AI Cost Management and Engineering Visibility Solution
See, govern, and predict the cost of AI engineering. Every token, model call, PR, and shipped feature tied to dollars, teams, and ROI against your own baseline.
What Is AI Cost Management?
AI cost management is the practice of tracking, attributing, forecasting, and optimizing AI spend across models, tools, teams, and workflows, tied to the engineering output it produced.
For GoGloby, the unit that matters is cost per shipped feature. The AI Intelligence Layer joins your AI spend to your actual work, so every dollar is attributed to a story, PR, model call, team, and the shipped feature it supported.
AI Cost Management vs. Cloud FinOps
Cloud FinOps tracks infrastructure and cloud services. AI cost management also has to see model calls, tokens, prompts, AI coding sessions, PRs, CI runs, stories, and shipped features, none of which appear on a cloud bill.
AI Cost Management vs. Engineering Dashboards
Engineering dashboards report velocity and cycle time, which are activity, not cost. AI cost management prices the work in dollars and ties it to what actually shipped.
Why AI Cost Management Breaks Inside Engineering Teams
Agentic Output Is Measured. Its Cost Isn’t.
AI 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 From 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
Expensive models routinely execute tasks a cheaper model could handle. With a large unit-price gap between model tiers and no per-task telemetry, routing waste is structurally invisible.
AI Cost Anomalies Surface Too Late
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.
Enterprise AI Cost Management 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.
AI Cost Management Metrics Engineering Leaders Track
What Engineering Leaders Measure, Sprint by Sprint
Cost Per Shipped Story
Ties the full AI cost of each completed story, from model calls to the PRs that closed it, back to the story itself. This is your core unit of value, the number that makes AI spend comparable across teams and sprints.
AI-Authored PR Rework
Tracks how much AI-generated code gets reopened, reverted, or rewritten after it first merges. High rework means you are paying twice for the same feature and shows where AI output is not yet production-ready.
Model Mix
Shows which models ran which tasks and what each tier cost against the work it produced. Expensive models routinely handle jobs a cheaper tier could do, and seeing the mix per task is how you catch that routing waste early.
Plan Versus Actual Spend
Compares forecasted AI spend to what each team actually consumed, sprint by sprint. It closes the gap between the budget you committed and the invoice you receive, and gives you next quarter’s number before you commit it.
Cost Per Merged PR
Attributes AI cost to every pull request that reaches main. It prices real engineering output at the PR level, so you can see which teams and workflows turn AI spend into shipped code most efficiently.
Failed CI Cost
Captures the AI and compute spend burned on pipeline runs that never passed. Failed CI produces nothing shippable, so tracking it surfaces the retry loops and flaky tests that quietly inflate your bill.
Built for Enterprise AI Cost Management Across the Org
We built the AI Intelligence 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.
Engineers
Your own numbers against the team average: tokens per feature, cost per merged PR, agent adoption.
Why GoGloby Is Different From AI Cost Management Tools
Observability shows traces. FinOps shows the bill. Engineering dashboards show activity. Only GoGloby prices shipped features and deploys engineers to fix what the AI Intelligence Layer finds.
Trusted by Enterprise Software Teams Managing AI Engineering at Scale
AI Cost Management Services GoGloby Delivers
Every AI Cost Signal, Every Engineering 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
AI Cost Query Tool for CTOs and Finance
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?
Which shipped features produced the highest AI cost variance this month?
Governance Model for AI Cost Management Tools
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 GoGloby for AI Cost Management
Built for budgeting, not for browsing.
Get Your AI Cost Management Benchmark
One week to connect. Deployed inside your own environment: no interviews, no auditors, no code leaving your perimeter.
What We Connect
Jira, GitHub, your IDEs, and your AI coding tools, deployed in your environment through the AI Intelligence Layer.
What You See
Cost per shipped feature, spend by team, developer, and model, real AI adoption, and waste flagged before the invoice.
What You Keep
The baseline is yours whatever you decide. If you stop after the Benchmark, you keep the numbers and the pipeline stays in your environment.
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 AI Intelligence 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 AI Intelligence 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 AI Intelligence 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.
AI cost management means tracking, forecasting, attributing, and optimizing AI spend across models, tools, teams, and workflows. For GoGloby the key metric is cost per shipped feature, so every dollar ties to the engineering output it produced, measured against your own baseline.
Cloud FinOps tracks infrastructure and cloud services. AI engineering cost management also needs model calls, tokens, prompts, AI coding sessions, PRs, CI runs, stories, and shipped features, none of which show up on a cloud bill.
AI cost management tools track model spend, cloud AI costs, token usage, budget alerts, and allocation. GoGloby differs by joining AI spend to engineering output and forward-deploying engineers to fix what the AI Intelligence Layer finds, rather than only reporting the bill.
CTOs, VPs of Engineering, platform leaders, and finance leaders at companies with growing Claude Code, Cursor, GitHub Copilot, OpenAI, Gemini, or Bedrock usage, and no clear connection between what they spend and what they shipped.
GoGloby establishes your baseline, then forward-deployed engineers work inside your team to reduce waste, set up the Agentic SDLC, and improve ROI month over month against that baseline. If you stop after the Benchmark, the baseline is yours to keep.
Contact Us
Submit your information to schedule a technical briefing. We will show you what your features cost, who really uses AI, and where delivery gets stuck, then walk you through forward-deployment in under 4 weeks.
Trusted by
Featured by
Awarded by
We follow data & security practices:
