OkOvia AI cost & quality intelligence for LLMs & GPU infrastructure

Know what each AI feature costs — and if it makes margin

OkOvia collects usage metadata from your apps, backends, and GPUs, attributes every consumption to a product operation, and combines cost with your revenue rules. You see cost by feature, model, customer, and provider; revenue and margin by feature, channel, and customer — and, with client-side evals, quality.

checkout · GPT-4o · 1,840 tokens · cost $0.0012 · revenue $0.004 · margin 70%

Revenue calculated from the rules your team configures.

Drop-in web tagPrompts never collectedFree in beta
Product

What you see in the console

Attributed cost, revenue, and gross margin by feature — with drill-down to each individual operation.

Demonstrative data — the layout mirrors the real dashboard.

How it works

Three steps to a live cost dashboard

Instrument once. OkOvia prices and attributes everything from there.

1

Instrument

Install the tag, SDK, or connector. OkOvia receives usage metadata — never prompts or completions by default.

2

Attribute

Tie each consumption to a feature, operation, customer, plan, channel, or environment.

3

Calculate

OkOvia applies your price and revenue rules to show attributed cost and margin, continuously.

Costs come from instrumented usage and your price rules; revenue from the rules you configure. Anything not instrumented or configured is not included.

Answers

Questions OkOvia answers

  • Which AI feature costs the most?
  • Which model gives the best cost-performance ratio?
  • Which customers or plans are eating my margin?
  • How much are errors, retries, and timeouts costing?
  • Is local inference really cheaper than an API?
  • Does my product's price cover its AI cost?
  • Which provider drives the biggest spend?
  • How do cost and margin evolve in real time?
  • Did quality drop when I switched to a cheaper model?
Integrations

What's available today

Honest status per surface — nothing is listed as ready before you can actually install it.

  • Web tag (browser)Available today
  • Cloud GPU connector — RunPod, AWS, GCP, Azure (self-hosted)Available today
  • Python SDK — pip install okoviaAvailable today
  • JavaScript/TypeScript SDK — npm install okoviaAvailable today
  • Swift SDK (iOS/macOS) — Swift Package ManagerAvailable today
  • On-device inference metering (Swift)Beta

Private by design

Prompts never collected

Only usage metadata leaves your app - never prompts, completions, or user content.

On-device redaction

Sensitive fields are redacted before anything is sent, on the client and on the server.

Credentials stay in your infra

Cloud GPU metering runs as a connector inside your infrastructure - keys never reach OkOvia.

Architecture

Architecture analysis on every pull request Private beta

Architecture Guard — the newest OkOvia module — reviews each PR against your recorded architecture decisions (ADRs), maintains a versioned graph of your system, and simulates failures before production runs them.

PR checks grounded in your ADRs

A GitHub Check flags changes that contradict recorded decisions — every finding cites the ADR and the code that support it.

A living architecture graph

Components, dependencies, risk, and cost in one versioned graph — snapshots, diff, and restore built in; export to JSON, Markdown, or Mermaid.

What-if simulation

Fail a node, remove a dependency, change a cost — see the blast radius and orphaned dependencies before your users do. Simulated values are always labeled as simulated.

Why OkOvia

Built for AI economics — not repurposed

APM tools watch latency and errors. Cloud dashboards show aggregate infra bills. OkOvia is the only one that ties usage to per-feature cost and margin.

CapabilityOkOviaAPM / error toolsCloud billingDIY logging
Per-feature AI cost~
Gross margin (revenue-aware)
One-line instrumentation (drop-in tag + typed SDKs)~
Cloud GPU metering (RunPod/AWS/GCP/Azure)~~
Local vs API inference
On-device privacy redaction~~
Real-time~
Client-side quality evals (only the score leaves)~
Budget caps & automatic model fallback (policies)~

✓ native · ~ partial/manual · — not supported

Frequently asked

What data leaves my app?

Usage metadata only: model, tokens, latency, status, feature. Prompts and completions are never collected by default, and sensitive fields are redacted before sending.

How is revenue calculated?

From the revenue rules your team configures per feature, plan, or operation. OkOvia never guesses it.

How much does OkOvia cost?

It's free while in beta.

Does it meter GPUs and local inference?

Cloud GPU usage (RunPod, AWS, GCP, Azure) via a self-hosted connector today; on-device metering is included in the Swift SDK (beta).

Start measuring what your AI really costs

Free while in beta. Install the SDK, send a test event, and watch cost and margin appear in minutes.

Want to be one of the first on the release version?