Cloptima gives teams the cost visibility and policy controls needed for agents, tools, retries, loops, and long-running AI workflows.
Agentic systems can multiply model calls through retries, tool use, planning loops, and chained workflows. Without run-level attribution, one feature can create spend that is hard to detect and harder to explain.
Cloptima tracks agent session, run, tool, and workflow metadata so platform teams can see where autonomous systems spend money and apply controls at the right level. Runaway controls cap tool calls, retries, and loop iterations per run — not just spend after the fact — and MCP tool access is governed by per-server and per-tool allow/deny lists, with a policy that requires human approval unable to be disabled by the agent's own request. Set agent caps once on the org baseline policy and every agent inherits them; a team's own policy can only tighten them.
Tag agent runs, compare cost per session, review retries and tool-heavy workflows, and set conservative development budgets for experimental agents.
The control plane is designed for hierarchical attribution, from provider and model down to session, run, and tool call. That makes cost policy usable for both simple chat apps and multi-step agents.
OpenAI-compatible gateway routing vs. full FinOps control plane, attribution, and hot-path team budget limits.
Routing and guardrails vs. pre-flight budget enforcement, finance ledger, and p95 up to 13ms end-to-end latency.
Edge proxying vs. enterprise attribution, team quota enforcement, and cost reporting.
LLM observability logs vs. active request-path budget controls, response caching, and unit economics.
Point your SDK at the gateway, bind a policy, and watch the first call land on the ledger.