# ForgeMeter — Full Content Index > Meter your team's AI spend across Cursor, Copilot, and Claude. Unified dashboards, budget alerts, and ROI insights for engineering teams. This file provides extended excerpts for AI crawlers and answer engines. For a concise index, see llms.txt at the site root. --- ## Product summary ForgeMeter is an AI Engineering Intelligence platform. It unifies usage data from Cursor, GitHub Copilot, OpenAI, and Anthropic into a single dashboard. Engineering leaders track costs per developer, per repo, and per model; set budget alerts; and generate ROI summaries using official APIs only (no traffic interception). Website: https://forgemeter.com Contact: https://forgemeter.com/contact ## Pricing (planned) - **Free** — 1 integration, 7-day history, up to 5 developers - **Team ($99/mo)** — 3 integrations, 90-day history, 25 developers, AI summaries, budget alerts - **Pro ($299/mo)** — Unlimited integrations, 1-year history, API access, repo-level analytics --- ## Blog articles ### How to Track Cursor AI Usage Across Your Engineering Team URL: https://forgemeter.com/blog/cursor-ai-usage-tracking Published: 2026-06-20 Category: Cursor & IDE Tools A practical guide for engineering leaders on measuring Cursor adoption, token usage, and per-developer AI spend using official APIs. Engineering teams are adopting Cursor faster than finance can track the bill. Tab completions, agent edits, and background models all show up on the same invoice — but almost none of it is visible in your existing engineering dashboards. If you're an engineering leader trying to answer "How much are we spending on Cursor, and is it worth it?", you need usage data tied to developers, repos, and models — not a single monthly line item. Why Cursor usage is hard to track Unlike traditional SaaS seats, Cursor consumption is usage-based. A power user running agent loops on a large codebase can cost 10× what a casual tab-completion user costs — on the same plan. Most teams discover this only after the invoice arrives. By then, you've already burned budget on: - Expensive models used for tasks a cheaper model would handle - Agent sessions that loop without meaningful output - Developers duplicating work across Cursor and Copilot The fix isn't blocking Cursor. It's metering it. What to measure Start with four metrics that map directly to cost and productivity: 1. Daily active users (DAU) — Who is actually using AI-assisted development? 2. Model distribution — Which models drive the most spend? 3. Per-developer token usage — Where are the outliers? 4. Agent vs. tab completion ratio — Are agent workflows delivering value? Cursor Enterprise provides an Analytics API that exposes team-level usage without intercepting traffic or modifying developer workflows. A lightweight tracking workflow Here's a workflow that works for teams of 10–200 engineers: Step 1: Connect official APIs Use read-only API access to pull usage on a schedule. Avoid proxy-based or MITM approaches — they create security review blockers and break when Cursor updates. Step 2: Normalize across tools Most teams don't run Cursor alone. They also have GitHub Copilot, direct OpenAI keys, and Claude API access. Normalize all usage into a single schema: developer, tool, model, tokens, estimated cost, timestamp. Step 3: Set budget thresholds Define monthly budgets per team or per developer tier. Alert at 80% and 100% — before the invoice, not after. Step 4: Review weekly, not quarterly AI tool adoption changes weekly. A monthly finance review is too slow. Engineering managers should see a 7-day rolling view of spend and adoption trends. Common mistakes to avoid - Counting seats instead of usage. A team of 50 with 40 daily active users and 5 power users has a very different cost profile t… --- ### GitHub Copilot Cost Analytics: What Engineering Leaders Need to Know URL: https://forgemeter.com/blog/github-copilot-cost-analytics Published: 2026-06-15 Category: GitHub Copilot Learn how to measure Copilot adoption, acceptance rates, and per-developer activity using GitHub's official metrics API — and connect it to your AI spend story. GitHub Copilot is often the first AI tool an enterprise buys for engineering. It's familiar, integrated into the IDE, and easy to procure. What's harder is answering the question your CFO will ask: "Is Copilot actually being used, and what is it costing us per developer?" Copilot billing and Copilot usage are two different problems. This guide covers the second one. The metrics that matter GitHub exposes a Copilot Usage Metrics API for organizations. The most actionable signals for engineering leaders are: | Metric | Why it matters | |--------|----------------| | Suggestion acceptance rate | Low acceptance = developers ignoring suggestions or wrong context | | Active users vs. licensed seats | Paying for seats nobody uses | | Lines accepted per developer | Proxy for productivity — with caveats | | Language breakdown | Adoption varies wildly by stack | | IDE distribution | VS Code vs. JetBrains vs. Neovim tells you where to invest in training | Acceptance rate alone is misleading. A team accepting 30% of suggestions on complex refactors may be more productive than a team accepting 60% on boilerplate CRUD. Context matters — but you still need the data. Copilot vs. Cursor: the overlap problem Most mid-size engineering orgs now run both Copilot and Cursor. That creates three blind spots: 1. Double billing — Two AI subscriptions per developer 2. Unclear tool preference — Developers pick whichever is faster, not whichever is cheaper 3. Fragmented reporting — Copilot metrics in GitHub, Cursor metrics in Cursor, API keys in OpenAI Engineering leaders need a single view. Copilot metrics should sit alongside Cursor and direct API usage — not in a silo. Building a Copilot analytics practice Start with adoption, not cost Before optimizing spend, confirm adoption. If only 40% of licensed developers used Copilot in the last 30 days, your problem is enablement — not model selection. Run a 2-week baseline: - Daily active Copilot users - Acceptance rate by team - Top 10 users by activity (identify champions, not to punish low users) Connect usage to outcomes Copilot metrics become powerful when paired with delivery signals: PR cycle time, review comments, incident rate. You don't need perfect causation — directional correlation is enough for quarterly business reviews. Set governance early Define which repos and teams get Copilot Business vs. Enterprise features. Document when developers should use Copilot vs. Cursor vs. direct API access. Ambiguity drive… --- ### Why Your Engineering Team Needs an AI Spend Dashboard in 2026 URL: https://forgemeter.com/blog/ai-engineering-spend-dashboard Published: 2026-06-10 Category: AI Spend & ROI AI tool sprawl is creating invisible engineering costs. Here's why a unified AI spend dashboard is becoming essential for CTOs and engineering managers. In 2024, the question was "Should we let developers use AI?" In 2026, the question is "How much is AI costing us, and can we prove it's worth it?" The average mid-market engineering team now runs a stack that looks like this: - Cursor for AI-native IDE workflows - GitHub Copilot for inline completions - OpenAI API keys for internal tools and scripts - Claude for code review, docs, and agent workflows Each tool has its own billing portal, usage model, and admin console. None of them talk to each other. The result is an AI spend blind spot that shows up as a growing line item with no owner. The cost of invisible AI spend When AI costs are fragmented, three things happen: 1. Budget surprises Finance sees four separate invoices. Engineering sees adoption going up. Nobody connects the two until QBR prep — when it's too late to adjust. 2. No ROI narrative You can't justify a $50K/year AI tooling budget to the board with "developers like it." You need adoption curves, cost-per-developer trends, and examples of time saved — tied to real usage data. 3. Governance gaps Without centralized visibility, you can't enforce model policies, team budgets, or allowlists. Shadow AI usage through personal API keys becomes the path of least resistance. What an AI spend dashboard should do Not every analytics tool fits this problem. Production LLM observability tools (Langfuse, Helicone) trace application requests. Engineering analytics platforms (Jellyfish, Faros) focus on delivery metrics. You need something in between: An AI engineering intelligence layer that answers: - How much did we spend on AI dev tools this month? - Which developers and teams drive the most usage? - Which models are we paying premium prices for? - Are we about to exceed budget? A good dashboard connects via official APIs only — Cursor Enterprise Analytics, GitHub Copilot Metrics, OpenAI Usage API, Anthropic Usage API. No MITM proxies, no browser extensions that read keystrokes. The ROI framework that works Engineering leaders who successfully defend AI budgets use a simple framework: 1. Baseline — Measure current spend and adoption for 30 days 2. Benchmark — Compare cost-per-developer against industry peers (or your own historical data) 3. Optimize — Route tasks to appropriate models, retire unused seats, train low-adoption teams 4. Report — Monthly summary for leadership: spend, adoption, savings, and next actions Teams using this framework typically find 20–30% savings in the first… --- Last updated: 2026-06-29