Operations | Monitoring | ITSM | DevOps | Cloud

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.

If you’re leading a technical organization, you’ll have a critical question when you want to perform uptime monitoring: should wse build and run an uptime monitor ourselves, or pay for a service? While it’s totally possible to build your own basic uptime monitor, you’ll end up costing your business more.

In today's connected world, APIs are the backbone of modern applications. Whether you're working on a microservices architecture, a mobile app, or a SaaS platform, APIs are what keep everything talking to each other. But how do you know if your APIs are healthy, performing well, and delivering what your users need? That's where API monitoring comes in. Let's break down what API monitoring is, why it matters, and how you can build effective API metrics dashboards to keep your systems running smoothly.

Generative AI is quickly becoming ubiquitous in the software development space, with tools like Anthropic’s Claude offering rapid methodologies for code iteration, testing, and deployment. As new solutions, such as MCP (Model Context Protocol), are created to make integration more seamless, enterprises are adopting these AI solutions to optimize their development processes, a familiar challenge repeatedly arises: cost.

Within the realms of microservices and distributed systems, gRPC has emerged as a cornerstone technology. Its adoption by tech giants like Google, Netflix, and Square underscores its capability to facilitate high-performance, scalable inter-service communication. Built as a modern take on the traditional Remote Procedure Call (RPC) paradigm, gRPC enables services, potentially written in different languages, to communicate efficiently and reliably across networks.

The Model Context Protocol (MCP) is rapidly becoming the connective tissue for agentic AI systems and IDE tooling. Whether you’re building a dev tool that integrates with LLMs or enabling a context-aware API backend, standing up an MCP server is a rite of passage. But MCP is still in its early days and there are some sharp edges. Here are four practical shortcuts to fast-track your MCP server development so you can skip the boilerplate and get to the good stuff: intelligent tooling.

APIs are the heart of most modern applications. Due to their simplicity and lightweight design, RESTful APIs are a popular choice for client-server communication in most applications. However, APIs can become limiting when fetching complex or related data. The front end may over-fetch or under-fetch the meaningful data. For example, different pages require different responses. RESTFul APIs require different field endpoints, each involving repetitive complex joining conditions.

Amazon Web Services, or AWS, offers a variety of cloud services ranging from AWS resources such as CDNs and data lakes to cloud computing and transformation services such as compute resources, virtual servers, and dynamic availability zones. For this reason, AWS cloud is one of the most broadly adopted cloud solutions, offering a global network of solutions at generally lower costs compared to on-premises solutions.

Enterprises lose millions annually due to API outages and performance degradation. Modern observability strategies are crucial to mitigate these risks. Today, almost every system is dependent on APIs. Data integration, authentication, payment processing, and many other functions rely on multiple reliable and performant APIs. Banks around the world, for example, have adopted the Open Banking API for payments, credit scoring, lending origination, fraud detection, and lots more.

If you're working in infrastructure or operations and looking to build reliable APIs, FastAPI might be the Python framework you need. This guide will help you understand how FastAPI can fit into your automation workflows and get you started with practical examples.

Kubernetes is built on the foundation of APIs and abstraction, and Calico leverages its extensibility to deliver network security and observability in both its commercial and open source versions. APIs are the special sauce that help automate and operationalize your Kubernetes platforms as part of a CI/CD pipeline and other GitOps workflows. Calico OSS 3.30, introduces numerous battle-tested observability and security tools from our commercial editions. This includes the following key features.

A well-crafted status page helps you build trust, reduce support tickets, and keep stakeholders informed in real time. But with so many tools available, which one should you choose? We’ve rounded up the top 10 status page tools — from legacy platforms to modern, dev-first options. Let’s dive in!

We're thrilled to announce that Checkly is now a launch partner in Vercel's Observability Marketplace! This integration makes it easier than ever for Vercel users to monitor their deployments with end-to-end testing and API monitoring powered by Checkly.

Google Cloud Platform, or GCP, is a cloud resources collection offered by Google for enterprise and standard users. GCP offers a wide range of cloud services, including compute, storage, networking, security, analytics, and even machine learning models. Google Cloud products are the backbone of many cloud applications. Google Cloud allows flexibility with the scalable and predictable cost management.

When applications experience performance issues, API latency is often a primary factor. For DevOps engineers, a clear understanding of API latency is essential for both resolving current performance problems and establishing preventative measures. This guide examines API latency from a technical perspective, covering its definition, measurement methodologies, and practical optimization techniques.

Checkly has always been your early warning system—giving engineering teams unmatched speed and precision in detecting problems through powerful synthetic monitoring. When systems fail, communicating clearly and quickly is just as important as fixing the issue itself. Downtime is inevitable. Confusion doesn’t have to be.