Consolidate your AI stack. Control your AI spend.
One runtime across every model, agent, and tool. One identity plane. One event surface. Per-team and per-workflow cost attribution enforced at the Gateway — not estimated from vendor invoices.
Your AI stack is sprawling faster than your ability to govern or pay for it.
Every team has its own wrapper. Every wrapper integrates with a different subset of models. Every model vendor bills differently. Every orchestrator has its own identity and policy story.
The cost of this is not only sprawl. It is that you cannot answer three questions at once: who is using what, under what policy, for how much. Your FinOps dashboard says one thing. Your security review says another. Your executive AI inventory says a third.
Consolidation is not a cleanup project. It is a control primitive.
Outcomes the buyer can underwrite.
- One invocation contract
- Every model, agent, tool, and workflow invoked through the same SDK / HTTP surface. Vendor switches become routing decisions, not rewrites.
- Per-workflow cost attribution
- Token-level cost attributed to the executing identity, the workflow, the team — enforced at the Gateway, not reconciled from invoices a month later.
- One system of record
- A single event surface for every AI-initiated action. Your inventory, your audit trail, your observability, and your FinOps all read from the same stream.
- Storage is predictable
- ~1.1 KB compressed per standard 8-event trace. At 10,000 executions per day with 30-day retention, ~330 MB of event-stream storage. Captured inputs (tool responses, retrieved documents, LLM context) are accounted separately and are workload-dependent. Rhodes & Kang (2026), §9.
One runtime. One plane. One record. Two named operational constraints.
One identity plane.
Every call carries a verifiable caller identity through to the downstream system. Identity is credentialed, not prompted. That is the foundation both governance and cost attribution rest on.
Per-stage model routing.
Hosted vs. local model selection is a runtime decision per workflow stage. Pick the cheapest model that meets the sensitivity and latency bar — without rewriting anything.
O1 · Budget Closure.
Execution halts at or before the declared ceiling across seven cost dimensions — LLM tokens, tool invocations, external API, compute, storage, risk units, compliance overhead. Budgets cannot be overrun; they are refused. A runtime constraint, not a dashboard alert.
O2 · Cost Monotonicity.
Cumulative cost is non-decreasing; recorded cost ≥ actual cost at any point. No retroactive ledger edits. The cost field a verifier replays matches what the auditor reviews.
Eight day-14 jobs your platform org ships.
Consolidate N team wrappers onto one runtime.
Every point-to-point model integration moves behind the Gateway. One invocation contract, one identity plane, one event surface. Switching providers becomes a routing decision.
Set per-team budget ceilings across seven cost dimensions.
O1 Budget Closure. LLM tokens, tool invocations, external API, compute, storage, risk units, compliance overhead. Budgets cannot be overrun — they are refused at the Gateway.
Swap an LLM provider mid-pipeline without a rewrite.
Per-stage model routing is a deployment config, not a code change. Workflow definition remains identical; the Gateway routes the stage to the new endpoint.
Route a sensitive workload to local-only models.
Policy pins the stage to local inference. Hosted models are refused for that class of workload by the Gateway. No narrative trust required.
Stand up a new agent type on the existing policy plane.
Identity, scope, and policy rules inherit automatically. The new agent gets the same governance surface as everything else on the runtime — no bolt-on per-agent governance story.
Generate the quarterly cost attribution by workflow and team.
O2 Cost Monotonicity. Cumulative cost is non-decreasing, sealed in the event stream. Your FinOps report reads from the same trace your auditor reads from.
Publish an LLM capability-result schema to your SDK consumers.
model_id, model_version, endpoint_digest, temperature, top_p, seed, tokenizer_hash, rag_corpus_digest, prompt_hash — all captured, all replayable. Your internal SDK consumers adopt one envelope shape.
Define and enforce a Gateway fail-mode for a workload class.
Fail-closed default for safety-critical workloads; different policies per tenant or workload class. Configurable at deployment; enforced structurally.
Bring us your AI inventory.
We will map your current model, tool, and team sprawl to a single governed runtime — and show you the per-workflow cost picture you cannot get today.