One control plane for every AI model you use.
Arcumo AI Gateway sits between your applications and every model provider behind them. Your teams ask for a capability — not a model — and the gateway decides what serves it, enforces what is allowed, keeps working when a provider does not, and prices every request as it happens.
AI sprawl happens one integration at a time.
No organization decides to run four model providers. It happens because one team shipped against OpenAI, another standardized on Claude, a third inherited Gemini with a platform, and a fourth went through Bedrock because procurement was already there. Each arrives with its own key, its own bill, its own dashboard and its own idea of what data is allowed to leave.
The result is familiar. Nobody can answer what AI actually costs by product or team. Applications are welded to a single provider, so one outage or rate limit becomes an incident. Expensive models keep serving work that a cheaper one handles perfectly well. And the security team has no place to stand between sensitive data and a provider it never approved.
These are not four problems. They are one missing layer.
WITHOUT A GATEWAY
WITH ARCUMO AI GATEWAY
Applications hold an Arcumo key, never a raw provider credential. Providers can be added, swapped or removed without touching application code.
Ask for a capability. Not a model.
An application sends the task it needs done — summarize this filing, plan this action, answer from this knowledge base — along with who is asking and how good the answer needs to be. Everything after that is the gateway's decision, and every part of it is policy you control.
Applications never hold provider credentials, and never name a model. Routing is policy, not code.
- Security and policy Virtual Arcumo keys instead of raw provider credentials, per-tenant rules for which providers may receive which categories of data, and a refusal — not a silent downgrade — when nothing compliant can serve a request.
- Intelligent routing Each task carries a policy: the ordered models that may serve it, the quality tier, the token ceiling and the maximum acceptable cost. Changing where work runs is a configuration change, not a release.
- Reliability and failover Retries with backoff, per-model circuit breakers and automatic failover to the next approved model. A provider outage degrades cost or latency instead of taking an application down.
- Cost governance Every request priced against the live provider rate card and attributed to a product, tenant and task. Budgets are enforced at the gateway, so an overrun is a declined request rather than a surprise invoice.
- Caching and optimization Prompt caching and response reuse where a task allows it, so repeated context stops being repeatedly billed — and cheaper models are used wherever they measurably hold up.
- Observability Structured telemetry on every call: model chosen, why it was chosen, tokens in and out, cache hits, failovers, latency and cost. Including the attempts that failed and still billed.
You cannot optimize what nobody is measuring.
Most organizations discover their AI spend monthly, as a single number on a provider invoice, with no way to attribute it to a product or a decision. The gateway prices every request at the moment it happens, which turns that number into something you can act on.
That makes the ordinary optimization questions answerable. Which workloads are paying premium-model rates for work a cheaper model handles? Which prompts repeat enough context to be worth caching? Which applications are trending toward their budget before the month ends?
We hold ourselves to the same standard we would ask of a vendor: savings claims should come from a measured baseline, not an estimate. The gateway records what a request actually cost and what the alternative would have cost, so the comparison is auditable rather than asserted.
OPTIMIZATION OPPORTUNITIES
- Requests eligible for a lower-cost model
- Repeated context eligible for prompt caching
- Applications trending past their configured budget
- Failed attempts that still consumed billed tokens
Illustrative interface. Representative of the reporting shape, not of any customer's results.
Run it where your data policy says it has to run.
The gateway is the layer that sees every prompt your organization sends. Where it runs is a security decision, so it is yours to make — not a constraint of how we happen to package it.
Bedrock-first, not Bedrock-only. Many organizations already have an AWS agreement, a security review and a billing relationship in place. Where that is true, routing through Amazon Bedrock avoids adding a new vendor to the estate. Where it is not, the same gateway calls provider APIs directly — the routing policy changes, the applications do not.
We built it because we needed it.
The gateway exists because Arcumo was about to duplicate provider integrations, routing and cost tracking across two products. It runs in production today, serving live traffic for our own systems.
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Proven on our own workloads first
TradeviaIQ™ runs its language-model work through the gateway rather than against a provider directly. We are the first system that breaks if the routing, failover or cost accounting is wrong.
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Measured, not assumed
Routing decisions are validated against real workloads before they ship. When we tested whether a cheaper model could serve a production task, it could not — so the policy leads with the model that actually works. The number of times that assumption fails is exactly why the measurement matters.
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Open to design partners
We are working with a small number of organizations to validate the gateway against workloads other than our own — cost, quality, latency, reliability and the operational burden of running it. Partners get discounted access and direct influence on what gets built.
Ways to start.
A gateway is worth deploying when there is something to govern. Most engagements begin by establishing that, and several stop there because the answer was enough.
AI cost optimization assessment
We inventory the providers, models, prompts and applications in play, analyze token usage, latency, retries, duplicated work and cacheability, and benchmark lower-cost alternatives on your workloads. You get a prioritized plan with the savings each change is expected to produce — and what it would cost to make.
Gateway implementation
Deployment into your environment, routing policies written against your actual tasks, budgets and data-handling rules configured per tenant, and your applications migrated one workload at a time — starting with something non-critical so the first measurement is a safe one.
Managed AI FinOps
Ongoing optimization as the model landscape moves. New models arrive, prices change and workloads drift; the routing policy that was right last quarter usually is not right now. We keep it current and report on what changed and what it saved.
Engagements are scoped to what you are trying to decide. If you are not yet sure which of these applies, the assessment is the one that tells you — start there.
Find out what your AI actually costs.
Most teams are surprised by the answer, and by how much of the spend turns out to be optional. We are taking on a small number of design partners for the gateway.
Arcumo AI Gateway is a product of Arcumo LLC.