The control plane for enterprise AI.
Route, govern, and meter every AI request across your models, clouds, GPUs, and private endpoints. The cost decision gets made before the request goes out, not in a spreadsheet after the bill lands.
Every request scored live on cost, latency, and quality. The best-fit route wins, with a fallback attached.
cost reduction on production workloads
routing overhead per request
lower cost vs a hardcoded Claude Sonnet setup
faster than that baseline
AI compute is becoming too expensive to hardcode.
- ModelOne, chosen once
- ProviderFixed
- RegionFixed
- RoutingNone — every request goes the same place
- CostAn assumption, not a measurement
- ModelBest fit, per request
- ProviderScored live, with a fallback
- RegionChosen by residency rule
- RoutingCost, latency, quality, and compliance
- CostAttributed, capped, and on the record
How Agentry controls every AI request.
Read the docsA gateway runs the rule you wrote. Agentry works out whether that rule is still right.
CloudverseLogs{
"status": 200,
"headers": { "Content-Type": "application/json" },
"body": {},
"responseTime": 0.1129,
"lastUsedOptionJsonPath": ""
}The cost of not routing.
| Monthly volume | Hardcoded spend | With Agentry | Monthly saving |
|---|---|---|---|
| 1M requests | $2,980 | $298 | $2,682 |
| 5M requests | $14,900 | $1,490 | $13,410 |
| 10M requests | $29,800 | $2,980 | $26,820 |
| 50M requests | $149,000 | $14,900 | $134,100 |
Assumption: 40–90% reduction applied at an 89% average. Your mix will differ; the audit measures yours.
- Model
- Claude Sonnet
- Latency
- 5,537 ms
- Cost / request
- $0.00298
- Model
- GPT-4o-mini
- Latency
- 3,962 ms-28.5%
- Cost / request
- $0.00010-96.8%
In the call path. Deployed in your environment.
Runs in your environment
Your government or commercial cloud tenancy, or on-premises. The data plane stays inside your boundary.
Prompt capture is a policy, per workload
Full capture, redacted, or metadata-only. Your choice, per scope.
Six-role RBAC across every surface
Reading a prompt is itself a governed, permissioned action.
Hash-verified records
Every prompt view logged. “Who read this” and “what did it cost” are both queryable.
Built for enterprise AI governance.
Read the docsMulti-tenant isolation
Workloads, policies, and cost kept fully separate.
Data residency controls
Route by region to meet sovereignty rules.
PII handling rules
Detection enforced before provider selection.
Budget caps
Hard ceilings applied before a request goes out.
Org / team policy scopes
Different teams run under different constraint sets.
Full execution trace logs
Every decision timed, scored, and logged.
No black boxes.
Every routing decision includes an evidence summary: the constraints active, the routes evaluated, the one selected, and why. If you can't explain a decision, you can't govern it.
SOC2 and ISO-aligned architecture. Audit trails, access controls, and policy enforcement structured to support compliance documentation. Specific certification status confirmed during your evaluation.
{
"requestId": "req_a1b2c3",
"constraints": {
"budget_cap": "$0.0005",
"region": "EU",
"latency_max": 4000
},
"routesEvaluated": 4,
"selectedRoute": "GPT-4o-mini (Together AI, eu-west)",
"reason": "Lowest cost within latency and region constraints",
"result": {
"cost": "$0.00010/req",
"latency": "3,962ms",
"fallback": "Mistral-Large (eu-central)"
}
}Own your stuff, or your vendor owns your advantage.
Under your policy
The distilled know-how of your business, versioned and served from your control plane, not siloed inside a vendor's platform.
A routing decision, not a lock-in
The model is a routing decision under your constraints: swap providers, stay cloud-agnostic, and keep your own SLAs.
An append-only ledger
Every execution recorded on an audit trail you control, retained on your terms, closed to any single vendor's roadmap.
"Switching models without losing institutional learning is the sovereignty test."
Four problems Agentry fixes.
The routing problems teams actually hit in production — and how Agentry resolves each one at decision time.
Multi-provider cost arbitrage
Requests are profiled by task complexity and routed to the cheapest model that holds quality — the same 15M calls drop from ~$44.7k to ~$12.6k a month.
Learn MoreMost teams find at least one ungoverned agent in the first onboarding call.
Start a 3-week private pilotConnect once. Route everywhere.
Read the integration docsSupported model providers
Trusted by teams running cloud and AI in production.
Optimization, governance, usage visibility, and AI spend controls across Azure, OpenAI, and Azure AI, running in production.
Enterprise cloud, data, and AI spend optimization and governance across providers, warehouses, and LLMs.
Enterprise governance, commitment management, and AI spend governance across cloud and AI services.
Already governing your cloud, data, or SaaS spend?
Agentry runs on the same platform as CloudVerse Technology Spend: one data model, one login, no re-procurement. If cloud, data, or SaaS spend is also on your plate, see the Technology Spend Platform.
Frequently Asked Questions
Common questions we get asked the most