Ops Pillar · Telemetry Ops

See everything, at petabyte scale.

Telemetry Ops is the observability foundation of Opstral. It collects OpenTelemetry signals from every service and stack, unifies logs, metrics and traces in one store built for petabyte scale, and lets any team build the dashboards and queries they need, then hands clean, correlated telemetry to Sentinel AI.

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OpenTelemetry-native
Logs, metrics & traces
Petabyte scale
Dashboard builder
Collectors for any language

Unified telemetryOTel-native

TracesIngestingLive

MetricsStreamingHealthy

LogsIndexedSearchable

StoragePetabyteElastic

Correlated view

Trace to log in one hop

A slow request opens straight to its spans and the exact logs they emitted.

Correlated

High-cardinality kept

Resource and signal attributes stay queryable, not sampled away.

Queryable

Your storage, open format

Telemetry lands in open standards you can keep and query, no lock-in.

Portable

What is Telemetry Ops

One observability layer for every signal you emit.

Most teams run observability across a pile of disconnected agents, formats and tools, and pay to keep data they cannot correlate. Telemetry Ops unifies it on open standards: OpenTelemetry-native collectors gather logs, metrics and traces from most languages and stacks, a store built for petabyte scale keeps them queryable instead of sampled away, and a dashboard builder turns any signal into a view. The result is clean, correlated telemetry that people can explore and that Sentinel AI can reason over.

OpenTelemetry-native collection

Instrument once, in the language you already use.

Telemetry Ops speaks OpenTelemetry end to end. Collectors and auto-instrumentation cover most languages and runtimes, and anything that emits OTLP is a first-class source, so you are not rewriting instrumentation to fit a vendor agent. Because it is OTel-native, the semantics travel with the data: resources, services and attributes stay intact from the SDK all the way to the query.

Collectors and auto-instrumentation for most languages
Any OTLP source is first-class, no proprietary agent
Resource and attribute semantics preserved end to end

CollectorsOTLP in

JVMjava auto-instrumentation reportinghealthy

GOotel-sdk traces + metricshealthy

NODEotlp/http exporter connectedlive

K8Scollector daemonset, node logslive

Unified logs, metrics & traces

Three signals, one correlated store.

Logs, metrics and traces land in one place and stay linked by the resources that produced them. A slow trace opens straight to the metrics around it and the exact logs its spans emitted, so an investigation is one path instead of three tools and a guess. Resources, whether a service, pod, container or cloud zone, sit at the centre, so context follows you as you pivot.

Logs, metrics and traces linked by resource
Pivot trace to log to metric without losing context
Resource-centric model across services and infra

Correlated viewOne path

TraceSlow spanLatency

MetricSaturationElevated

LogErrorsSpiking

Resourcepayments-podPinned

Petabyte-scale storage & query

Keep the detail. Query it fast.

Telemetry Ops is built for petabyte-scale telemetry, so high-cardinality data stays queryable rather than sampled down to fit a budget. Query in the language you know, PromQL for metrics and SQL for logs and traces, and get results fast enough to explore, not just to alert. Retention and tiering are yours to set, so cost tracks value instead of surprising you.

Petabyte-scale store, high cardinality kept queryable
PromQL and SQL over the same telemetry
Retention and tiering you control for predictable cost

Query enginePromQL / SQL

Hot tierFast

Warm tierReady

Cold / archiveCheap

Cardinality keptFull

Your retention policy decides what stays hot

Dashboard builder

A dashboard for every signal, built by anyone.

Turn any query into a panel and any set of panels into a dashboard, without waiting on a specialist. Build service views, team views and executive views over the same telemetry, and share them across the estate. Open dashboard standards mean the boards you build travel with you and are not trapped in one vendor's format.

Turn any query into a panel, any panels into a board
Service, team and executive views over one dataset
Open dashboard standards, portable and shareable

Dashboard builderLive panels

Latency

Throughput

Error rate

Saturation

Explore & investigate

Ask a new question without pre-building for it.

High-cardinality exploration lets you slice telemetry by any attribute after the fact, so you can chase an outlier you never predicted. Drill from a dashboard anomaly into the traces behind it, then the logs those spans emitted, and group by resource, endpoint or tenant to find where the pressure really sits. The workflow is built for the unknown-unknowns, not just the metrics you already chart.

Slice by any attribute, high-cardinality friendly
Drill from anomaly to trace to log in one flow
Group by resource, endpoint or tenant on the fly

ExploreGroup by

tenant: acmeHigh

endpoint: /payMed

region: eu-1Med

build: 4821Low

Outlier isolated to one tenant and endpoint

Open, portable & governed

Your telemetry, your standards, your perimeter.

Telemetry Ops is built on open standards, OpenTelemetry for data and open formats for storage and dashboards, so nothing is trapped in a proprietary silo. It runs inside your perimeter, on-prem or air-gapped, and the same governance that covers the rest of Opstral applies here: role-based access, data curation before any model, and full audit. That is what lets Sentinel AI reason over the telemetry safely.

Open standards for data, storage and dashboards
Runs in your perimeter, on-prem or air-gapped
RBAC, curation and audit, governed like every pillar

Open & governedNo lock-in

OTELOpenTelemetry data modelstandard

STOREopen format, your storageportable

RBACrole-based access, full auditgoverned

EDGEon-prem / air-gapped deployin perimeter

07Real-world use cases

Built for the signals you already collect

These are not demo scenarios. Each one is a situation Telemetry Ops teams meet in real operations, and each ends the same way: an investigation that starts at the evidence and finishes with a decision someone can defend.

A new question at 3am

Slice by any attribute without having pre-built a dashboard for it.

Answered in the moment

Instrumenting a new service

Collectors and auto-instrumentation for most languages, any OTLP source first-class.

Instrument once

Executives and engineers differ

Service, team and executive boards built over the one dataset.

No second pipeline

Data cannot leave the perimeter

Runs on-prem or air-gapped, with RBAC, curation and audit throughout.

Governed like every pillar

08Business perspective

What it is worth outside the observability team

Telemetry Ops is an engineering capability with a business ledger. The entries that matter are lower observability spend, no vendor lock-in, faster investigation and sovereign deployment, and it moves all of them.

Lower observability spend

One open store replaces per-vendor agents and duplicated data.

No vendor lock-in

Open standards for data, storage and dashboards throughout.

Faster investigation

Any question answered without pre-building a dashboard for it.

Sovereign deployment

Runs on-prem or air-gapped, governed and audited.

09The numbers

The cost of unread data, summed honestly

Worked example against an observability budget. Every figure shows the sum it came from; during a pilot we substitute your own inputs.

Paid to store, never queried

$1.05M

62% never read × $1.70M observability spend

Hours on cross-tool questions

1,080 hrs

60/mo × 2 hrs × 12

Cost of those questions

$92k

1,080 hrs × $85/hr fully loaded

Released if retention follows usage

$527k

Modelled at 50% reduction, not a measured result

Telemetry Ops sees it. Sentinel makes sense of it.

Collecting telemetry is only half the job. The unified logs, metrics and traces that Telemetry Ops gathers feed Sentinel AI, the intelligence component at the core of Opstral. Sentinel runs the OIAO loop over that telemetry, correlates signals into incidents, finds root cause across the estate graph, and resolves through governed, reversible Action Tickets.

Sherlock closes the root-cause loop and ProcBot executes the fix, every step explained with citations and fully audited.

Observe
Telemetry Ops streams correlated logs, metrics and traces from every source.
Investigate
Sentinel AI slices high-cardinality telemetry to find root cause across the graph.
Act
ProcBot executes the approved MOP through a reversible, audited Action Ticket.
Optimize
Sherlock verifies the fix against the same telemetry before the incident clears.

See Telemetry Ops on your stack.

Book a walkthrough and see OpenTelemetry-native collection, unified logs, metrics and traces at petabyte scale, and dashboards built over your own signals.

Request a Demo Meet Sentinel AI