Maestro scores every method in your Java services and sets how deeply each one is traced — full method-level detail where the logic is complex, a light touch where it isn’t. One tool does it for every service and every team, so the traces feeding your dashboards, your data platform, and your AI agents are complete, consistent, and governed at the source.
The OpenTelemetry agent sees the edges of your service — the request in, the database call out — but not the logic in between. To compensate, teams over-instrument everything, resulting in a flood of useless spans and skyrocketing bills. Ultimately, this noisy, incomplete trace data is exactly what you end up feeding into your analytics platforms and AI models.
Garbage in, garbage out — at premium prices. No dashboard, query, or model fixes telemetry that was already wrong before it left the JVM. And as traces become observability’s source of truth — with logs and metrics increasingly derived from them — the quality of your traces is the quality of your observability.
No source access. No annotations. No rewrites. You hand Maestro your compiled application, tell it what you care about, review what it proposes, and ship — the same four steps whether it’s one service or six hundred.
One or many compiled JARs. Maestro works from bytecode — it never reads, rewrites, or sees your source.
It builds the cross-service call graph, then scores every method against the observability goals you pick.
See exactly what gets traced and captured. Toggle methods, set policy, lock it in. Nothing reaches prod unseen.
Maestro generates an OTel extension JAR for each service. Standard OTLP flows to whatever backend or agent you run.
The same application should be instrumented differently depending on what you’re trying to do. Your priorities set the depth — not a one-size-fits-all default.
Teams are leaving closed APM behind for open standards, their own data platforms, and AI-driven analysis. Maestro is the layer that makes that move pay off — and it sits in the same place for every application, every team, and every destination you send data to.
Vendor-neutral by design. Same OpenTelemetry rails, clean signal, zero lock-in. The agent layer legacy APM made proprietary — now open, governed, and yours.
Fix the trace data at the source and the downstream problems dissolve — the ones you can prove this quarter, and the ones that compound as more of the analysis gets handed to machines.
Complete across services, detailed where it counts, consistent everywhere, and governed before deploy. The signal is right because it’s right at the source — not patched up after the fact.
Provable todayAny analysis is only as good as what it reads — and the reader is your engineers today, your data platform and AI / SRE agents next. The same clean, labeled traces that turn a two-day debug cycle into a four-minute one now are what turn “the model guessed” into “the model knew” later. Cheaper answers either way — we make the new stack work rather than compete with it.
Stop paying to store noise. Maestro keeps full fidelity on the paths that matter and trims the rest — and at $10 per JAR per month, our pricing never scales with your data volume, so we don’t profit when your bill grows.
Provable todayEverything Maestro produces is inspectable, diff-able, and yours. Read it, review it in a pull request, check it into your repo.
An OpenTelemetry extension JAR for each service — the instrumentation itself, generated at no extra charge.
A readable plan of every method, its tier, and what it captures.
The cross-service call graph Maestro built from your JARs.
The run configuration — standard OTel, ready to launch.
OpenTelemetry gives you the standard. It doesn’t give you control over what gets collected, what it costs, or what ends up inside a span. Maestro is where you set those rules once and have them enforced everywhere — every team, every service, every push.
Maestro isn’t a specialist tool for one team. The SRE on call, the developer shipping code, the platform engineer standardizing sixty services, and the manager answering for the bill all work from the same instrumentation layer — one standard, across every application and every backend in the enterprise. Here’s what changes on the ground, from each seat.
Cleaner signal, faster root-cause, and a cost conversation that finally goes your way.
Read the story → The DeveloperNo instrumentation homework, no span tax in review, no redeploy just to see inside.
Read the story → The Platform EngOne instrumentation standard across sixty services — onboarded in minutes, not days.
Read the story → The ManagerObservability decoupled from release cycles, and a vendor negotiation that gets easier.
Read the story →Java is where enterprise observability cost and complexity bite hardest, so that’s where Maestro starts — ship-ready now. The same engine extends across the JVM and beyond next.
One number, billed annually in advance, and it never moves because your traffic did. The trial isn’t a sandbox demo — it’s your real services, instrumented, feeding your real backend. That’s why we take a card at signup: so day 31 isn’t the morning your traces go dark. Cancel any time in those thirty days and you’re not charged.
Billed annually, in advance. Twelve JARs under Maestro management is $120 a month — $1,440 for the year, quoted and invoiced once.
Not per span, per host, per seat, or per environment. Add a JAR and the number moves by ten dollars.
A JAR is one compiled .jar file you upload to Maestro for analysis — the packaged output of your Java build, the artifact you actually deploy and run. Each one under management is $10 a month.
Not sure how your build maps to JARs? We’ll count it with you →
One control plane for every team, every Java service, and every backend you already run. Take thirty days free on your own stack, or talk to us about a design-partner benchmark on your before-and-after — measured on your own services.
30 days free · card required at signup · cancel any time before day 31