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When discussing the process of testing an API, one of the most common sets of terms you might encounter are “mocks” and “stubs.” These terms are quite ubiquitous, but understanding exactly how they differ from one another - and when each is the correct method for software testing - is critical to building an appropriate test and validation framework. In this blog, we’re going to talk about the differences and similarities between mocks and stubs.

Every enterprise depends on continuous changes to its IT environment. New code releases, infrastructure updates, configuration changes, and security patches are all crucial to support continuous innovation. These same changes are also a leading source of operational risk and one of the most common causes of failures at the network, infrastructure, and software layers, resulting in outages.

VictoriaMetrics is a fast, cost-efficient, and highly scalable time-series database designed as a drop-in replacement for Prometheus storage. It is widely used for collecting, storing, and querying metrics at scale, while remaining lightweight enough to run as a single binary or container. Because it is fully Prometheus-compatible, VictoriaMetrics supports standard PromQL queries and integrates seamlessly with Grafana.

In today's global enterprises, distributed infrastructure is the norm, not the exception. Organizations operate across continents and are driven by customer proximity and regulatory requirements. For the Elastic Stack, this reality often translates into a multi-cluster deployment model, where data is collected and stored in multiple geographically dispersed Elasticsearch clusters. But, why adopt complexity? The decision to decentralize data storage is generally driven by three critical factors.

Infrastructure code rarely stays clean on its own. Teams move fast, and reviews aren’t always deep or consistent. Over time, misconfigurations build up and increase the risk of outages, security gaps, or unpredictable behavior. Static scanning tools can help, but they often require setup, expertise, and don’t always reflect how infrastructure code is actually used across environments. Code Optimizer, now in beta, helps teams catch those issues earlier.

Harness has been recognized by TechStrong Group for its comprehensive, AI-native platform vision, winning Best End-to-End DevOps Platform, Best Platform Engineering Solution, and DevOps Industry Leader of the Year. At Harness, our mission has always been simple but ambitious: to enable every software engineering team in the world to deliver code reliably, efficiently, and quickly to their users, just like the world’s leading tech companies.

This article is part of our series on how Datadog’s engineering teams use LLM Observability to iterate, evaluate, and ship AI-powered agents. In this first story, the Graphing AI team shares how they instrumented their widget- and dashboard-generation agents with LLM Observability to detect regressions and debug failures faster. Visibility into how large language model (LLM) applications behave in real time is essential for building reliable AI-driven systems at Datadog.

As we move further into 2026, we wanted to pause for a moment and reflect on what the past year looked like for OnPage, not just in terms of features shipped, but in how the platform evolved to better support the way teams actually work in high-stakes environments. 2025 was a foundational year for us.

Modern, hybrid environments change continuously. But, legacy ITOps workflows assume stable infrastructure. IT environments don’t behave in predictable ways. Infrastructure changes continuously, services spin up and shut down on demand, and data formats evolve with every deployment. Most ITOps workflows, however, are still designed around the assumption of stability. That mismatch drives failure. Static runbooks expect environments to stay put.