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AI pricing explained: what AI actually costs and how providers charge for it in 2026

AI pricing covers the cost structures and billing models providers use to charge for AI products: per-token APIs (GPT-4o at $2.50/1M input tokens), per-seat subscriptions (Copilot at $30/user/month), per-conversation billing (Agentforce at $2/conversation), and consumption-based GPU compute (H100 instances at $55.04/hour). There is no standard. The total AI cost is almost always higher than the sticker price.

Shipped: Stop rebuilding Views from scratch

In Explorer, you build a filter set and group-by to answer a cost question, and often that’s exactly the configuration you’d want to save for later. But saving it as a View meant navigating away from Explorer, opening the Views page, and rebuilding the same configuration from scratch: filter by filter, dimension by dimension. That friction was enough to discourage saving exploratory analysis as a View at all You can now save any Explorer analysis as a View in place.

Beyond Mythos: responding to a new threat landscape

Canonical’s security philosophy has always been built on the premise that vulnerabilities exist and will be discovered. Our response relies on defense-in-depth architecture, rapid patch deployment, and strict adherence to Coordinated Vulnerability Disclosure (CVD). AI changes vulnerability discovery volume and speed. We have a robust vulnerability management process that is backed by rigorous compliance certifications.

Template: Streamlining open source design contributions

As designers working at Canonical, we’re always thinking about open source. We believe that encouraging more designers to contribute to open source benefits everyone, from the project maintainers to the end users themselves. In the 2025 edition of FOSSBackstage conference, we presented our research findings on why designers don’t get involved in open source projects and found a particular breakdown between designers and project maintainers.

Measuring engineering organizations in the age of AI

Engineering leadership is in the middle of a real transition, and most of the leaders I talk to know it. AI has reshaped how software gets built quickly enough that the operating models many of us spent a decade refining no longer fit cleanly, and there is a great deal of serious work happening across the industry to figure out how these models should evolve. The teams I find most impressive right now are the ones treating their operating model as an open question rather than a settled one.

IsDown is joining UptimeRobot

Today I'm sharing some big news. IsDown is joining UptimeRobot When I started IsDown, the idea was simple. Keeping track of outages across dozens of vendor status pages was painful, and I wanted to make it easy to see, in one place, when the services you depend on go down. Thousands of teams now rely on IsDown to do exactly that. Joining UptimeRobot is the natural next step.

Visibility Isn't Reliability: Why Observability Alone Cannot Protect SLAs

Over the past decade, enterprises have invested heavily in observability platforms designed to deliver comprehensive insight into increasingly complex environments. Modern systems generate continuous telemetry across infrastructure, applications, networks, cloud services, and third-party dependencies. Metrics, logs, traces, and topology maps now provide a level of technical transparency that would have been difficult to imagine only a few years ago.

GitKraken: The Code Flow Company

From plan to main. Software is no longer just a tool. It is the infrastructure of modern life. Software keeps airplanes in the sky and power flowing into our homes. It helps doctors save lives, scientists discover cures, farmers feed cities, and astronauts navigate space. It powers economies, protects supply chains, and connects billions of people across the world. Every major system humanity depends on now depends on software. Which means developers are no longer just building applications.

Track Deployment status for your PRs (Beta)

You shouldn’t have to leave your PR list to know where your code is deployed. Yet, developers constantly lose time context-switching just to see if a change hit staging or production. To solve this, we are launching the Beta version of Deployment Status Tracking for your PRs. This feature surfaces live deployment statuses directly within your PR list view as code moves through your pipeline.

Un-observable AI is Un-trustworthy AI

Recently, someone talked Chipotle’s customer support agent into reversing a linked list – a task completely unrelated to burritos in any way. Screenshots circulated, people laughed, but underneath the joke sat a sharper question. If a production support agent will do that on a public channel, what else will it do that nobody is screenshotting? The bug is funny. The trust gap behind it is not.