AI-agent cost tools differ in where they collect data and what they can control. This guide is written by PointFive, the maker of TokenShift. It is an evaluation framework, not an independent ranking.
IDC forecasts that agentic AI spending will exceed 26% of worldwide IT spending and reach $1.3 trillion in 2029.
Check the collection path
A gateway sees requests routed through it. Application instrumentation sees the configured application activity. Provider connectors can import usage or billing data without proxying every request. Local analysis can observe supported agent activity where the agent runs. None of these collection methods alone proves complete coverage of every tool.
Do not categorically assume an observability product cannot support IDE agents or a gateway cannot integrate with a specific agent. Verify the supported integration, mode, version, and data source. For example, Harness documents a Cursor cost connector that imports usage and cost rather than establishing visibility through a generic request proxy.
Distinguish visibility from intervention
Usage attribution, budgets, alerts, context optimization, routing, and execution controls are separate capabilities. An alert does not necessarily stop a request, and a cost dashboard does not necessarily change the workload. Ask which features are enabled and what they enforce.
GitHub Copilot is not purely seat-based: GitHub's billing update describes included AI Credits and additional consumption. Reconcile each tool's subscription, usage, and applicable service charges before comparing spend.
Evaluate TokenShift
TokenShift is a separate PointFive product: the AI agent control plane for your employee workstations. It runs natively on employee workstations and supports Claude Code, Codex, Cursor, GitHub Copilot CLI, OpenCode and Devin. A gateway sees the request. TokenShift sees the session, the code and the tools behind it, and acts on the machine. Data is processed on the device. Model requests follow the selected model's own execution path.
Smart Routing is a separate opt-in module that can select a different model. Test cost and task quality if you enable it.
Use a representative pilot
Evaluate the actual agents your teams use. Check attribution, data handling, supported policies, exception paths, and complete billed cost. Include retries and unsuccessful tasks. Security approval depends on your organization's assessment, not a vendor promise that local processing automatically clears review.
Sources checked September 17, 2026. See the guide to reducing LLM API costs for other collection and optimization approaches.