You're trying to finalize your observability budget for next year. The CFO wants numbers. Your vendor contract is up for renewal. And you're stuck — again — because you don't actually know how much telemetry data your teams are going to send.
Worse, you're not even the one generating the data. Every log line, metric, and trace comes from dozens of services owned by engineering teams who have other priorities. They're not thinking about ingestion costs, high cardinality metrics, or log volume thresholds. You are. Which means every time the system buckles, the bill spikes, or someone adds a noisy new log pattern, you're the one left chasing it down.
You spend hours playing telemetry whack-a-mole. Chasing devs. Asking, "Do you still need this metric?" Digging into usage graphs. Trying not to break production. This isn't sustainable.
The solution isn't about giving DevOps more levers to pull — it's about building systems that make it effortless for developers to take action themselves. What you need is a telemetry data management layer that provides centralized, dynamic control over logs, metrics, and traces — not to exert control, but to hand it off safely.
The telemetry ticking time bomb
What started as "let's make sure we have visibility" turns into a deluge. Tens of millions of log lines per hour. Thousands of unique metric series. A mountain of traces no one has time to sift through.
Every new microservice comes with its own telemetry footprint — often copy-pasted from the last project or bolted on in a rush. Engineers are told to "instrument everything" but rarely revisit what they've added.
This telemetry data sprawl isn't just annoying — it's out of control. It drives up observability costs, slows down your tooling, and makes it harder to spot real issues. In on-prem setups, it can even crash your logging or monitoring pipelines.
But here's the kicker: the people responsible for the observability system aren't the ones creating the data. You're left holding the bag — expected to control the chaos without touching the code that caused it.
Manual fixes won't save you
You've probably tried the manual route. You've written docs. Sent Slack messages. Set up dashboards to highlight "bad" telemetry. Maybe even ran office hours to review log usage with teams. It doesn't work long-term.
Engineers are busy shipping features, not trimming logs. You don't have their context to make safe cuts. And the telemetry is always changing — a new deploy could wipe out your fixes in seconds. You need automation. You need context. You need governance.
Applying a smart telemetry data management layer
Instead of modifying code or pleading with engineers, you need a control layer between your services and your observability backend. And that layer should be built with artificial intelligence that operates faster and smarter to control costs and maintain control over your telemetry pipeline.
What does that mean? This layer gives you full visibility into what's being emitted and ingested, dynamic controls to route and filter data without touching application code, automation that detects data issues, and smart recommendations for how to fix issues.
Think of it like a reverse proxy for your telemetry. It doesn't just pass through data — it inspects, transforms, and governs it.
Change the game
A good telemetry data management layer will automatically detect and cap high-cardinality metrics, identify and drop noisy logs while preserving critical information, set volume or cost limits per team or application, test changes before rolling them out live, and track what's being transformed for governance and compliance.
This gives you a safety net. You're no longer at the mercy of whatever telemetry developers decide to send — you have centralized levers to control costs, performance, and stability.
Telemetry management without disrupting workflows
The goal isn't to become the telemetry police. It's to make observability sustainable without slowing down developers.
Start with visibility by monitoring what's being sent. Make the first fixes yourself using safe, reversible transformations. Use evidence to show teams what's costing money. Give teams control if they want it, letting them override defaults or set their own filters. And keep them shipping — don't block deploys or introduce friction.
Bottom line: predictable budgets, less chasing, more control
You don't have to live in a world where observability costs are unknowable, stability is at risk, and your team is stuck cleaning up someone else's mess. A smart telemetry data management layer gives you what the code can't: a way to govern data after it's emitted, in a way that's automated, collaborative, and non-intrusive.
You'll finally be able to forecast observability costs with confidence, enforce telemetry quality without dev buy-in for every change, and keep systems stable even as data volumes grow. Most importantly, you'll stop being the bottleneck and start being the team that made observability scalable.
This is why we built Sawmills
Sawmills provides that essential telemetry data management layer, giving you the visibility, control, and automation needed to make observability sustainable.
With Sawmills, developers gain insight into their telemetry footprint while DevOps teams get the governance tools they need without becoming the bottleneck. We've designed it to integrate seamlessly into your existing workflows, providing immediate value without disrupting your teams.
Schedule a time with our team to learn more.