Checkly announced a new monitoring as code (MaC) workflow — enabled by a new CLI released today in beta — that unites software testing and monitoring in one code-driven workflow that spans development, testing, deployment, and operations.
The company also unveiled new features and enhancements, including general availability of Playwright Test, updated public dashboards, a robust new analytics API, and successful completion of its SOC 2 Type II security audit.
Checkly’s new MaC workflow and CLI enables developers, platform engineers, and SREs to code, test, and deploy their testing and synthetic monitoring in one seamless workflow. The new MaC workflow harnesses the power of Playwright for browser checks, and allows developers to store all resources in a Git repo, alongside their existing application code for increased deployment speed and confidence. The new Checkly MaC CLI also allows developers to code and structure resources using TypeScript, allowing them to take advantage of the improved developer experience and code maintainability.
“Configuring monitoring is often an overly manual process of operations teams. With MaC we are now liberating this traditionally siloed workflow, enabling developers to configure monitoring themselves early during development,” says Hannes Lenke, Checkly Co-Founder and CEO. “We’re thrilled to deliver our MaC workflow supporting Playwright and API checks to give developers the ability to code, test and deploy with confidence.”
Further demonstrating the deep value of the platform to customers and users, Checkly has already run more than 5.0 Billion API and browser checks and, in the last quarter, added nearly one hundred customers and thousands of users, including development teams at top brands and organizations globally.
Product Innovations Announced:
- MaC Workflow and CLI (beta): A new MaC workflow and CLI allows developers to code Playwright-based E2E checks and API checks using TypeScript / JavaScript, then easily test and deploy those checks against Checkly’s global infrastructure.
- GA for Playwright Test: Playwright Test is GA, and Playwright is now the default and recommended testing and monitoring framework to use with Checkly. This release also adds a new UI that includes screenshots, videos, traces, and error logs to improve monitoring efficiency.
- Updated Dashboards: Updates to dashboards allow more users to brand and personalize the look and feel of their dashboards using custom CSS rules, configure custom subdomains, and also features improved readability and accessibility.
- New Analytics API: A new analytics API gives users more freedom to generate reports for business needs. Users can now integrate Checkly data into 3rd party BI platforms like Microsoft PowerBI, Google Data Studio, Grafana, and more.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.