API Load Testing Tool
How It Works
PFLB API load testing tool enables you to simulate real-world traffic and observe your API’s behavior with just three simple steps.
Define the API Load Testing Scenario
Configure and Run Your API Stress Test
Benefit from our powerful, scalable cloud infrastructure capable of generating immense loads from 18 geo-locations, ensuring your application is ready for global high-traffic deployment.
Configure and Run Your API Load Test
Benefit from our powerful, scalable cloud infrastructure capable of generating immense loads from 18 geo-locations, ensuring your application is ready for global high-traffic deployment.
Discover Your API Load Testing Results
Utilize PFLB’s custom Grafana dashboards to meticulously analyze your API’s behavior under heavy load. Gain comprehensive insights into performance metrics and identify bottlenecks.
More Reasons to Love PFLB
User-Friendly
Comprehensive Support
Detailed Documentation
PFLB Client’s Review
Integrations with Your Favourite Tools
- Postman
Load test Postman request collections.
- Insomnia
Import and load test Insomnia request collections.
- Grafana
The integration with Grafana provides clear and actionable test reports that help you pinpoint bottlenecks in your system.
FAQs
Dive in Our Knowledge Base
01How to Stress Test my REST API?
To stress test your REST API using PFLB, define your API requests manually with our no-code script builder or import collections from Postman or Insomnia. Configure the load generator locations, manage RPS (requests per second), test duration, and ramp-up. Run the test and analyze the results with PFLB’s custom Grafana dashboards for detailed insights.
02How to Choose API Methods for API Load Testing?
Select API methods that are critical to your application’s performance and user experience. Focus on endpoints with high traffic, complex operations, or those that handle large data volumes. Using PFLB, you can easily define and test these methods to identify potential performance bottlenecks and ensure your API can handle real-world load conditions.
03How to Build a New PFLB Load Testing Script Based on an Existing Postman API Request Collection?
To build a new load testing script in PFLB based on an existing Postman API request collection, follow these steps:
- Export your API request collection from Postman.
- Log in to PFLB and navigate to the script builder.
- Import the Postman collection into PFLB.
- Configure and parameterize the imported requests as needed.
- Set up your test plan, including load generator locations, RPS, test duration, and ramp-up.
- Run the test and monitor the results using PFLB’s custom Grafana dashboards.
04Can I Simulate Traffic from Different Geo Locations?
Yes, with PFLB, you can simulate traffic from different geographical locations. This allows you to test your API’s performance and latency across various regions, ensuring it can handle global traffic efficiently. Simply configure the load generator locations within the PFLB platform to distribute the load as needed.
05What Types of API Does PFLB Support?
PFLB supports a wide range of API types, including RESTful APIs, SOAP, and gRPC. This versatility allows you to test various API architectures and ensure their performance under different load conditions.
06How to Load Test Postman Collections?
To load test your Postman collection follow these steps:
- Export the Collection: Open Postman, select the collection you want to load test, and export it as a JSON file.
- Import into PFLB: Go to the script builder interface and import the exported Postman JSON file. PFLB will automatically create the necessary requests and scenarios.
- Configure Load Test Parameters: Set up the load test parameters within PFLB. Specify the number of virtual users, test duration, and any other custom settings required for your testing scenario.
- Run the Load Test: Start the load test and monitor its progress in real-time using PFLB’s dashboard.
- Analyze Results: After the test is complete, analyze the results using PFLB’s custom Grafana reports. Examine key performance metrics such as response times, error rates, and throughput to identify any performance issues.