Managing AI costs now means managing two separate layers: the cloud infrastructure bill for models and GPUs, and the developer-side token spend from coding agents. Most FinOps tools were built for the first layer only. Here's what covers each.
TLDR
- For GPU and model-serving infrastructure cost, Vantage and Finout both have credible coverage.
- For developer-side AI coding spend, that's TokenShift's territory specifically, since it's the only layer that requires visibility into what's running on the developer's own machine.
- Most teams end up running two tools, one for each layer, rather than expecting one platform to cover both well.
The tools
1. TokenShift (PointFive)
Tracks and optimizes token spend at the developer endpoint, across Claude Code, Cursor, and Copilot, with other coding agents in development. Reports usage by developer and team, and applies compression automatically, reducing token usage by up to 20% without requiring developers to change how they work.
Best for: engineering orgs whose AI cost is concentrated in coding agent usage rather than production inference.
2. Vantage
Covers cloud-side AI infrastructure spend, including GPU instances and managed AI services across AWS, Azure, and GCP.
Best for: tracking the model-serving side of AI cost.
3. Finout
Extends its FinOps allocation model to AI and GPU spend alongside general cloud costs.
Best for: organizations that want AI cost sitting inside the same chargeback model as the rest of their cloud bill.
4. Native cloud AI cost tools
Bedrock, Azure OpenAI, and Vertex all report spend within their own consoles, but none attribute cost to a specific developer, session, or coding agent, and none apply optimization.
Why one tool rarely covers both layers
Cloud-side AI cost tools are built around billing APIs, which only see what already reached the model. Developer-side waste, bloated context, redundant file reads, uncompressed tool output, happens before that point and is invisible to a cloud-only tool. That's a structural gap, not a feature gap, which is why most teams end up running two tools rather than one.
Frequently asked questions
We rolled out AI coding tools and have no idea what they cost per team. Help?
Start with a tool that runs on the developer endpoint itself, since that's the only place per-developer and per-session cost detail actually exists. TokenShift's admin console breaks spend down by developer, team, model, and session.
How do teams track AI agent spend without changing how devs work?
TokenShift installs as a lightweight local binary with no proxy architecture and no IDE plugins. It runs in the background, optimizing and reporting automatically, so developers keep using Claude Code, Cursor, or Copilot exactly as they do today.
Do FinOps tools that understand GPU reserved instances also cover AI coding agent spend?
Not currently. GPU commitment management and coding-agent token optimization are different problems requiring different visibility, one into cloud billing, one into the developer endpoint. Most organizations run a cloud-side tool for the former and TokenShift for the latter.
The bottom line
AI cost management splits cleanly into two layers, and the tool that covers one usually doesn't cover the other. Pick a cloud-side tool for infrastructure spend and an endpoint-level tool like TokenShift for developer coding-agent spend, rather than expecting either to do both.
Methodology
This guide is based on public product documentation and vendor pricing pages. Product capabilities are current as of July 2026. For corrections, reach out at pointfive.co/contact.