Four bills, one place to read them

A cost tool, a Kubernetes dashboard, an ML platform, and whatever you built for agents. Four tools that never see each other, so nobody can say what the whole estate costs. CoverOps sits over all four.

  1. “Last year's launch, still billing every hour.”

    Unattached volumes. Oversized instances. Servers left running in a region nobody uses. Servers stopped but still paying for their disks. Every finding arrives priced in rupees, with the fix attached.

    • Read-only access you create and can revoke: a role with an External ID on AWS, read-only credentials on Azure, GCP, DigitalOcean and Oracle Cloud. Never a password
    • Every enabled region scanned, cost and utilisation per resource
    • Kubernetes cost: not built yet, on the roadmap with the in-cluster agent
    • Fixes applied only after your cloud team approves, each with a rollback

  2. “Your job is running. Your GPUs are idle.”

    Teams watch whether a job is alive. Nobody watches accelerator utilisation. So eight H100s sit at 30% for a fortnight and the bill looks completely normal.

    See the arithmetic
    • Runs under 35% utilisation get flagged as starved, not compute-bound
    • Only the idle share is priced as recoverable
    • Spot runs without checkpointing, where one preemption bins the epochs you paid for
    • Endpoints under 25% utilisation, holding a GPU warm between requests

  3. “A blocked run costs nothing. That is the whole saving.”

    Cached tokens cost a tenth of fresh ones. Most teams have caching off, or a prompt prefix that shifts just enough to miss it every time.

    See the arithmetic
    • Every run metered against the catalog. Fresh, cached and output priced apart
    • Frontier models doing work a cheaper tier handles at 96% success
    • Caching off, or hitting under a quarter of the time
    • Looping and timed-out runs that billed in full for nothing
    • Guardrails run before billing. A blocked run is a clean saving

  4. “One outage should page once. Not three times.”

    Every metric is measured against its own rolling baseline, so a signal has to be unusual to register. Related anomalies collapse into one incident with a probable cause attached.

    • A rolling baseline, not a threshold nobody tuned
    • Anomalies inside five minutes become one incident
    • Playbooks propose the fix, or apply it with the same audit trail
    • Automation can only do what an operator could

Every screen in the console

These are the console's own menu labels, so a demo uses the same words.

Overview
Spend under management, what is still recoverable, live CPU, memory, latency and GPU vitals over one stream, and an activity rail.
Cloud accounts
Read-only connect to AWS, Azure, GCP, DigitalOcean, Oracle Cloud. Inventory reconciled on a schedule, per-resource cost and utilisation, idle findings with the fix attached.
Kubernetes (demo data only)
On the demo estate: register clusters, scale node pools, cordon and drain nodes, deploy, scale, restart and delete workloads, live capacity meters.
Containers (demo data only)
On the demo estate: every pod with live CPU and memory, restart counts, restart, stop and evict controls, and streaming logs.
Alerts
A 26-metric rule builder spanning infrastructure, cost, security, GPU and agent metrics, with duration and cooldown windows and routing to channels you choose. Alerts deliver in-app, by browser push, email, Slack, webhook and PagerDuty. SMS, WhatsApp and Teams are not connected yet.
MLOps (demo data only)
On the demo estate: model registry with drift tracking, GPU training runs priced per accelerator-hour, inference endpoints with scale-to-zero, and a waste report that names the recoverable dollars.
AIOps
Baseline-relative anomaly detection, correlation into single incidents with a probable cause, and playbooks that suggest or auto-apply the remediation.
AgentOps (demo data only)
On the demo estate: agent registry, per-run token metering priced from the model catalog, budget and token guardrails that block before billing, and a savings report.
Delivery (demo data only)
On the demo estate: apps with verified GitOps deployments and one-click rollback, plus a security centre for supply-chain, container, IAM and configuration findings.
Audit report
Every finding ranked and priced per month in ₹ or $, with evidence, the fix, and a plan grouped by confidence. Says whether each figure came from billing data or list prices, and prints to PDF.
Approvals
Fixes raised as proposals on resources you tag. Re-checked against the live resource at approval, applied by a named approver, rolled back from a snapshot.
Account
Team and role management, API keys, a data export, and an audit log of what the platform did.

Three rules it is built around

Your account, not ours
We read and, when you approve, change; we never host. Every resource stays in your cloud account, reached through a role you wrote and can delete.
One live stream
Sync results, metric ticks, firing alerts and deployments all arrive on one event stream. Nothing polls, nothing goes stale.
Automation cannot outrank you
Playbooks call the same functions the console does, so automation can never do something you could not do by hand.

Where the build is: live adapters for AWS, Azure, GCP, DigitalOcean and Oracle Cloud read your real inventory, utilisation and cost, read-only. DigitalOcean and Oracle Cloud are the newest and fixes on them are advice-only for now. The CoverOps Agent turns it all into a plan of what to do and when. The roadmap says what is next.

See what it finds on your estate

Sign up and connect one account with a read-only role for a free scan. The full audit, from ₹24,999, is in your console within 3 working days. Or open the sample audit first.

Start your free scan