Cost-Effective Performance Testing for a Healthcare Mobile App (Android & iOS) Using RedLine13 and JMeter at QualiTlabs
Challenge
● The app needed to support thousands of concurrent users efficiently.
● REST and GraphQL APIs required extensive performance testing to ensure optimal speed
and reliability.
● The client requested a cost-effective solution that minimized cloud infrastructure expenses
while ensuring comprehensive testing.
● The app needed to support thousands of concurrent users efficiently.
● REST and GraphQL APIs required extensive performance testing to ensure optimal speed
and reliability.
● The client requested a cost-effective solution that minimized cloud infrastructure expenses
while ensuring comprehensive testing.
Solution
At QualiTlabs, we used RedLine13 and JMeter integrated with AWS to perform large-scale performance testing efficiently and cost-effectively. Leveraging AWS, we simulated thousands of users without extra hardware, ensuring scalability and real-world accuracy.
We measured key metrics including response times (REST & GraphQL APIs), percentiles (90th–99th), throughput, error rates, CPU/memory usage, and latency to identify bottlenecks and optimize performance.
Using AWS’s pay-as-you-go model and JMeter’s open-source power, we minimized costs while achieving enterprise-grade results. Tests revealed slower REST APIs for large datasets and database-related bottlenecks.
We recommended query optimization, caching, and GraphQL refinement to enhance performance. Final reports provided actionable insights to improve scalability, reliability, and end-user experience.
At QualiTlabs, we used RedLine13 and JMeter integrated with AWS to perform large-scale performance testing efficiently and cost-effectively. Leveraging AWS, we simulated thousands of users without extra hardware, ensuring scalability and real-world accuracy.
We measured key metrics including response times (REST & GraphQL APIs), percentiles (90th–99th), throughput, error rates, CPU/memory usage, and latency to identify bottlenecks and optimize performance.
Using AWS’s pay-as-you-go model and JMeter’s open-source power, we minimized costs while achieving enterprise-grade results. Tests revealed slower REST APIs for large datasets and database-related bottlenecks.
We recommended query optimization, caching, and GraphQL refinement to enhance performance. Final reports provided actionable insights to improve scalability, reliability, and end-user experience.
Results
Through QualiTlabs’ integration of RedLine13 with AWS and the use of JMeter, we provided the
client with a scalable and cost-effective performance-testing solution.
This approach allowed the healthcare provider to:
● Identify performance bottlenecks and optimize their system for scalability.
● Ensure their app can handle high user traffic during peak times.
● Minimize costs through cloud-based infrastructure and pay-as-you-go pricing without
compromising the quality or depth of the analysis.
Through QualiTlabs’ integration of RedLine13 with AWS and the use of JMeter, we provided the
client with a scalable and cost-effective performance-testing solution.
This approach allowed the healthcare provider to:
● Identify performance bottlenecks and optimize their system for scalability.
● Ensure their app can handle high user traffic during peak times.
● Minimize costs through cloud-based infrastructure and pay-as-you-go pricing without
compromising the quality or depth of the analysis.