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Frontier Cockpit
by Paula Silva
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GitHub Copilot observability for engineering teams

See what your AI spends, learns, and delivers. Local by default, Azure by choice.

Frontier Cockpit turns the OpenTelemetry signals GitHub Copilot already emits into developer coaching, AI Credits cost control, and enterprise governance, without raw prompts ever leaving the developer's machine.

Get started in three commands View the repository
SOURCE VS Code and GitHub Copilot, OTLP signals COLLECT AND STORE, LOCAL Collector, Tempo, Prometheus, Loki, Aspire EXPLAIN Cockpit app, Grafana, coaching cards OPTIONAL, SANITIZED FORWARD TO AZURE
Why now

AI usage observability became a first-class engineering discipline.

Three shifts in 2025 and 2026 turned "how much is our AI costing, and is it helping" into a question every engineering organization now has to answer with data.

Per token
Usage-based billing. GitHub Copilot is moving to AI Credits, so cache reuse, context size, and model mix now translate into cost on every request.
90%
Adoption is near-universal. The 2025 DORA report found roughly 90 percent AI adoption, with higher throughput but pressure on delivery stability. Fast feedback loops are the control system.
Top ask
FinOps for AI. The State of FinOps 2026 ranks managing AI and GenAI spend among the highest priorities, with unit economics and cost visibility the open challenges.
Educate

Developers learn their own patterns

Cold context, cache misses, oversized prompts, and error loops become visible minutes after they happen, with coaching cards that explain what to change and why it matters.

Impact

Leaders get honest numbers

AI Credits burn versus allowance, projection to end of month, model cost concentration, and adoption quality signals, clearly labeled as operational telemetry, never fabricated billing.

Govern

Platform teams keep the boundary

Raw content stays local. The optional Azure path forwards only sanitized, redacted telemetry to client-owned resources, with the same redaction applied twice, locally and in the cloud.

Two editions, one product

Start local for every developer. Add Azure when the organization needs governance.

Developer Edition

Frontier Cockpit Local

A complete observability stack on the developer's machine: OpenTelemetry Collector, Aspire Dashboard, Prometheus, Tempo, Loki, Grafana, scheduled jobs, and a purpose-built cockpit app at localhost:3300.

  • One hundred percent local, all ports bind to 127.0.0.1
  • Content capture off by default, opt-in only
  • Runs identically on macOS, Linux, and Windows
  • AI Credits estimates with cache and context coaching
Enterprise Edition

Frontier Cockpit Hybrid

Everything in Local, plus sanitized forwarding to client-owned Azure: Container Apps collector, Application Insights, Log Analytics, Azure Monitor workspace, and Azure Managed Grafana, deployed with Bicep.

  • Raw prompts and tool payloads never leave the machine
  • Defense in depth, redaction applied locally and in Azure
  • Dev, test, and prod parameter sets with cost tags
  • GitHub Enterprise audit log and usage metrics ingestion
Metrics that change behavior

Volume tells you what happened. Efficiency and outcomes tell you what to do next.

Frontier Cockpit pairs raw counts with the efficiency, cost, and outcome signals a developer, a tech lead, and a FinOps owner each need.

01
Cost and FinOps
AI Credits estimates per session, repository, and model. Monthly budget utilization with projection and alert levels. Cache savings expressed in credits, the number executives remember.
02
Context and cache
Hot, warm, and cold token composition. Context utilization, typical and peak. Compaction events. The coach flags cold starts and context pressure before they burn the budget.
03
Experience and outcomes
Time to first token, response duration, tool call depth, error rates, edit acceptance, and edit survival without revert, attributed to real Git workspaces, branches, and sessions.
04
Data quality and coverage
Workspace attribution, observed versus expected coverage, and materializer freshness, so every number carries an honest confidence signal instead of a false sense of completeness.

Local AIU and AI Credits values are operational estimates for planning and coaching. Official GitHub Copilot billing and adoption reporting come from GitHub billing exports and the GitHub Copilot usage metrics API, which the product ingests where available.

How it works

Receive, store, summarize, and explain the signals GitHub Copilot already emits.

GitHub Copilot in VS Code emits OpenTelemetry traces, metrics, and logs following the GenAI semantic conventions. Frontier Cockpit gives them a home and a meaning.

VS Code and GitHub Copilot→ Local OTel Collector→ Tempo, Prometheus, Loki, Aspire→ Jobs, materializer and rollup→ Cockpit app, Grafana, Coach
Local Collector→ Redaction pipeline, deletes raw content→ Azure Container Apps collector→ Application Insights, Log Analytics, Managed Grafana

Ten pinned containers, healthchecked, loopback-only, with scheduled processing inside Docker so macOS, Linux, and Windows behave identically. Continuous integration validates shell quality, app builds, compose configuration, dashboards, and secret scanning on every change.

Privacy first, by construction

Content capture, meaning raw prompts, responses, file paths, tool arguments and results, is disabled by default at every layer and requires an explicit opt-in per installation. In hybrid mode, dedicated pipelines delete raw content attributes before anything leaves the machine, and the cloud collector applies the same redaction again. Credentials are generated per installation, so nothing ships with default passwords.

Get started

From a fresh clone to a working cockpit in three commands.

Requires Docker Desktop or Docker Engine on Linux, Git, Python 3, and VS Code with GitHub Copilot.

# macOS or Linux
git clone https://github.com/paulasilvatech/frontier-cockpit.git && cd frontier-cockpit
cp local-otel/client.env.example local-otel/client.env
bash local-otel/client-bootstrap.sh

# Windows, PowerShell 7
pwsh -ExecutionPolicy Bypass -File local-otel/client-bootstrap.ps1

Then reload VS Code, run one GitHub Copilot Chat or agent request inside a Git repository, and open the local cockpit at localhost:3300.

Grounded in current research

Built on public GitHub, Microsoft, and industry sources.

  • GitHub Docs and changelog on GitHub Copilot usage-based billing and AI Credits allowances, requests, and budgets.
  • GitHub changelog, GitHub Copilot metrics general availability, including per-user AI Credits in the usage metrics API.
  • Visual Studio Code release notes, GitHub Copilot agent monitoring with OpenTelemetry.
  • DORA, State of AI-assisted Software Development, 2025, and follow-on 2026 research on AI impact.
  • FinOps Foundation, State of FinOps 2026, and the FOCUS specification on virtual currency and token cost.
  • OpenTelemetry GenAI semantic conventions and the OpenTelemetry AI agent observability guidance.
  • Peer-reviewed research on GitHub Copilot's measured impact on developer productivity.
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About

Who built this, and why.

I am Paula Silva, an AI-native software engineer and Software Global Black Belt. My work spans the full AI transformation journey, from application and legacy modernization to agentic architecture and platform engineering, helping enterprise teams adopt AI-assisted development at scale. GitHub Copilot is one tool in that practice, not the whole of it.

Frontier Cockpit grew out of one recurring question inside that broader practice: what is our AI actually costing, is it making developers better, and how do we govern it without reading everyone's prompts. It is local and private by default, with an optional path to governed Azure history when the organization is ready.

It is a client-run package, designed to be installed, taught, and adapted. The goal is not just a dashboard, it is helping developers and leaders learn from their own AI usage.

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