The Hidden AI Bill: Why Non-Prod LLM Costs Spiral
Production AI spend gets attention. Non-prod LLM calls in development, CI, and load tests often do not. Simulation fixes that.
Latest insights on Agentic AI workflows, cloud native architectures, and performance optimization best practices from the Speedscale team.
Production AI spend gets attention. Non-prod LLM calls in development, CI, and load tests often do not. Simulation fixes that.
AI-generated code is moving fast—but without behavioral validation, you're gambling with production stability. See how Proxymock changes the equation.
Fast mode or deep mode? Haiku or Opus? Cursor or Claude Code? The decision fatigue from AI coding tools is killing the productivity they promised.
Most flaky test fixes focus on retries and quarantine. The real fix is replacing hand-written test data with recorded traffic that stays fresh.
Compare WireMock, MockServer, and proxymock for enterprise Java API mocking. See how each handles recorded traffic, Kubernetes, CI/CD, and AI-generated code.
Enterprise Spring Boot APIs need more than unit tests. Learn how to test external service integrations, JWT auth, and production edge cases with real traffic.
Learn how to capture, inspect, and archive encrypted microservice traffic with Speedscale's eBPF collector—no certificate management or code changes required.
How we built an AI agent that implements Jira tickets, creates merge requests, and monitors them autonomously—and the iterative journey to get there.
Speedscale launches proxymock as an OpenClaw skill on ClawHub, bringing traffic replay and production context to Claude for improved reliability.
Static analysis catches code smells. Runtime validation catches behavioral failures. Enterprise teams adopting AI coding tools need both to ship safely.