the metrics that matter, the mistakes teams make, a step-by-step Gatling example with code, and how to analyze results so you can make informed capacity, cost, and SLA decisions.

Why LLM APIs need their own load testing strategy

Standard load testing approaches assume fast, predictable responses. LLM APIs break that assumption. You need a strategy that accounts for streaming, variable latency, token-based pricing, and rate limits.

How LLM inference differs from traditional API calls

A typical REST API call takes 50–200ms. An LLM completion can take 2–30 seconds depending on the model, prompt length, and output size. Here's what makes them different:

• Streaming responses: most LLM APIs use Server-Sent Events (SSE) to stream tokens incrementally, keeping connections open far longer than a standard request-response cycle
• Variable latency: response time depends on prompt complexity, output length, model size, and current provider load; the same request can take 500ms or 15 seconds
• Token-based costs: every request has a direct cost tied to input and output tokens, making runaway tests expensive
• Rate limiting: providers enforce token-per-minute and request-per-minute limits that interact with concurrency in non-obvious ways
• Queuing and cold starts: under load, requests queue behind GPU inference batches, creating latency spikes that don't appear in single-request tests

The cost of getting it wrong: UX, SLAs, and cloud bills

When LLM performance degrades under load, the impact is immediate:

• User experience: if your chatbot takes 5 seconds to start responding, users leave; interactive applications need a time to first token (TTFT) under 500ms
• SLA violations: enterprise customers expect p95 latency guarantees, and LLM variability makes these hard to meet without testing
• Unpredictable costs: a load test that fires 10,000 GPT-4o requests with long prompts can cost hundreds of dollars; without controls, a production traffic spike does the same

The gap between "works in development" and "works at 500 concurrent users" is where load testing LLM APIs earns its value.

Key metrics for LLM load testing

Traditional load testing metrics like average response time and requests per second don't capture the full picture for LLM APIs. You need metrics that reflect how users actually experience inference.

Time to first token (TTFT)

TTFT measures the delay between sending a prompt and receiving the first token back. It's the single most important metric for user-perceived performance in streaming applications.

• GPT-4o: ~464ms TTFT (source: Pendium.ai benchmark, March 2026)
• Claude Opus 4.7: ~850ms TTFT at p50
• Target for interactive chatbots: <500ms

If your TTFT exceeds 1 second under load, users will perceive the application as slow or broken.

Tokens per second (throughput)

Throughput measures how fast the model generates output once it starts. This determines how long users wait for complete responses.

• Cerebras running Qwen 3 235B: 525 tokens/sec (source: digitalapplied.com, April 2026)
• GPT-5.5: 92 tokens/sec
• Claude Opus 4.7: 78 tokens/sec

Higher throughput means shorter wait times for long-form outputs like summaries, code generation, and document drafting.

P50, p95, and p99 latency

Averages hide the worst experiences. Percentile-based latency tells you what your slowest users see:

• P50: the median experience; half your users are faster, half slower
• P95: the experience for 1 in 20 users; this is where SLOs and SLAs usually live
• P99: the tail; 1 in 100 users hit this latency, and it's often 3–5x the median for LLM APIs

Always test and report p95 and p99 alongside median. LLM inference has heavy tails — a p50 of 800ms can easily come with a p99 of 6 seconds.

Error rate under concurrency

Track the percentage of requests that fail (HTTP 429 rate limits, 500 errors, timeouts) as you increase concurrent users. A healthy LLM API should maintain an error rate below 1% at your target concurrency. Anything above that signals you're hitting provider limits, inference capacity walls, or infrastructure bottlenecks.

Cost per request

LLM APIs charge per token. Your load test needs to track the total token consumption and translate it to dollars. This metric feeds directly into capacity planning and helps you answer: "What will it cost to serve 10,000 daily active users?"

Common LLM load testing mistakes (and how to avoid them)

These are the mistakes we see teams make most often — and each one can invalidate your test results or blow your budget.

Using unrealistic test data

Sending the same short prompt ("Hello, how are you?") in every request doesn't reflect production traffic. Real users send prompts that vary widely in length, complexity, and domain.

What to do instead: Build a corpus of 50–100 prompts that represent your actual production distribution — short queries, long context windows, multi-turn conversations, and edge cases. Randomize prompt selection during the test.

Ignoring token cost implications

A load test that fires thousands of long-context requests can rack up significant API costs. Teams often discover this after the invoice arrives.

What to do instead: Estimate token costs before running the test. Start with short runs at low concurrency to validate your cost model. Use automated stop criteria to cap spending — Gatling Enterprise lets you set these as part of your simulation configuration.

Testing only happy-path scenarios

If you only test successful completions, you won't know how your system handles rate limits, malformed responses, or model timeouts.

What to do instead: Include scenarios that trigger 429 (rate limit), 503 (service unavailable), and timeout responses. Test what happens when the model returns truncated output or unexpected formats. Your error-handling code needs load testing too.

Neglecting streaming endpoint testing

Many teams test the non-streaming endpoint because it's simpler. But if your application uses streaming (and most production LLM apps do), you need to test the SSE endpoint.

What to do instead: Test the exact endpoint your production code calls. If you stream tokens to users, your load test must stream tokens too. Gatling supports SSE natively, which means you can simulate realistic streaming behavior without workarounds.

Overlooking rate limiting behavior

LLM providers enforce rate limits at multiple levels — requests per minute, tokens per minute, and sometimes concurrent connections. These limits interact with your load profile in ways that are hard to predict without testing.

What to do instead: Start your load test below your known rate limits and ramp gradually. Monitor 429 responses carefully. Map out the exact limits for your API tier and plan your concurrency accordingly.

Testing in isolation from other services

Your LLM API call probably isn't the only thing happening. There's a database lookup, a context retrieval step, maybe a vector search — all of which add latency and contend for resources.

What to do instead: Test your full request pipeline, not just the LLM call. Include upstream and downstream services in your simulation. This gives you realistic end-to-end latency numbers, not just inference time.

How to load test an LLM API with Gatling (step-by-step)

Let's walk through a concrete example: load testing the OpenAI Chat Completions API with streaming enabled using Gatling's Java SDK. The full documentation for this use case is available in the Gatling LLM API guide.

Setting up the project

Start with a standard Gatling project. If you're using Maven, Gradle, or npm, the Gatling dependencies are all you need. You can also write tests in JavaScript, TypeScript, Scala, or Kotlin — the concepts are the same.

Store your API key in an environment variable. Never hardcode secrets in test scripts.

Configuring the HTTP protocol for SSE

The HTTP protocol definition sets the base URL and configures SSE buffering. The sseUnmatchedInboundMessageBufferSize setting ensures Gatling can handle the stream of tokens coming back from the API:

HttpProtocolBuilder httpProtocol = http
   .baseUrl("https://api.openai.com/v1/chat")
   .sseUnmatchedInboundMessageBufferSize(100);


Defining the scenario with realistic prompts

The scenario sends a prompt to the streaming completions endpoint, processes incoming SSE messages until it sees the [DONE] signal, and then closes the connection:

import static io.gatling.javaapi.core.CoreDsl.*;
import static io.gatling.javaapi.http.HttpDsl.*;

public class SseLlmSimulation extends Simulation {
   String apiKey = System.getenv("API_KEY");

   HttpProtocolBuilder httpProtocol = http
       .baseUrl("https://api.openai.com/v1/chat")
       .sseUnmatchedInboundMessageBufferSize(100);

   ScenarioBuilder prompt = scenario("LLM Load Test")
       .exec(
           sse("Send prompt")
               .post("/completions")
               .header("Authorization", "Bearer " + apiKey)
               .body(StringBody(
                   "{\"model\":\"gpt-4o\",\"stream\":true,\"messages\":" +
                   "[{\"role\":\"user\",\"content\":\"Summarize the key benefits " +
                   "of load testing\"}]}"
               ))
               .asJson(),
           asLongAs("#{stop.isUndefined()}").on(
               sse.processUnmatchedMessages((messages, session) ->
                   messages.stream().anyMatch(m ->
                       m.message().contains("[DONE]"))
                       ? session.set("stop", true) : session
               )
           ),
           sse("Close").close()
       );

   {
       setUp(prompt.injectOpen(atOnceUsers(50)))
           .protocols(httpProtocol);
   }
}


This test injects 50 concurrent users at once, a good starting point to see how the API handles parallel inference requests.

Injecting concurrent users

Gatling gives you fine-grained control over load profiles. For LLM testing, consider these patterns:

• Ramp-up: rampUsers(100).during(60) gradually increases load over 60 seconds, helping you identify the concurrency threshold where latency degrades
• Burst: atOnceUsers(200) simulates a sudden spike, revealing queuing behavior and rate-limit responses
• Sustained: constantUsersPerSec(10).during(300) maintains steady load for 5 minutes to surface memory leaks and resource exhaustion

Start with ramp-up to find your limits, then validate with sustained and burst profiles.

Running the simulation and reading results

Run the simulation locally or on Gatling Enterprise for distributed, multi-region execution. After the run, you'll get a detailed HTML report (locally) or interactive dashboards (on Enterprise) showing:

• response time distribution (p50, p75, p95, p99)
• requests per second over time
• error breakdown by status code
• active users over time

Look for latency spikes that correlate with concurrency increases, that's where the system starts to struggle.

Why Gatling for LLM load testing

Gatling started as an open-source project and is used by thousands of engineering teams for load testing. That same foundation — including native SSE support — is what makes it a strong fit for LLM API testing.

Test-as-code in JavaScript, TypeScript, Java, Scala, or Kotlin

Write your tests in the language your team already uses. Version them in Git, review them in PRs, and run them in CI — just like application code. No proprietary scripting languages or GUI-only workflows. The LLM example above took about 30 lines of Java. The same test works in JS/TS with nearly identical syntax.

Real-world concurrency simulation

Gatling uses an asynchronous, non-blocking architecture. In practice, a single Gatling instance can maintain thousands of concurrent SSE connections — each virtual user holds its own streaming session with the LLM endpoint, just like a real user would.

Performance dashboards and regression tracking

After each run, Gatling generates detailed HTML reports locally. On Enterprise, you get interactive dashboards that capture every request at full resolution. You can compare runs side by side, set up automated regression alerts, and share results with stakeholders — useful when your VP asks "did last week's model change affect latency?"

Cost controls and quota management

You can set automated stop criteria to cap test duration, request count, or error rate. If your org has multiple teams running LLM load tests, Enterprise lets you set per-team quotas so one team's stress test doesn't eat the entire API budget.

CI/CD integration for continuous performance testing

Plug Gatling into Jenkins, GitHub Actions, GitLab CI, or any pipeline that runs Maven, Gradle, or npm. Run LLM load tests on every deployment to catch performance regressions before they reach production. Load testing vs. performance testing aren't the same, but with Gatling in your pipeline, you can do both.

How to analyze your LLM load test results

Running the test is only half the work. Here's how to extract actionable insights from your results.

Response time distribution analysis

Don't look at averages. Pull up the percentile distribution:

• If p50 is 800ms but p99 is 8 seconds, you have a tail latency problem — likely caused by request queuing on the inference side
• If TTFT degrades linearly with concurrency, you're hitting inference throughput limits
• If latency is stable up to a threshold then jumps sharply, you've found your rate limit ceiling

Plot response times against concurrent users to visualize the breaking point.

Throughput and capacity planning

Use your test data to answer practical capacity questions:

• How many concurrent users can you support while keeping p95 TTFT under 1 second?
• At your target user count, what's the total token throughput (tokens/sec)?
• How does throughput scale if you add a second model endpoint or enable request routing?

These numbers feed directly into infrastructure and budget decisions.

Error pattern identification

Group errors by type and timing:

• 429 errors: you're exceeding rate limits; reduce concurrency or request a higher tier
• Timeout errors: inference is too slow under load; consider shorter prompts, a faster model, or request queuing
• 5xx errors: the provider or your middleware is failing; investigate logs and retry logic
• Truncated responses: output hit the max token limit; adjust max_tokens or your prompt design

Cost correlation

Map total token consumption to dollars for each test run. Break it down by:

• input tokens vs. output tokens (pricing differs)
• cost per concurrent user
• cost at projected daily traffic

If your test shows $0.12 per request at scale, and you expect 50,000 requests per day, that's $6,000/day. This math informs model selection, caching strategy, and architecture decisions.

Resource utilization patterns

If you're running self-hosted models or inference proxies, correlate your load test with system metrics:

• GPU utilization: are you saturating inference capacity?
• Memory: are model weights being paged in and out?
• Network: is bandwidth a bottleneck for streaming responses?

Gatling Enterprise integrates with Datadog and Dynatrace to overlay infrastructure metrics on your test results.

LLM load testing vs. other tools

Choosing the right tool depends on your team's needs. Here's how Gatling compares to common alternatives.

Gatling vs. k6 for LLM APIs

k6 is a solid tool for HTTP load testing. Its SSE support currently relies on community extensions, which means testing streaming LLM endpoints may require additional setup. Gatling has native SSE support built into the core framework, which simplifies streaming inference tests. For reporting, Gatling generates detailed HTML reports locally and offers enterprise dashboards; k6 pairs with Grafana Cloud for similar capabilities.

Gatling vs. Locust for LLM APIs

Locust is Python-based, which appeals to ML teams already working in that ecosystem. Its single-threaded event loop handles moderate concurrency well, though it can become a bottleneck when simulating thousands of long-lived SSE connections. Gatling's JVM-based architecture uses asynchronous I/O to maintain high concurrency with lower resource overhead. If your team works primarily in Python and your test scale is moderate, Locust is a reasonable choice. For larger-scale LLM load tests, Gatling's architecture is designed for that workload.

Purpose-built LLM benchmarking tools (GenAI-Perf, llm-load-test)

Tools like NVIDIA's GenAI-Perf and llm-load-test are designed specifically for model benchmarking — comparing inference speed across models and hardware configurations. They excel at that narrow use case. Where they differ from Gatling is scope: they typically don't test your full application stack, and they're not designed for CI/CD integration or long-term regression tracking. Many teams use both — a benchmarking tool for model evaluation, and Gatling for production-grade load testing of the complete system.

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 Next steps

LLM APIs demand testing strategies that account for streaming, variable latency, token costs, and rate limits. The teams that test early and test continuously are the ones that ship reliable, cost-predictable AI features — and the ones that can give stakeholders clear answers about capacity, SLA compliance, and per-user cost.

If you're evaluating how to standardize LLM load testing across your organization, Gatling Enterprise adds role-based access control, per-team quota management, and centralized dashboards so multiple teams can run tests without stepping on each other's budgets or infrastructure.

Request a demo to see Enterprise in action.

Want to start testing today? Download Gatling open-source — the Community Edition includes everything you need to run the LLM load test example in this guide.

🔗 Read the full guide in the docs→

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