TensorCost TensorCost
TensorCost vs Workato

Workato governs what an agent may do. We govern what the call was worth.

Workato shipped a real LLM gateway, not a marketing page: one endpoint in front of OpenAI, Anthropic, Bedrock, Azure OpenAI and Gemini, token accounting normalized per request with reasoning tokens split out, monthly budgets and per-minute limits per virtual key, PII masking and injection screening on the way through. Adoption is a base URL and a key, which is less friction than we ask for. It sits next to 14,000 connectors we will never build. What it does not do is test the swap or verify the saving — there is no judge in the product and no savings number is claimed anywhere in it.

At a glance

Six rows. We lose the first two and say so.

Workato
TensorCost
Token accounting normalized per request across every provider, attributed by virtual key, team, model, provider and tag, with reasoning tokens broken out separately. Metered on the wire rather than read off an invoice.
The same thing, plus a daily tie-out against each provider's own reported usage and a number that shows what stayed unattributed instead of absorbing it into the total. This row is parity, not an advantage, and we would rather write that than pretend otherwise.
Routes by virtual key, team or tag with a workspace default and overrides, one request format across every provider, automatic fallback when one degrades, and an approved-model list that runs in enforce or observe-only.
The same moves. The difference is what has to be true before a route is allowed to change: a policy cannot arm until it has earned shadow proof on your own traffic, and it disarms itself if quality slips afterwards.
Comparing models on real traffic is offered to data science teams, but nothing published describes a test. No judge, no sequential analysis, no automatic revert. Changing the routing rule is the whole mechanism.
Every routed pair judged twice with the two answers swapped between passes, so whichever one the judge reads first cannot win on that alone. Four axes, and a safety regression in either pass fails the swap outright rather than being averaged away. An always-valid sequential test keeps watching after the change is live and reverts the policy the moment the evidence turns.
No savings figure is claimed, verified or otherwise. Budgets, alerts before the ceiling, and per-key caps — spend is shown and capped, which is not the same as showing that a change is what made it fall.
Thirty days either side of an accepted change, measured against a control cohort of comparable workloads that did not change, so it is not "spend fell while we happened to be installed". The ledger can read negative, and when it does it says so.
A self-hosted or fine-tuned model can be registered as a provider and governed like a commercial one. Utilization, depreciation and real $/GPU-hour sit outside the product — registering an endpoint is not telemetry.
An agent that reads the driver directly on bare metal, with no Kubernetes, labels training phase so a checkpoint write is not mistaken for an idle card, and joins capex amortization to token spend so build-against-buy is a measured number rather than a typed-in rate. Through Hopper; no Blackwell yet.
Bought as part of an iPaaS, priced on task consumption, sales-led, and the AI gateway is gated to specific plans. They hold SOC 2 Type II, ISO 27001 and HIPAA, and their audit trail is built for EU AI Act and SOX programs.
Flat subscription, three public tiers, no usage meter, and nothing in the bill computed off the savings we find. Our audit log is hash-chained and you can re-derive it yourself with sha256sum — but SOC 2 is on the roadmap with no auditor engaged and no date. If a certificate is a hard gate for you this quarter, that is a real reason to buy them and not us.

Where we differ

A route without a test is a bet

Workato will move your traffic to a cheaper model on a routing rule, and the rule takes effect on the next request. Whether the cheaper model still answers as well is left for you to discover in production, which is exactly the reason the recommendation sits in a dashboard at most companies and nobody acts on it — whoever swaps the model owns the regression. Removing that risk is the product, not a feature of it: two passes with the order swapped, a safety regression that hard-gates the result, and a test that keeps running after you ship.

Most teams should run both

Their gateway exists to feed an agent platform, and its cost view sees the traffic that goes through their endpoint. Training runs, owned GPUs and any inference that never touches Workato are invisible to it. In the other direction, 14,000 connectors and a governed path into Workday and SAP are not things we will ever have. The honest split: they decide which systems an agent may touch, we decide whether the model behind it should have been cheaper and prove the answer held.

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