See, Govern, and Predict the Cost of AI Engineering
Every token priced in dollars and traced to the feature that produced it. The true cost of every shipped feature, the waste inside it, and next quarter’s number before you commit the budget.
Agentic Output Is Measured. Its Cost Isn’t.
Four instrumentation AI-Native engineering gaps in most engineering orgs today.
Spend is untraceable at source
LLM invoices aggregate at the org level. Token usage inside Claude Code, Cursor, and Copilot sessions is never joined to the commits, PRs, or features it produced.
No join key: feature to model calls
Jira and Linear track points and cycle time but never see tokens. Without session-to-feature attribution, cost per shipped feature is not a computable metric.
Model mix is unmanaged
Opus-class models routinely execute Sonnet-grade tasks. With a 5-10x unit-price gap and no per-task telemetry, routing waste is structurally invisible.
Anomalies surface at invoice time
Retry loops, cache misses, and long-context blowups appear as line items 30 days later, not as same-day signals with a trace ID attached.
The Organization Dashboard, Live
Human vs AI pull requests · top tasks by token spend in dollars, split by model · output vs investment, filterable by team and time window.
What Engineering Leaders Measure, Sprint by Sprint
The numbers a CTO takes into a budget review. Each one resolves to the features behind it.
Cost per shipped story
Rework rate on AI-authored PRs
Model mix per task type
Plan vs actual, per team
Cost per merged PR and review cycle
Cost of failed CI iterations
Built for CTOs, Read Across the Org
We built the Layer for the people who defend the AI budget. Every dollar resolves to a person, team, feature, and model, so the same data answers what finance and every engineer are asking too.
C-level & finance
An AI P&L per quarter: planned vs actual spend, cost per shipped feature, forecast to year-end with confidence bands. Evidence instead of estimates.
Engineering management
Cross-team spend, velocity, and model mix: spot the team burning Opus tokens on Sonnet-grade work before the invoice lands.
Team leads
Feature-level burn: estimate vs actual tokens, model mix per task, and which features are about to blow the sprint’s AI budget.
Team members
Your own numbers against the team average: tokens per feature, cost per merged PR, agent adoption.
Why GoGloby is Different
Observability shows traces. FinOps shows the bill. Neither can price a feature.
GoGloby Clients
Every Signal, Every Transport
OpenTelemetry from agents and IDEs, hooks on tool-use events, webhooks and API polling from platforms: normalized, queued, and attributed in your cloud.
Sources
Transport
traces · metrics · logs → OTLP/gRPC exporters
pre/post tool-use events from Claude Code, Cursor
push events: PRs, commits, issues, deployments
API polling & historical backfill
Copilot metrics, Cursor cloud agents
Pipeline
ingest API · auth per token
buffered, replayable
one event schema
person · team · story · model
cost & velocity marts
Insights
The Query Tool Works Like a CTO Advisor
Not a chatbot: a grounded analytics engine over your telemetry warehouse. Every number in an answer resolves to rows, traces and spans you can audit.
How an answer is produced
RBAC-scoped: members see themselves & their team, execs see the org
Cost, velocity and attribution metrics defined once — no metric drift
Read-only queries against the telemetry warehouse in your VPC
Figures cite trace/span IDs and dashboard drill-down links
Advisory-grade questions
Which teams drive our Q3 AI budget variance, and why?
Simulate: route all refactor stories to Sonnet — quarterly savings and velocity impact?
Can Front-end absorb +20% scope next quarter within the current AI budget?
Where is cache hit-rate costing us the most, per repo?
One Governance Model Applied to Every Tool in the Mix
Configuration splits between servers you control and MDM-managed workstations — Claude Code, Copilot + VS Code, and Cursor.
Customer cloud — servers
Integrations, teams, roles, API keys, telemetry & retention policy.
Per-person tokens; auth at the edge, IAM-scoped (e.g. AWS ECS).
Replayable queues; RBAC on every mart.
Read-only serving layer; nothing writes back to repos
Developer workstations — mixed toolchain
Pushed via MDM (e.g. managed-settings.json, org policies): permissions, telemetry endpoints, allow-lists; not overridable locally.
managed-settings.json → .claude/settings.json (in repo) → ~/.claude; CLAUDE.md guidance; hooks emit OTel
GitHub org Copilot policies enabled; per-user bundle sets hooks, telemetry env vars, settings.json
org Cloud-Agents API key server-side; local hooks installer + rules/skills config per machine.
repo-checked config reviewed like code; user defaults lowest precedence.
Why Engineering Leaders Choose the Layer
Built for budgeting, not for browsing.
See Your Real AI Engineering Costs
One week to connect. You get your baseline: what every feature costs in dollars, who really uses AI, and where delivery gets stuck. Deployed inside your own environment: no interviews, no auditors, no code leaving your perimeter.
FAQs
Your dashboard shows velocity and cycle time. It doesn’t show what a feature costs in dollars, and it doesn’t send anyone to fix what it found. The Layer joins your AI spend to your actual work: every model call resolves to the person, feature, and model that produced it. Keep your dashboard, it won’t argue with ours.
That’s the Benchmark. One week, your environment, connected to your Jira and GitHub. You see your real numbers before committing to anything, and the baseline is yours whatever you decide.
No. The full pipeline deploys into your VPC: collector, queue, warehouse, dashboards. Telemetry never leaves the account, every figure resolves to a trace ID you can audit, and access is IAM-scoped with SSO and RBAC.
Features from your own backlog, graded by complexity, passing your quality gates. The Layer prices them in dollars and compares like with like: what a medium feature costs you now versus three months ago. Your definitions, your data.
The Layer prices work, not people watching. Every developer sees their own numbers against the team average, managers see teams, execs see the org: RBAC by design. The conversation shifts from “who’s slow” to “which tasks burn money on the wrong model,” and that’s a conversation engineers want to have.
First numbers in one week: the collector connects to Jira, GitHub, and your AI tools, and historical backfill starts pricing your recent work immediately. A full baseline builds over the first month.
Contact Us
Submit your information to schedule a technical briefing. We will walk you through how we put Claude into production safely and our under 4-week embedding process.
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