Load Testing Tools: k6, Locust, and Artillery for Performance Testing

Load testing answers the question: "How does this system behave when N users hit it simultaneously?" Without it, you find out the answer in production — usually during a product launch or viral moment, at the worst possible time.

The three tools covered here — k6, Locust, and Artillery — are the most actively maintained open-source options in 2026. Each has a different scripting model and integration story.

---

What to Measure

Before writing a single test script, define what "good" means for your system:

The saturation point is the most important finding from a load test. Every system has one — the point where adding more load causes disproportionate latency increases. Your headroom above current peak traffic tells you how long until you need to scale.

---

k6

k6 (by Grafana Labs) is JavaScript-scripted, integrates with CI/CD, and has excellent output formatting. It's the most popular choice for teams already in the JavaScript ecosystem.

Basic k6 script:

// load-tests/api-checkout.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Trend, Rate, Counter } from 'k6/metrics';

// Custom metrics
const checkoutDuration = new Trend('checkout_duration', true);
const checkoutErrors = new Rate('checkout_errors');
const ordersCreated = new Counter('orders_created');

// Test configuration
export const options = {
  stages: [
    { duration: '2m', target: 10 },   // Ramp up to 10 VUs over 2 minutes
    { duration: '5m', target: 50 },   // Ramp up to 50 VUs
    { duration: '10m', target: 50 },  // Hold at 50 VUs for 10 minutes
    { duration: '3m', target: 100 },  // Spike to 100 VUs
    { duration: '5m', target: 100 },  // Hold the spike
    { duration: '2m', target: 0 },    // Ramp down
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'],        // 95% of requests < 500ms
    http_req_failed: ['rate<0.01'],          // Error rate < 1%
    checkout_duration: ['p(95)<2000'],       // Checkout flow < 2s at p95
    checkout_errors: ['rate<0.005'],         // Checkout errors < 0.5%
  },
};

const BASE_URL = __ENV.BASE_URL || 'https://api.yourdomain.com';
const API_TOKEN = __ENV.API_TOKEN;

export function setup() {
  // Runs once before all VUs — use for test data setup
  return {
    products: [
      { id: 'prod_001', price: 1999 },
      { id: 'prod_002', price: 4999 },
    ],
  };
}

export default function (data) {
  const headers = {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${API_TOKEN}`,
  };

  // Step 1: Get product list
  const productsRes = http.get(`${BASE_URL}/products?limit=10`, { headers });
  check(productsRes, {
    'products 200': (r) => r.status === 200,
    'products has items': (r) => r.json('data').length > 0,
  });

  sleep(1);  // Think time between actions

  // Step 2: Add to cart
  const product = data.products[Math.floor(Math.random() * data.products.length)];
  const cartRes = http.post(
    `${BASE_URL}/cart`,
    JSON.stringify({ productId: product.id, quantity: 1 }),
    { headers }
  );
  check(cartRes, { 'cart 201': (r) => r.status === 201 });

  sleep(2);

  // Step 3: Checkout (the critical path)
  const checkoutStart = Date.now();
  const checkoutRes = http.post(
    `${BASE_URL}/orders`,
    JSON.stringify({
      cartId: cartRes.json('id'),
      paymentMethod: 'pm_card_visa',  // Stripe test card
    }),
    { headers, timeout: '10s' }
  );

  const checkoutMs = Date.now() - checkoutStart;
  checkoutDuration.add(checkoutMs);

  const checkoutOk = check(checkoutRes, {
    'checkout 201': (r) => r.status === 201,
    'checkout has orderId': (r) => r.json('orderId') !== undefined,
  });

  checkoutErrors.add(!checkoutOk);
  if (checkoutOk) ordersCreated.add(1);

  sleep(3);
}

Running k6:

# Local run
k6 run load-tests/api-checkout.js

# With environment variables
BASE_URL=https://staging.yourdomain.com API_TOKEN=xxx k6 run load-tests/api-checkout.js

# Output to InfluxDB for Grafana visualization
k6 run --out influxdb=http://localhost:8086/k6 load-tests/api-checkout.js

# CI integration — fails if thresholds are breached
k6 run --exit-on-running --out json=results.json load-tests/api-checkout.js

GitHub Actions CI integration:

# .github/workflows/load-test.yml
name: Load Test (Staging)

on:
  workflow_dispatch:  # Manual trigger
  schedule:
    - cron: '0 2 * * 1'  # Every Monday at 2 AM

jobs:
  load-test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: grafana/setup-k6-action@v1
        with:
          k6-version: 0.50.0
      - name: Run load tests
        run: k6 run load-tests/api-checkout.js
        env:
          BASE_URL: ${{ secrets.STAGING_URL }}
          API_TOKEN: ${{ secrets.STAGING_API_TOKEN }}
      - name: Upload results
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: load-test-results
          path: results.json

---

Locust

Locust is Python-based, which makes it natural for teams with Python expertise. Its distributed mode scales to millions of simulated users by adding worker nodes.

Locust test script:

# locustfile.py
from locust import HttpUser, task, between, events
from locust.exception import StopUser
import random
import json

class APIUser(HttpUser):
    wait_time = between(1, 3)  # Think time between tasks
    token = None

    def on_start(self):
        """Called when a simulated user starts — authenticate"""
        response = self.client.post("/auth/login", json={
            "email": f"testuser{random.randint(1, 1000)}@loadtest.com",
            "password": "TestPassword123!"
        })
        if response.status_code != 200:
            raise StopUser()
        self.token = response.json()["token"]
        self.headers = {"Authorization": f"Bearer {self.token}"}

    @task(3)  # Weight: this runs 3x more often than task weight 1
    def browse_products(self):
        with self.client.get(
            "/products",
            params={"page": random.randint(1, 5), "limit": 20},
            headers=self.headers,
            name="/products [list]",  # Group similar URLs in results
            catch_response=True
        ) as response:
            if response.status_code == 200:
                data = response.json()
                if len(data.get("data", [])) == 0:
                    response.failure("Empty product list")
            else:
                response.failure(f"HTTP {response.status_code}")

    @task(1)
    def create_order(self):
        # Step 1: Get a product
        products_res = self.client.get("/products?limit=5", headers=self.headers)
        if products_res.status_code != 200:
            return

        products = products_res.json().get("data", [])
        if not products:
            return

        product = random.choice(products)

        # Step 2: Create order
        with self.client.post(
            "/orders",
            json={"productId": product["id"], "quantity": 1},
            headers=self.headers,
            name="/orders [create]",
            catch_response=True
        ) as response:
            if response.status_code == 201:
                response.success()
            else:
                response.failure(f"Order creation failed: {response.status_code}")

# Run: locust -f locustfile.py --host=https://staging.yourdomain.com
# Web UI at http://localhost:8089 to configure and start the test

Distributed Locust for high load:

# Start master
locust -f locustfile.py --master --host=https://staging.yourdomain.com

# Start workers (on separate machines or containers)
locust -f locustfile.py --worker --master-host=master.internal

# Headless distributed run
locust -f locustfile.py \
  --master \
  --headless \
  --users 1000 \
  --spawn-rate 50 \
  --run-time 10m \
  --host https://staging.yourdomain.com \
  --html report.html

---

Artillery

Artillery is YAML/JSON-configured (no scripting required for basic tests) but supports JavaScript plugins for complex scenarios. Best for teams that want tests-as-config rather than tests-as-code.

Artillery config (load-test.yml):

config:
  target: "https://staging.yourdomain.com"
  phases:
    - duration: 60
      arrivalRate: 5
      name: "Warm up"
    - duration: 300
      arrivalRate: 50
      name: "Sustained load"
    - duration: 60
      arrivalRate: 200
      name: "Spike"
  defaults:
    headers:
      Content-Type: "application/json"
  plugins:
    expect: {}  # Response validation plugin

scenarios:
  - name: "User journey: Browse and purchase"
    weight: 70  # 70% of users follow this flow
    flow:
      - post:
          url: "/auth/login"
          json:
            email: "{{ $randomString() }}@test.com"
            password: "TestPass123!"
          capture:
            - json: "$.token"
              as: "authToken"
      - get:
          url: "/products"
          qs:
            limit: 10
          headers:
            Authorization: "Bearer {{ authToken }}"
          expect:
            - statusCode: 200
            - contentType: json
      - think: 2
      - post:
          url: "/orders"
          json:
            productId: "prod_001"
            quantity: 1
          headers:
            Authorization: "Bearer {{ authToken }}"
          expect:
            - statusCode: 201

  - name: "Browse only"
    weight: 30
    flow:
      - get:
          url: "/products"
      - think: 3
      - get:
          url: "/products/{{ $randomString() }}"

# Run Artillery
artillery run load-test.yml

# Run with HTML report
artillery run --output results.json load-test.yml
artillery report results.json

---

Tool Comparison

Quick decision:

• JavaScript team, CI-first → k6
• Python team, need distributed → Locust
• Want YAML config, quick start → Artillery

---

Performance Baselines by Tier

---

Working With Viprasol

We run load tests as part of production readiness reviews — before major launches, infrastructure changes, or traffic events. Our test suites cover the critical user journeys, include realistic think times, and produce reports that tell you exactly where the saturation point is and what to do about it.

→ Talk to our team about performance testing your application.

---

See Also

• Software Testing Strategies — where load testing fits in the test pyramid
• PostgreSQL Performance — diagnosing DB bottlenecks found in load tests
• Caching Strategies — the fastest fix for load test findings
• Kubernetes vs ECS — auto-scaling to handle load test peaks
• Cloud Solutions — infrastructure sizing and scaling