BigCommerce Load Testing: How to Performance Test BigCommerce Stores

Introduction

BigCommerce load testing is essential if you want to keep your storefront fast, your APIs responsive, and your checkout experience stable during traffic spikes. Whether you are preparing for a product launch, a seasonal promotion, or simply validating day-to-day performance, performance testing helps you find bottlenecks before customers do.

BigCommerce stores typically rely on a mix of storefront page rendering, cart operations, checkout flows, and API-driven integrations. That means a real-world load testing strategy should cover more than just the homepage. You need to validate category browsing, product detail pages, cart additions, storefront GraphQL behavior, and management API endpoints used by back-office tools or custom apps.

In this guide, you will learn how to load test BigCommerce stores with LoadForge using realistic Locust scripts. We will cover basic storefront traffic, authenticated API performance testing, and advanced checkout-oriented scenarios. Along the way, we will also look at how to interpret results and optimize your BigCommerce performance testing process using LoadForge’s distributed testing, real-time reporting, cloud-based infrastructure, and CI/CD integration.

Prerequisites

Before you start load testing BigCommerce, make sure you have the following:

• A BigCommerce store URL, such as https://store-example.mybigcommerce.com
• Access to your storefront and, if needed, a staging environment for safe testing
• A BigCommerce API account or app credentials for management API testing
• Optional Storefront API token if you want to test GraphQL storefront requests
• Sample product IDs, category URLs, and variant IDs for realistic traffic flows
• A LoadForge account to run distributed load tests at scale

You should also identify which environment you are testing:

• Production, for careful low-risk validation
• Staging, for aggressive stress testing
• Pre-release environments, for release validation in CI/CD pipelines

For BigCommerce specifically, gather these details before scripting:

• Store domain
• Store hash
• API token or OAuth access token
• Product IDs and variant IDs
• Category and search URLs
• Any custom storefront routes or headless frontend endpoints

Understanding BigCommerce Under Load

BigCommerce performance testing should reflect how the platform behaves under realistic concurrency. A BigCommerce store usually includes several performance-sensitive layers:

Storefront Rendering

Storefront traffic often includes:

• Homepage requests
• Category pages
• Product detail pages
• Search results pages
• Static assets like CSS, JavaScript, and images

Even if BigCommerce handles much of the platform infrastructure, your theme, third-party scripts, image sizes, and custom widgets can still create bottlenecks.

Cart and Checkout Operations

Cart and checkout flows are often the most business-critical part of load testing an e-commerce platform. Common stress points include:

• Add-to-cart requests
• Cart updates
• Shipping quote calculation
• Checkout creation
• Payment step latency

A store may appear healthy under browsing traffic but fail under cart-heavy or checkout-heavy workloads.

BigCommerce APIs

Many BigCommerce deployments rely on APIs for:

• Product synchronization
• Order processing
• Inventory updates
• Customer account features
• Headless storefronts

These calls often hit endpoints like:

• /stores/{store_hash}/v3/catalog/products
• /stores/{store_hash}/v3/orders
• /graphql

API performance testing is especially important if you have ERP, PIM, OMS, or mobile app integrations.

Common Bottlenecks in BigCommerce

When load testing BigCommerce stores, the most common issues are:

• Slow theme templates and oversized assets
• Excessive third-party scripts
• High latency on product and category pages
• Search performance degradation
• Cart and checkout instability under concurrency
• API rate limits or integration slowdowns
• Poor cache behavior on dynamic pages

A good BigCommerce load testing plan simulates a blend of anonymous shoppers, returning users, and administrative or integration traffic.

Writing Your First Load Test

Let’s start with a basic BigCommerce storefront load test. This script simulates users visiting the homepage, category pages, product pages, and search results. It is a strong baseline for storefront performance testing.

Basic BigCommerce Storefront Load Test

from locust import HttpUser, task, between
import random
 
class BigCommerceStorefrontUser(HttpUser):
    wait_time = between(2, 5)
 
    category_paths = [
        "/mens/",
        "/womens/",
        "/sale/",
        "/new-arrivals/"
    ]
 
    product_paths = [
        "/products/classic-logo-tee/",
        "/products/running-shoes-pro/",
        "/products/waterproof-jacket/",
        "/products/everyday-backpack/"
    ]
 
    search_terms = [
        "shirt",
        "shoes",
        "jacket",
        "backpack"
    ]
 
    @task(3)
    def view_homepage(self):
        self.client.get("/", name="Homepage")
 
    @task(4)
    def browse_category(self):
        path = random.choice(self.category_paths)
        self.client.get(path, name="Category Page")
 
    @task(5)
    def view_product(self):
        path = random.choice(self.product_paths)
        self.client.get(path, name="Product Detail Page")
 
    @task(2)
    def search_products(self):
        query = random.choice(self.search_terms)
        self.client.get(f"/search.php?search_query={query}", name="Search Results")

What This Test Covers

This script focuses on anonymous storefront traffic, which is often the largest share of e-commerce traffic. It helps answer questions like:

• Can the homepage handle concurrent shoppers?
• Do category and product pages remain fast under load?
• Does search degrade during traffic spikes?

Why This Matters for BigCommerce

BigCommerce storefront performance directly impacts conversion rates. Slow category and product pages can increase bounce rates, while slow search results can frustrate users trying to find products quickly.

In LoadForge, you can run this test from multiple global test locations to see how your BigCommerce storefront behaves for users in different regions. This is especially useful if your store serves a geographically distributed customer base.

Advanced Load Testing Scenarios

Once you have baseline storefront metrics, move on to more realistic BigCommerce performance testing scenarios.

Scenario 1: Add to Cart and Cart Retrieval

This script simulates product detail page visits followed by adding an item to the cart. For BigCommerce stores, cart operations are critical because they often expose issues that simple page browsing does not.

from locust import HttpUser, task, between
import random
import re
 
class BigCommerceCartUser(HttpUser):
    wait_time = between(1, 3)
 
    product_pages = [
        {
            "path": "/products/classic-logo-tee/",
            "product_id": 111,
            "variant_id": 201
        },
        {
            "path": "/products/running-shoes-pro/",
            "product_id": 112,
            "variant_id": 202
        },
        {
            "path": "/products/waterproof-jacket/",
            "product_id": 113,
            "variant_id": 203
        }
    ]
 
    cart_id = None
 
    @task(3)
    def browse_product(self):
        product = random.choice(self.product_pages)
        self.client.get(product["path"], name="Product Detail Page")
 
    @task(2)
    def add_to_cart(self):
        product = random.choice(self.product_pages)
 
        payload = {
            "lineItems": [
                {
                    "quantity": 1,
                    "productId": product["product_id"],
                    "variantId": product["variant_id"]
                }
            ]
        }
 
        with self.client.post(
            "/api/storefront/carts",
            json=payload,
            name="Create Cart / Add Item",
            catch_response=True
        ) as response:
            if response.status_code in [200, 201]:
                data = response.json()
                if "id" in data:
                    self.cart_id = data["id"]
                    response.success()
                else:
                    response.failure("Cart ID missing from response")
            else:
                response.failure(f"Unexpected status code: {response.status_code}")
 
    @task(1)
    def view_cart(self):
        if self.cart_id:
            self.client.get(f"/api/storefront/carts/{self.cart_id}", name="Get Cart")

Why This Scenario Is Important

A BigCommerce store may handle page views well but struggle when many users begin cart activity at once. This test helps validate:

• Cart creation response times
• Cart API stability
• Product and variant lookup correctness
• Session-related behavior under load

For realistic testing, use actual product and variant IDs from your store. If your storefront uses custom JavaScript or a headless frontend, adjust the requests to match the exact calls your application makes.

Scenario 2: Storefront GraphQL API Load Testing

Many BigCommerce implementations use the Storefront GraphQL API, especially in headless architectures. This example simulates users querying product data through /graphql.

from locust import HttpUser, task, between
import random
 
class BigCommerceGraphQLUser(HttpUser):
    wait_time = between(1, 4)
 
    def on_start(self):
        self.client.headers.update({
            "Authorization": "Bearer YOUR_STOREFRONT_API_TOKEN",
            "Content-Type": "application/json"
        })
 
    @task(4)
    def query_featured_products(self):
        payload = {
            "query": """
            query FeaturedProducts {
              site {
                products(first: 6, sortBy: FEATURED) {
                  edges {
                    node {
                      entityId
                      name
                      path
                      prices {
                        price {
                          value
                          currencyCode
                        }
                      }
                    }
                  }
                }
              }
            }
            """
        }
 
        self.client.post("/graphql", json=payload, name="GraphQL Featured Products")
 
    @task(3)
    def query_product_by_path(self):
        product_paths = [
            "/classic-logo-tee/",
            "/running-shoes-pro/",
            "/waterproof-jacket/"
        ]
        path = random.choice(product_paths)
 
        payload = {
            "query": """
            query ProductByPath($path: String!) {
              site {
                route(path: $path) {
                  node {
                    __typename
                    ... on Product {
                      entityId
                      name
                      sku
                      description
                      availabilityV2 {
                        status
                      }
                      prices {
                        price {
                          value
                          currencyCode
                        }
                      }
                    }
                  }
                }
              }
            }
            """,
            "variables": {
                "path": path
            }
        }
 
        self.client.post("/graphql", json=payload, name="GraphQL Product Detail")

What This Reveals

This test is useful for headless BigCommerce performance testing because it measures:

• GraphQL query latency
• Product data retrieval under concurrency
• Response consistency for dynamic frontend content
• API behavior during peak browsing traffic

If your storefront depends heavily on GraphQL, include this in your regular stress testing strategy. In LoadForge, you can scale this across many users to validate whether your frontend architecture remains responsive during campaigns or launches.

Scenario 3: BigCommerce Management API Testing

The Management API is commonly used by internal systems and integrations. This script tests catalog and order-related endpoints using an API token.

from locust import HttpUser, task, between
import random
 
class BigCommerceManagementApiUser(HttpUser):
    wait_time = between(2, 5)
 
    store_hash = "abc123xyz"
 
    def on_start(self):
        self.client.headers.update({
            "X-Auth-Token": "YOUR_BIGCOMMERCE_API_TOKEN",
            "Accept": "application/json",
            "Content-Type": "application/json"
        })
 
    @task(3)
    def list_products(self):
        self.client.get(
            f"/stores/{self.store_hash}/v3/catalog/products?limit=25&page=1&include=primary_image",
            name="Management API - List Products"
        )
 
    @task(2)
    def get_product(self):
        product_id = random.choice([111, 112, 113, 114])
        self.client.get(
            f"/stores/{self.store_hash}/v3/catalog/products/{product_id}",
            name="Management API - Get Product"
        )
 
    @task(1)
    def list_orders(self):
        self.client.get(
            f"/stores/{self.store_hash}/v2/orders?limit=20&page=1",
            name="Management API - List Orders"
        )
 
    @task(1)
    def update_product_inventory(self):
        product_id = random.choice([111, 112, 113])
 
        payload = {
            "inventory_level": random.randint(10, 100)
        }
 
        self.client.put(
            f"/stores/{self.store_hash}/v2/products/{product_id}",
            json=payload,
            name="Management API - Update Inventory"
        )

When to Use This Test

Use this scenario when you want to validate:

• Back-office integration performance
• Inventory sync throughput
• Product catalog API responsiveness
• Order retrieval latency under concurrent jobs

This is especially useful if your BigCommerce store integrates with external systems like ERPs, warehouses, CRMs, or custom admin tools.

When running this in LoadForge, consider separating storefront traffic and management API traffic into distinct user classes so you can simulate mixed workloads more accurately.

Analyzing Your Results

After you run your BigCommerce load testing scenarios, the next step is understanding what the numbers mean.

Key Metrics to Watch

For BigCommerce performance testing, focus on:

• Average response time
• 95th percentile response time
• Error rate
• Requests per second
• Throughput under peak concurrency

The 95th percentile is especially important because it shows the experience of slower requests, not just the average. In e-commerce, a few slow requests during checkout can have a direct revenue impact.

How to Interpret Storefront Results

If homepage requests are fast but product pages are slow, the issue may be:

• Theme complexity
• Dynamic product rendering
• Third-party widgets
• Large image payloads

If category pages degrade under load, investigate:

• Filter logic
• Search indexing
• Template rendering
• Collection size and pagination

How to Interpret Cart and Checkout Results

If cart creation or retrieval slows down, you may be seeing:

• Session handling overhead
• API bottlenecks
• Variant lookup inefficiencies
• Downstream service latency

For checkout-related failures, pay close attention to:

• Error spikes during high concurrency
• Long-tail latency
• Rate limiting
• Dependency timeouts

How to Interpret API Results

For Management API or GraphQL testing, look for:

• Increased latency as concurrency rises
• Non-200 responses
• Rate limit responses
• Performance differences between read-heavy and write-heavy operations

LoadForge’s real-time reporting makes it easy to watch these patterns as the test runs. You can also compare test runs over time, which is valuable when validating theme changes, app additions, or infrastructure updates in CI/CD workflows.

Performance Optimization Tips

Once your load testing identifies weak points, use these BigCommerce optimization tips to improve results.

Optimize Theme Performance

• Minimize render-blocking JavaScript
• Compress and resize images
• Reduce unnecessary app embeds and widgets
• Audit third-party scripts regularly

Improve Product and Category Pages

• Simplify template logic
• Limit heavy on-page dynamic content
• Use efficient pagination and filtering
• Review search and collection configuration

Reduce API Overhead

• Cache frequently requested data where possible
• Avoid redundant GraphQL queries
• Batch integration jobs when appropriate
• Use efficient field selection in GraphQL requests

Test with Realistic Traffic Mixes

Do not only test the homepage. Include:

• Browsing traffic
• Search traffic
• Product page traffic
• Cart operations
• API calls from integrations

Use Distributed Testing

BigCommerce stores often serve customers across regions. LoadForge’s cloud-based infrastructure and global test locations help you understand how latency and performance vary by geography.

Common Pitfalls to Avoid

BigCommerce load testing is most useful when it reflects real user behavior. Avoid these common mistakes.

Testing Only Static Pages

A homepage-only test will not tell you how your store behaves during add-to-cart surges or checkout spikes.

Ignoring Product Variants

Many BigCommerce stores rely on variants for size, color, or configuration. If your load test uses invalid variant IDs, add-to-cart failures may not reflect true platform performance.

Using Unrealistic API Patterns

Do not hammer a single endpoint with identical requests unless that matches production behavior. Real stores have a mix of reads, writes, search, and navigation traffic.

Testing Production Too Aggressively

Stress testing checkout or inventory endpoints in production can disrupt operations. Use staging environments for high-intensity tests whenever possible.

Forgetting Authentication Requirements

Storefront GraphQL and Management API endpoints often require valid tokens. Make sure your Locust scripts include the correct authentication headers and realistic request payloads.

Not Monitoring Error Details

A rising error rate is not enough on its own. Review response bodies and status codes to determine whether failures are caused by rate limiting, invalid payloads, timeouts, or application issues.

Conclusion

BigCommerce load testing helps you validate storefront speed, API reliability, and checkout stability before traffic spikes expose problems in production. By testing realistic user journeys such as browsing, searching, cart creation, and API-driven integrations, you can uncover performance issues that directly affect conversion rates and customer experience.

With LoadForge, you can run BigCommerce performance testing at scale using Locust-based scripts, distributed testing, real-time reporting, global test locations, and CI/CD integration. If you are serious about performance testing and stress testing your BigCommerce store, now is the perfect time to build your first scenario and see how your storefront performs under real load. Try LoadForge and start testing with confidence.