Observability.
Unified metrics, traces and dashboards across apps, models and infrastructure.
Metrics, traces and dashboards in one place across applications, models and infrastructure — correlate a latency spike to a deploy, a GPU, or a model version without tab-hopping.
- Category
- Developer tools
- Deployment
- Managed → air-gapped
- Governance
- IAM · encryption · audit
Three tools for metrics, logs and traces — and none of them know your model.
Most teams stitch observability from a metrics system, a logging system and a tracing system, then correlate across them by hand during an incident. And now there's a fourth signal none of them natively understand: the model — its tokens, its cost, its output quality. Aether unifies metrics, traces and logs in one timeline and treats the model as a first-class signal, so a latency spike, a cost spike and a quality regression are visible in the same place, correlated, not reconstructed across four dashboards at 2 a.m.
Correlate a spike to a deploy, a GPU, or a model version.
The value of unified observability is the join: this latency increase started with that deploy, on these GPUs, when the model version rolled. When the signals live in one system that knows about your infrastructure and your models, that correlation is a click, not a cross-tool investigation. SLOs and burn-rate alerts catch problems early, with prebuilt and custom dashboards across the whole stack.
On the platform it observes.
Because it runs on the same platform as your workloads and the model, instrumentation is wired in rather than bolted on, and there's no egress of telemetry to a third-party SaaS — which matters when the traces contain regulated data. It deploys in your boundary, managed to air-gapped.
Ask one question that spans infra and the model: what changed when latency and cost both jumped?
incident #412 · p95 latency +180%, token cost +140%
correlated:
deploy api@2026-06-19 14:02 (model pin aether-40b@2026-05 → @2026-06)
gpu pool util 96% (was 60%)
quality eval -0.04 on support-set
→ likely cause: model version bump; roll back pin [apply]Latency, cost and quality are one correlated view tied to the deploy and the model version. In a three-tool setup that's an investigation across dashboards; here the model is a first-class signal, so the cause surfaces with the symptom.
Three steps to running.
Metrics, traces and logs flow in automatically across the stack.
Tie a latency spike to a deploy, a GPU or a model version.
SLOs and burn-rate alerts catch problems early.
Observability, in full.
Metrics, traces and logs correlated in one timeline.
Token, cost and quality signals alongside infra metrics.
Define service levels and alert on burn rate.
Prebuilt and custom views across the whole stack.
Provision it in a few lines.
Every service is reachable from the same SDK, CLI and infrastructure-as-code — one identity, one bill, one audit trail across the whole catalog.
# Provision observability on Aether Cloud
aether developer-tools create \
--service observability \
--name app \
--region us-1 \
--deploy managed # or vpc | on-prem | air-gappedAt a glance.
- Signals
- Metrics · traces · logs
- AI-aware
- Token · cost · quality
- SLOs
- Burn-rate alerting
- Dashboards
- Prebuilt + custom
- Correlation
- Across the whole stack
Built for real work.
Production monitoring
Incident investigation
AI app quality tracking
On one model, not stitched together.
The usual stack runs observability in one product, the model in another and the data in a third — and the seams between them are the cost. Aether Cloud runs it on the same platform that serves the model, governs your identity and deploys into your boundary, with the rest of the catalog one hop away.
No stitching a vector DB to one place, a warehouse to another and a model to a third — observability sits next to the rest of the catalog, one identity, one bill.
The provider that runs Aether runs your observability — so the data and the model never leave the same governed boundary to talk to each other.
Need a capability that isn’t here yet? The model writes and deploys it into the same boundary — the catalog is a starting point, not a ceiling.
Good to know.
Unified observability — with model token, cost and quality signals alongside infra metrics, in one timeline.
Yes — model quality and cost sit next to infra metrics so you can correlate.
Yes — define service levels and alert on burn rate.
Your boundary, your choice.
Pairs well with.
Managed build, test and deploy pipelines with environments, approvals and rollbacks.
Private image registry with vulnerability scanning, signing and replication.
Declarative provisioning of the whole catalog, with plan, diff and drift detection.
Structured logs and distributed traces with retention, search and alerting.
Progressive delivery, targeting and experiments with instant rollback.
Run Observability on Aether Cloud.
Unified metrics, traces and dashboards across apps, models and infrastructure. Deployable managed, in your VPC, on-prem or fully air-gapped — talk to us about the configuration your workloads and your boundary require.