Speedscale provides a platform that enables developers to create realistic local development environments by simulating production conditions and generating synthetic test data. This approach addresses the challenge of inadequate staging environments and helps teams stress test cloud services using real user behavior, improving system reliability and reducing cloud costs.
Funding
$12M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
SSTSFounders
Product
Problem
Development teams face challenges in creating realistic testing environments that accurately mimic production conditions, leading to inadequate staging environments and difficulties in stress-testing cloud services. This results in unreliable systems, increased cloud costs, and delays in identifying defects before deployment.
Solution
Speedscale provides a platform that enables developers to simulate production conditions on their laptops, facilitating the creation of realistic local development environments. The platform transforms user behavior into API traffic snapshots, which are then used to automatically generate mocks and tests. By capturing and replaying traffic, Speedscale allows developers to explore environment-specific APIs and databases, reducing cloud bills and autogenerating mocks tailored to their needs. The platform integrates with CI/CD pipelines, enabling automated production traffic validation and continuous performance testing with every code commit.
Target Audience
Speedscale is designed for development teams, API engineers, and software engineers who need to create realistic testing environments, perform load testing, and ensure the reliability of their cloud services.
Features
- Traffic capture and analysis via sidecar, Postman collection, or logs
- Automated redaction of PII from API calls
- Generation of traffic snapshots containing tests and mocks
- Traffic replay to validate code changes against realistic production environments
- Service discovery and mocking to explore API and database dependencies
- Synthetic data generation to create specific data conditions and augment production-like schemas
- Kubernetes operator for quick and automated setup in any cloud or on a laptop
- Integration with CI/CD pipelines for automated production traffic validation