GSX meters agent spend at the point of execution, so you know exactly what to optimize or shut down.
Provider invoices tell you what you spent last month. They don't tell you which agent spent it, on what task, or whether the work it did was worth the tokens it burned.
GSX Agent FinOps close that gap:
Monthly provider bills aren’t itemized and can't create agent spend accountability.
Knowing an agent burned 8,296 tokens a call means little until you know what it delivered.
Agents may be running on LangChain, CrewAI or your own platform. Spend scatters across them.
For the people who need to answer, “How much does the agent cost?”
Attribute and cross-charge AI spend: See spend by department, region or use case, and cross-charge it back to the teams that consumed it.
Right-size every model call: Route each task to the most cost-effective model, cache what repeats, and keep deterministic work out of the LLM entirely.
Prove return on every agent: Report cost against the success criteria each agent was built to hit, so every dollar of consumption maps to an outcome.
Every time an agent acts, it spends money. GSX shows what each of those decisions costs while it happens, not after. Metering without an objective is just accounting: every GSX agent is built against declared success criteria, so every token has something to be measured against.
Token counts, model calls, tool and API invocations are captured in real-time, rather than reconstructed from an invoice retroactively.
Custom metadata tags on any step in any agent, so spend is trackable by agent, flow, step, model, department or region.
Goal-oriented design puts cost data next to the customer-defined success criteria each agent was built to hit.
GSX controls cost in three places: when an agent is built, while it runs, and at the moment money is about to be spent. Don't report after the money is gone; report in real time. And a cost anomaly is a behavior signal: a retry loop that quietly multiplies token spend shows zero errors and normal response times — it surfaces here first, not on the invoice.
The orchestration engine picks the model for each request on live evaluation of cost and latency.
Note: The cheapest model call is the one that never happens. GSX resolves what it can from canonical knowledge and deterministic logic, routes the rest on cost, latency and data sensitivity, and only reaches for a frontier model when the task genuinely needs one.
Runs, success rate, latency, tokens and cost; every agent reports the same scorecard, whether it lives on GSX or an external platform.
What teams do with cost data once every agent’s spend is metered, attributed and in one place. Invoices and chargebacks change human behavior; agents don’t read invoices. So GSX gives the humans chargeback reports, and gives the agents hard limits: budgets, thresholds and limits.
Isolate spend by department, region or use case from the tags applied at build time, and cross-charge it back to the teams that consumed it.
Errors, timeouts, fallbacks and abandoned paths are logged in the same stream as cost data, so spend that delivered nothing is visible too.
Thresholds notify budget owners as spend trends toward a limit, and high-value transactions gate for sign-off before they execute.
Ask in Slack or Teams which agents cost the most this week and what they delivered, or set a proactive alert in plain language.
Reveal all of the metering, attribution and cost metrics for your agent ecosystem.
Yes. Agents running on LangChain, CrewAI, or your own runtime are registered just like native ones and report the same scorecard: runs, success rate, latency, tokens, and cost. The platform an agent uses is simply an attribute in the registry, so your reporting continues across all platforms.
Tokens make up the biggest part of spend, but they’re not the only factor. GSX tracks model calls, tool and API use, and the reasoning steps between them as they happen. Since the runtime sends this data directly, you can see what a workflow costs in real time.
There are two main differences. First, attribution happens at build time, not later. Metadata tags are added to steps as you design the agent, so spend is linked to a team, region, or use case without extra data work. Second, cost is shown alongside the agent’s success criteria, so you can see what your spend achieved, not just what it was.
GSX can do both, depending on the situation. Before a call, it uses logic and known information to skip unnecessary model use, caches repeated actions, and sends each request to the cheapest suitable model. While running, you can set thresholds for any tracked metric, including token use, to alert budget owners as spend approaches a limit. High-value transactions may require approval before they are processed. Policy-based spending limits for each agent are coming soon.
No. Model tokens, compute, and storage are billed at the provider’s rate with no extra markup. There’s also no per-agent or per-seat fee, so adding agents doesn’t increase licensing costs. We want your cost per interaction to go down as you add more agents.
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