Usage is split across vendors and tools
OpenAI, Anthropic, GitHub Copilot, Claude Code, and API usage each expose different reports, metrics, and account structures.
AI Adoption Analytics
Know who uses AI, what consumes tokens, and what it will cost
Inspectcat gives engineering and finance leaders one place to monitor AI adoption across employees, service accounts, API keys, vendors, models, token volume, and projected run-rate.
Users
employee adoption and leaderboard
Tokens
model, key, and service consumption
$
actual spend and projection
Usage, cost, and projection
Active users
People
Service usage
API keys
Token cost
Projection
Vendor mix
OpenAI78%
Anthropic54%
GitHub Copilot38%
Spend drivers
Platform automations64%
Product engineering47%
Internal AI tools31%
The visibility gap
AI spend spreads before companies can explain it
Teams adopt ChatGPT, Claude, Copilot, API keys, service accounts, and internal automations at the same time. Without a shared usage layer, leaders see invoices before they understand behavior.
People are not the only consumers
AI usage comes from employees, API keys, service accounts, Claude Code sessions, and automated product workflows.
Costs need attribution and a forward view
A monthly invoice explains the past. Teams need daily trend, top spender, run-rate, and projection signals early enough to act.
Dashboard direction
The AI Usage dashboard should explain behavior, not just show spend
The product direction is an operating view for adoption and cost: who is using AI, what is consuming tokens, which vendors and models are growing, and what the current trend means for the next invoice.
Company usage overview
Answer whether AI adoption is growing across the company or concentrated in a small group.
- Active users over time
- Employee usage versus service/API usage
- Token, request, and session volume by period
Cost and projection
Move from invoice review to a forward-looking operating view of AI spend.
- Actual token cost by vendor and model
- Projected monthly run-rate from recent usage
- Cost spikes, top cost drivers, and vendor mix
Leaderboard and attribution
Identify the people, teams, API keys, and service accounts driving adoption and spend.
- Top users and teams by activity or cost
- Service accounts and automated workflows
- API key, project, and connection-level breakdowns
Vendor and model mix
Make OpenAI, Anthropic, GitHub Copilot, and future vendors comparable enough to manage.
- Provider-level usage and cost
- Model usage distribution
- Copilot, Claude Code, and API usage in one vocabulary
Capabilities
Built around the signals Inspectcat can actually observe
Connect the providers teams already use
Inspectcat imports usage from OpenAI, Anthropic, and GitHub Copilot, with a vendor model designed to add more providers over time.
- OpenAI usage and cost APIs
- Anthropic usage, cost, and Claude Code reporting
- GitHub Copilot organization and enterprise reports
Measure adoption across people, teams, and services
Track active users, sessions, requests, API keys, service accounts, projects, models, and team-level consumption.
- Daily, weekly, and monthly active user signals
- Employee leaderboard and top spend drivers
- Token, request, session, API key, and service-account volume
Turn spend into an operating forecast
Use daily spend, provider mix, cost line items, actual token costs, and projected token costs to understand where AI spend is growing.
- Spend by vendor, model, project, API key, and connection
- Token mix across input, output, and cache usage
- Cost projection from current usage trends
Next step
Bring AI usage and spend into one operating view
Connect your providers, monitor adoption across people and services, and understand where AI cost is going before invoices become the only source of truth.
Repository readiness
AI-Ready Repositories
Start with a repository scan, get a readiness score, and fix the gaps that slow down AI-assisted engineering.
Generated documentation sites
Living Technical Docs
Define the docs you need, map the repositories behind the system, and generate maintained technical context for people and agents.