Intelligent caching and model routing reduced our AI API costs from $12,340 to $3,680 per month. Production-tested optimizer. Open source. MIT license.
Most edge computing remains cloud-dependent, with genuine use cases limited to strict latency or connectivity needs — making it more marketing than architecture.
End-to-end testing fails in microservices due to non-determinism, complex environments, slow feedback, and unclear ownership, making tests flaky and unreliable.
Vector search is not "just OpenSearch." It just needs to be run as a platform with SLAs, governance, and quotas to control drift, leaks, and out-of-control costs.
Explore Google Gemini 3 API’s architecture, native multimodality, and agentic workflows with a hands-on guide to building a production-ready multimodal AI.
How cloud-native microservices transform insurance analytics by enabling scalability, real-time processing, and seamless modernization of legacy platforms.
A clear-eyed breakdown of serverless costs — why they’re hidden, when they make sense, and how to choose between functions and containers before surprises hit your bill.