Grafana Labs has obtained Federal Risk and Authorization Management Program (FedRAMP) High Authorization through Palantir Technologies’ FedStart Program.
Additionally, Grafana Labs has achieved compliance with Impact Level 5 (IL5) requirements, the corresponding authorization for DoD agencies. This is an important milestone that makes enterprise-grade observability accessible to organizations operating in highly regulated environments.
Grafana Labs has achieved a significant milestone with its FedRAMP High Authorization and IL5 compliance, enabling the company to deliver innovative observability solutions that allow government agencies and their commercial partners to better understand and manage their complex systems and infrastructure. Grafana Federal Cloud is a fully managed, highly scalable observability platform, powered by the industry-leading LGTM stack: Grafana Mimir for Prometheus metrics, Grafana Loki for logs, Grafana Tempo for traces, and Grafana, the de facto standard for data visualization.
"Our open source solutions have been used by federal agencies like the Department of Defense for years, but achieving FedRAMP High Authorization and IL5 compliance for our managed solution represents a significant step forward in our federal strategy," said Ash Mazhari, Grafana Labs VP of Corporate Development. "This authorization transforms Grafana from an open source tool to a strategic, managed observability platform for the most sensitive and mission-critical government infrastructure. Our partnership with Palantir accelerates our ability to deliver secure, scalable observability solutions that meet the highest standards of federal compliance and operational excellence.”
Grafana Federal Cloud is now listed on the FedRAMP Marketplace as part of the Palantir Federal Cloud Service, making it accessible to federal agencies and their commercial partners.
The Latest
Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...
Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...
When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...
If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...
Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...
AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...
Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...
Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...
Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...
In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...