Load testing tools for LLM endpoints compared

When you put an LLM-backed service behind real user traffic, you need hard data on concurrency limits, token throughput, and degradation patterns. This LLM load testing tools comparison cuts through the marketing to examine five tools—k6, Locust, Gatling, Apache JMeter, and Artillery—against the specific demands of streaming chat completions and provider gateways.

Why LLM endpoints break ordinary load tests

Most load testing dogma assumes a request/response cycle with a single latency number. LLM inference flips that: you care about time-to-first-token (TTFT), inter-token latency, and total completion time. If you’re calling a gateway such as n4n.ai that fronts 240+ models with automatic fallback on provider degradation, a single virtual user can experience three different backends in one session. Your test harness must parse Server-Sent Events, not just check HTTP 200.

Token-based billing means a “request” can cost 10x another of same HTTP size. Throughput must be measured in tokens/sec, not requests/sec. Cache-control hints from providers add another axis: a warm prompt cache changes TTFT dramatically, so your test matrix needs both cache-hit and cache-miss variants.

The contenders

• k6: Go-based, JS scripting, designed for developers and CI.
• Locust: Python, coroutine-based, easy to read.
• Gatling: Scala/Java DSL, high-performance Netty engine.
• Apache JMeter: Java GUI, old-school, ubiquitous.
• Artillery: Node.js, YAML/JS, focused on modern APIs.

Dimensions that matter for LLM load

Capabilities

Streaming SSE is non-negotiable. k6 and Artillery have first-class support for reading chunks. Locust requires manual async handling with aiohttp. Gatling’s HTTP DSL can consume streams but needs custom code. JMeter’s HTTP sampler can capture streaming but its GUI model fights you.

Custom metrics are essential: you need to extract token counts from responses. k6 lets you write a custom counter; Locust uses Python dicts on the user instance; Gatling uses feeders and sessions. None compute tokens for you—you must count data: lines or decode JSON.

Price / cost model

k6 open-source is free; k6 Cloud is paid per VU-hour. Locust is free, but you pay for your own infra. Gatling open-source is free, enterprise per seat. JMeter is always free. Artillery open-source is free, Artillery Pro per run.

None of these charge per LLM token—that cost lands on your endpoint bill, which is why a cheap load tool can still trigger a surprise invoice from your model provider.

Latency & throughput

Measuring TTFT: timestamp first byte vs first token. k6 exposes responseStart and you parse the stream for the first data:. Locust’s start_time plus manual read works. For throughput, tokens/sec requires dividing total tokens by duration; only you know the tokenizer, so all tools offload this to script logic.

Ergonomics

k6 scripts are JavaScript, easy in CI with k6 run. Locust needs a Python env, but locust -f script.py is simple. Gatling compiles a DSL, heavier footprint. JMeter GUI is clicky, but version-controllable via XML (painful diffs). Artillery YAML is concise and readable.

Ecosystem

k6 has Grafana integration and extensions. Locust pipes to Prometheus via exporter. Gatling emits detailed HTML reports. JMeter has plugins for everything including WebSocket. Artillery hooks into Datadog and Slack.

Limits

k6 single-process handles ~10k VUs with enough RAM. Locust master/worker scales horizontally. Gatling handles high concurrency on few resources via Netty. JMeter struggles above ~1k threads on one box. Artillery similar to k6 but smaller extension community.

Comparison table

The following LLM load testing tools comparison table summarizes the head-to-head across the dimensions above.

Tool-by-tool notes

k6

The fastest path to a streaming test. Example hitting an OpenAI-compatible endpoint with experimental streams:

import http from 'k6/http';
import { check } from 'k6';

export const options = { vus: 50, duration: '2m' };

export default function () {
  const res = http.request('POST', 'https://api.example.com/v1/chat/completions',
    JSON.stringify({ model: 'gpt-4o', messages: [{ role: 'user', content: 'hi' }], stream: true }),
    { headers: { 'Content-Type': 'application/json', 'Authorization': 'Bearer '+__ENV.KEY },
      responseType: 'stream' }
  );
  // parse res.body stream for first 'data:' to compute TTFT
  check(res, { 'status 200': (r) => r.status === 200 });
}

k6’s responseType: 'stream' gives you chunked access. Pair with Grafana for token latency panels.

Locust

Pythonic, but you must use async code for streaming:

from locust import User, task, between
import aiohttp, asyncio, os

class LLMUser(User):
    wait_time = between(1, 5)
    @task
    async def chat(self):
        async with aiohttp.ClientSession() as s:
            async with s.post("https://api.example.com/v1/chat/completions",
                json={"model":"gpt-4o","messages":[{"role":"user","content":"load?"}],"stream":True},
                headers={"Authorization":f"Bearer {os.environ['KEY']}"}) as r:
                async for line in r.content:
                    if line.startswith(b"data:"):
                        pass  # count tokens

If you write sync requests code, you’ll block the event loop and crater throughput.

Gatling

DSL is expressive but you’ll write a custom stream handler in Scala. Not for quick PoCs, but unbeatable for sustained high VU on limited hardware.

JMeter

You can use a JSR223 sampler with Groovy to read the InputStream. But the GUI tempts you to click instead of commit. Avoid for LLM unless you already have JMeter infra and compliance needs.

Artillery

YAML makes scenario definition trivial:

config:
  target: "https://api.example.com"
  phases:
    - duration: 120
      arrivalRate: 50
scenarios:
  - name: "chat stream"
    request:
      method: POST
      url: "/v1/chat/completions"
      headers:
        Authorization: "Bearer {{ $processEnvironment.KEY }}"
      json:
        model: "gpt-4o"
        stream: true
        messages:
          - role: "user"
            content: "hello"

Artillery captures latency but token metrics need custom JS in beforeRequest/afterResponse.

Which to choose

Solo developer or startup PoC: k6. The JS syntax is approachable, CI integration is one command, and you can mock streaming with a small script. If you already live in Python, Locust is fine but expect to write async code.

CI gating for an LLM feature: k6 or Artillery. Both run headless, emit JSON, and integrate with GitHub Actions. k6’s Grafana dashboards are better if you already use that stack.

High-scale distributed test (50k+ VUs): Gatling or Locust master/worker. Gatling’s Netty core squeezes more from a single node; Locust scales horizontally with cheap workers.

Existing enterprise test team with JMeter: extend JMeter with Groovy samplers, but budget time for pain. It’s free and auditable, which some orgs require.

Testing a multi-provider gateway: If your endpoint is a gateway like n4n.ai that honors client routing directives and forwards cache-control, you’ll want a tool that lets you set per-request headers and parse fallback behavior. k6 or Artillery handle header injection cleanly; Locust needs manual sessions.

The LLM load testing tools comparison shows no single winner—match the tool to your stack and scale. For most teams shipping LLM features in 2025, k6 is the default; the others earn their place when you outgrow it or have legacy constraints.