Your AI spend outran your governance. Take it back.
Attribute every dollar to a team, a feature, a use case. Route every workload to the right model. Walk into the budget conversation with numbers you can defend.
The model you picked once is now the expensive one.
What the gap costs while you decide.
- AI spend you can't cleanly attribute to a team, feature, or use case.
- Unit economics borrowed from infrastructure, not sized for tokens and GPUs.
- Inference and GPU cost that shifts every week as agents and copilots scale.
- The "is this worth it?" question from finance you can't yet answer with confidence.
What Agentry does for AI engineering teams.
Multi-provider routing
Every request scored across providers on cost, latency, and quality. Best-fit wins, fallback attached.
Policy guardrails
Allowed providers, residency, and budget enforced before execution.
Right-sized GPU economics
Move workloads between hosted APIs and dedicated GPU pools as price and load change.
Spend attribution
Cost allocated to the team, product, and workload that ran it, automatically.
Token and GPU visibility
Token-level tracking and GPU utilization in one view, per model and per run.
Model registry and failover
Versioned models with automatic failover when a provider degrades.
How Agentry controls
every AI request.
Read the docsTrigger when a new deal record is created.
Enrich the record with web research.
Score the lead and route to the right AE.
Route lead to Enterprise and draft outreach.
Route lead to SMB and draft outreach.
What changes once Agentry is routing.
Benchmarked figures are labeled. Ranges are drawn from production workloads.
Outcomes you can take to the board.
- Attribution. AI spend mapped to teams, features, and use cases.
- Routing. the 10–100x waste of the wrong model for a job, gone.
- AI-native unit economics. cost per request, per feature, per user.
- Governance. policy, access, residency, and vendor oversight in one place.
- Shadow AI, surfaced. the spend leaving engineering through Copilot, Cursor, and agents.
- Credibility. a number that holds up in front of finance, the CEO, and the board.
The people who answer for the AI bill.
Every model route explained and priced, so the AI number holds up in front of the board.
Governance and cost on one model, so growth doesn't mean losing control of spend.
Routing and fallback logged automatically, with no chasing why a request went where it did.
Cost per request, per feature, per user, without instrumenting it by hand.
One policy layer across every provider and GPU pool in production.
AI engineering questions, answered.
What Heads of AI and MLOps leads ask first.