Operations | Monitoring | ITSM | DevOps | Cloud

Most teams know they are spending money on AI in production. Far fewer realize how much they are spending outside production. It’s easy to get lost as you evaluate which model has the best responses, is fast enough, and cheap enough to run in production. That is because the AI bill usually shows up as a giant blob. It is easy to see the total.

APIs sit at the center of modern digital infrastructure. Mobile applications, SaaS platforms, microservices, and third party integrations all depend on APIs to exchange data and execute business logic in real time. When an API becomes unavailable, slows down, or returns incorrect data, users feel it immediately. Transactions fail. Dashboards stop updating. Logins break. Revenue and trust are affected within minutes.

Engineering teams are shipping faster than ever. AI coding tools like Claude Code and OpenAI’s Codex have quietly removed some of the biggest friction points in the development cycle — and the result is that FinOps teams are being asked to keep up with a pace most practitioners haven’t fully reckoned with yet. That acceleration has a cost consequence. More shipping means more services, more experiments, more infrastructure spun up without review cycles.

Modern software runs on APIs. Whether you are operating microservices, integrating third party services, or building customer facing platforms, APIs are the backbone of your architecture. As systems become more distributed, simply knowing whether an endpoint is up or down is no longer enough. Teams need deeper visibility into performance, reliability, latency, and behavior across environments. That is where API observability tools come in. API observability goes beyond basic health checks.

Modern applications are powered by APIs. Every login request, checkout transaction, mobile interaction, and third-party integration depends on APIs responding quickly and reliably. When an API slows down, the entire user experience suffers. Even a one-second delay in response time can: For ecommerce platforms, fintech systems, SaaS products, and real-time applications, slow APIs do not simply create inconvenience. They directly affect revenue, customer retention, and operational stability.

On paper, lift-and-shift VM migration to Kubernetes sounds simple. Compute can be moved. Storage can be remapped. But many migration projects stall at the network boundary. VM workloads are often tied to IP addresses, network segments, firewall rules, and routing models that already exist in the wider environment. That is where lift-and-shift becomes much harder than it first appears.

How platform and SRE teams are using Mezmo's open-core agent framework — with any LLM, any tools, any observability backend.

Elixir’s “let it crash” philosophy is one of the best ideas in modern software design. Supervisors restart failed processes, the system self-heals, and life goes on. It’s like having a really good immune system. The problem is that a really good immune system can also hide chronic conditions. A GenServer crashing and restarting is working as designed.

Here’s a scenario most IT teams know too well: a single error message lights up the monitoring dashboard at 2 a.m. Within seconds, calls are coming in from customers. Within minutes, the revenue meter is running. If your team is still figuring out who owns the incident while that meter ticks, you’ve already lost precious time. According to 2024 EMA Research, unplanned IT downtime now costs organizations an average of $14,056 per minute, rising to $23,750 per minute for large enterprises.

Seer is our AI agent that takes bugs and uses all of the context Sentry has to find the root cause and suggest a fix. We use it all the time to help us improve Sentry. Seer fixes Sentry. More recently, Seer has been helping us fix itself — Seer fixing Seer. An upstream outage triggered a bit of an avalanche, revealing a bug that had been hiding away for months. When it came time to fix it, Seer pointed us exactly where we needed to look.