Operations | Monitoring | ITSM | DevOps | Cloud

Today, we are pleased to announce the release of CFEngine 3.28.0! The code word for this release is introspection. This is a non-LTS, not supported, feature release, with features which will become part of the next LTS scheduled for summer 2027.

In some organizations, high token counts have become a proxy for productivity. Some engineering teams are being pushed to max out context windows and wire in sprawling tool sets. More tokens can mean better agent reasoning and richer context during development, but token costs compound in production. Tokens accumulate across sessions, users, and tool calls in ways that are easy to overlook. Datadog’s 2026 State of AI Engineering report quantifies the scale of this problem.

How many tools does your team need to answer one question about production? For most enterprise IT teams the honest count is four: a metrics dashboard, a log analyzer, a tracing tool, and the spreadsheet where someone stitches the other three together during an incident. Each of those tools stores its own copy of the truth and sends its own bill.

What happens when a city's traffic signals freeze at rush hour and nobody in the operations center knows why? For the teams running a connected city, that gap between a failure and its first clue is the worst place to be. Smart city monitoring closes that gap. It gives operators a live view of every network, device, and service the city runs. A fault gets caught and traced before citizens ever feel it. Without that visibility, small problems stay hidden until they spread.

Last updated: July 2026. Pricing verified against public vendor pricing pages on July 9, 2026. The monitoring tool market in 2026 is split. On one side, enterprise platforms keep adding features: security scanning, network monitoring, CI/CD integration, cost management. On the other, developer-focused tools are going deeper on what matters during a production incident: how fast you get from alert to the line of code that caused the problem.

Your Node.js app is slow and you are not sure where. The response time dashboard shows spikes but not causes. The logs say nothing useful. CPU looks fine. Memory looks fine. Users are complaining anyway. This is the standard Node.js performance debugging experience. The single-threaded event loop, async-everything execution model, and connection pool sharing across all requests make Node.js performance problems different from what you see in Ruby or Python.

‍For years, ilert has given our customers extensive analytics across their alerts, notifications, and on-call activity, a comprehensive overview of how their teams and services respond to incidents. These capabilities were backed by a separate analytical database running on Google BigQuery. It held the numbers behind every reporting dashboard in ilert, and for a long stretch it was perfectly fine. Then three problems grew too big to ignore.

Database migrations are where the mock data problem shows up most clearly. A migration that adds an index to a table with 500 rows in the development database runs in milliseconds and passes every test. The same migration against a production table with 8 million rows locks the table for 90 seconds during peak traffic. Nobody saw it coming because nobody tested it against 8 million rows. This isn't an edge case.