How to Cut Observability Costs Without Losing Visibility

Your observability bill grows every time your systems talk. Most commercial platforms charge by consumption: hosts monitored, gigabytes of logs ingested, custom metrics stored, traces kept. Nobody signs off on that growth. It just shows up on the invoice.

That's why observability keeps coming up in conversations with engineering leaders this year. Even Google's partner team told us everyone is talking about it, because it has gotten so expensive. The good news is that most of the spend is fixable, and fixing it doesn't mean seeing less.

Why is observability so expensive?

Observability tells you what your systems are doing: errors, slow requests, failed deployments, and the code that caused them. To do that, your applications send a constant stream of logs, metrics and traces to a platform that stores and indexes them.

On a consumption-based model, you pay for all of it. Think of it like renting: the more space your data takes up, the more you pay each month, and you never own anything. A few habits make it worse:

• Debug-level logging left on in production, so every request writes pages of text nobody reads.
• Metrics tagged with high-cardinality labels like user or session IDs, which turn one metric into millions.
• Every trace kept at full detail, even for healthy requests.
• All data held in expensive, searchable storage for as long as compliance requires, instead of moving older data to cheaper archives.
• Several tools doing the same job because different teams picked their own.

AI agents add to the pile. Every agent action generates telemetry, and as we wrote in AI Agents Need More Than Dashboards, you need that visibility to catch an agent doing something it shouldn't. Without a plan for cost, more agents means a bigger bill every month.

How Pindrop saved over $1M a year on observability

Pindrop, a leader in voice authentication and fraud prevention, handles millions of interactions a day. As the platform grew, different teams solved similar problems in different ways, and observability costs climbed along with everything else.

Tensure started with platform engineering work: GitOps automation that made infrastructure builds 60% faster and cut end-to-end deployment time from two hours to 30 minutes. That work also showed something nobody had a clear view of before. Multiple observability tools were doing the same job.

We consolidated them onto an automated, open-source stack built on Prometheus and Grafana, with logs, metrics and traces in one place. Pindrop dropped the licensing and hosting costs of its commercial observability tooling and now saves over $1M a year. Its engineers kept full visibility, and with one set of tools instead of several, they have fewer places to look during an incident.

Five ways to bring observability costs down

1. Find the overlap first

Before you negotiate a renewal or switch vendors, list every tool that collects logs, metrics or traces, and what each one is used for. Teams that grew quickly almost always have two or three tools covering the same ground. Removing a duplicate is the fastest saving, and it doesn't change how anyone works.

2. Send less data, not less signal

Turn off debug logging in production. Sample traces from healthy requests and keep every trace from failed ones. Drop metric labels that create millions of combinations nobody queries. Move logs you keep for compliance to low-cost archive storage after a set period. Each of these cuts what you pay for without hiding a single real problem.

3. Pick the pricing model that fits how you run

Consumption pricing works for some teams and punishes others. You have options:

• Open-source tools you run yourself, like Prometheus and Grafana. You pay in engineering time instead of license fees, which is the trade Pindrop made.
• Managed open-source services, like Google Cloud Managed Service for Prometheus, which keep the open-source standard without the upkeep of running it yourself.
• Platforms that price by infrastructure instead of data volume. Our partner groundcover charges per Kubernetes node, so the bill stays predictable as data grows.

The right answer depends on your size, your team's skills and how much of your stack runs on Kubernetes. That's the decision we help clients work through.

4. Keep your data in your own cloud

Many commercial platforms store your telemetry in their cloud, which means paying them to hold it and paying again to move it out. Bring-your-own-cloud (BYOC) setups keep observability data inside your own environment. For financial services teams with strict data residency and audit requirements, that also simplifies compliance.

5. Make good habits the default

Savings don't last if every new service starts from scratch. Build observability into your platform's golden path: standard instrumentation with OpenTelemetry, sensible log levels, sampling rules and retention policies already set. Add alerts on sudden jumps in data volume, so a noisy deployment gets caught in a day instead of on next month's invoice.

Where observability fits with platform engineering

Observability spend is a platform problem. When every team instruments its services differently, you get duplicate tools, inconsistent data and bills nobody can explain. When the platform sets the standard, you get one view of your systems and costs that track with value.

That's how Tensure approaches it. We don't sell an observability tool. We look at what you run today, find where the money goes, and set up the right tools so they work well and stay affordable as you add services and AI agents.

Frequently asked questions

What is consumption-based observability pricing?

It means you pay based on how much data you send and store: gigabytes of logs ingested, number of custom metrics, traces kept and hosts monitored. Costs rise automatically as your systems grow or get noisier, which makes the bill hard to predict.

Can you cut observability costs without losing visibility?

Yes. Most savings come from removing duplicate tools and data nobody uses, like debug logs in production or full traces of healthy requests. Pindrop cut over $1M a year while keeping logs, metrics and traces in one place.

Is open-source observability good enough for financial services?

Prometheus, Grafana and OpenTelemetry are widely used in production at large companies. The trade-off is that someone has to run them. A managed open-source service or a well-automated platform reduces that work.

How long does it take to see savings?

Removing duplicate tools and cutting noisy data can lower the next bill. Switching platforms takes longer, because you need to migrate dashboards and alerts without gaps in coverage.