Systems | Development | Analytics | API | Testing

NGINX with OpenResty offers unmatched performance for serving APIs (application programming interfaces) at scale, with the added benefits of the open-source ecosystem. It’s fast, flexible, and production-proven—an ideal choice for scalable web platforms and high-throughput APIs. But even the most reliable platform can leave teams blind to what matters: real-time API usage, user behavior, and production errors.

Usage-based pricing has long been the default for APIs—straightforward to implement and easy for customers to understand. You charge based on consumption: API calls, compute time, or data volume. It is predictable, measurable, and scales well with usage. But as APIs become more intelligent—especially in AI-driven platforms—raw consumption no longer remains a reliable proxy for customer value. A user can rack up thousands of API calls and still achieve nothing meaningful.

To incorporate the keywords like "HTTP 403," "HTTP error 503," "the service is unavailable," and "monitor Google Cloud API traffic" into the blog, I would recommend integrating them naturally into the content. Additionally, for internal linking from Keploy.io’s website blogs, here’s a possible update to your blog, integrating the mentioned keywords and linking.

Anyshift provides AI context for your infrastructure, powered by Annie—an AI infrastructure assistant trained on your environment. From answering complex infrastructure questions to suggesting Terraform code and catching hidden issues, Annie helps teams manage, monitor, and optimize their infrastructure with ease and precision. Unlike generic AI copilots, Anyshift provides context-aware insights based on your actual infrastructure and codebase—not just LLM guesses.

Emerging technologies like generative AI (GenAI) and agentic AI are poised to significantly enhance IT operations. These advancements offer new, truly transformative ways to manage, optimize and automate IT environments, and are certain to improve efficiency and foster innovation. GenAI’s ability to process vast amounts of unstructured data and agentic AI’s autonomous decision-making capabilities span predictive analytics to automated problem-solving.

BigQuery ML adds TimesFM, LLM-based structured output generation, row-wise inference functions, expanded model choice, and Contribution Analysis GA.

When I first started writing Go software a little over a decade ago, one of the features I found particularly intriguing was the ability to build statically-linked binaries for multiple operating systems and architectures without a lot of headache. This build toolchain feature is widely relied upon by nearly all Go developers, especially when needing to build multi-arch container images destined to be run in a Kubernetes cluster consisting of amd64 and/or arm64 nodes.

Your organization may be one of the many that is rushing to implement AI. But do you know where you fall on the AI maturity model? More than just a framework for understanding AI, the AI maturity model is a strategic guide that helps turn AI investments into tangible business results. A 2024 IDC study commissioned by Microsoft titled “The Business Opportunity of AI” found that organizations gain a $3.7x return for every $1 spent on generative AI.

Kong has supported Redis since its early versions. In fact, the integration between Kong Gateway and Redis is a powerful combination to enhance API management. We can summarize the integration points and use cases of Kong and Redis into three main groups.