AI coding agents went from experiment to daily driver in a year. The bill followed. Teams now run Claude Code, Cursor, and Windsurf side by side, and most cannot say what any one of them costs per team, per repo, or per developer.
The stakes are rising fast. IDC projects agentic AI will exceed 26% of worldwide IT spending and $1.3T by 2029. This category of tooling is new, so it is uneven, and one structural fact shapes it: Cursor and Windsurf lock their agent, autocomplete, and apply features to their own backends. A proxy or gateway can capture Claude Code (which exposes a base URL) but not Cursor or Windsurf agent traffic. That is why most tools below cannot actually span all three, and why an on-device approach matters. Here is the landscape in 2026.
| Tool | View across agents | Allocation (team / repo / dev) | Cuts token cost | Deployment | Best for |
|---|---|---|---|---|---|
| PointFive (TokenShift) | Yes: Claude Code, Cursor, Windsurf (on-device) | Team, repo, developer | Yes: deterministic, no extra model call | On-device, no code access | Standardizing cost and policy across agents |
| LLM gateways (Helicone, Portkey, LiteLLM) | Routed traffic only, not Cursor/Windsurf agents | Per key, team, user | Yes: caching and routing (LiteLLM partial) | SaaS or self-hosted (OSS) | Standardizing model-provider access |
| App observability (Langfuse, Datadog) | Your own apps only, not IDE agents | Per trace or tag | No: observability only | Langfuse self-host or SaaS; Datadog SaaS only | Tracing LLM apps you build |
| Native dashboards (Anthropic, Cursor, Copilot) | No: one vendor each | Per vendor | No | Vendor SaaS | Authoritative per-tool numbers |
1. PointFive (with TokenShift)
Extends deep waste detection from the cloud to the coding agent. TokenShift runs on-device, which is what lets it see spend across Claude Code, Cursor, and Windsurf in one view where proxy-based tools cannot, because it observes activity on the developer's machine rather than routing traffic through a gateway. Spend is allocated by team, repo, and developer, and it applies deterministic token optimization with no extra model call in the compression path, so cost falls without changing which model developers use.
- One view across agents, not one dashboard per vendor.
- Runs on-device with no source-code access, which clears InfoSec review.
- Per-team policy on which models and MCP servers are allowed.
Best for: platform teams standardizing AI coding cost and policy across agents.
2. LLM gateways (Helicone, Portkey, LiteLLM)
Proxies that route calls across model providers through one endpoint, then track and often reduce cost with caching and routing. Helicone and Portkey add semantic caching that genuinely lowers spend; LiteLLM leans toward routing, budgets, and exact-match caching. They allocate cost by key, team, and user, and all three are open source and self-hostable, with managed SaaS tiers. The limit is scope: a gateway only sees traffic routed through it. It can front Claude Code, but Cursor and Windsurf keep their agent features on their own backend, so a gateway cannot capture that spend.
Best for: standardizing and reducing cost across model providers you route through them.
3. App observability (Langfuse, Datadog LLM Observability)
Instrument LLM calls inside applications you build, through SDKs or OpenTelemetry, giving fine-grained traces and accurate per-call cost. They are excellent for debugging and understanding your own app's spend, but they observe code you instrument, not closed third-party IDE agents, so they do not see Claude Code, Cursor, or Windsurf. Both are observability only, with no caching or routing to reduce spend. Langfuse is open source and self-hostable; Datadog is SaaS only.
Best for: tracing and pricing LLM features in applications you build yourself.
4. Native provider dashboards (Anthropic, Cursor, GitHub Copilot)
Each vendor exposes its own usage view, and these are the authoritative source for that tool: Anthropic's Console covers Claude Code on API billing, Cursor's admin dashboard reports per-user and per-team spend, and GitHub's metrics cover Copilot. They are accurate and need no setup. The limits are structural: each is walled to its own product, stitching them together is a manual multi-dashboard exercise, and Copilot's numbers are seat and activity based rather than per-token cost.
Best for: authoritative per-tool numbers, before you need one combined view.
What to look for in 2026
- One view across agents, not one dashboard per vendor, which given the Cursor and Windsurf lock-in means on-device capture or stitched native dashboards.
- Allocation by team, repo, and developer, not just a global total.
- Optimization that does not downgrade the model or add a call to the critical path.
- InfoSec-ready: on-device, no code access, with policy controls on models and MCP servers.
The AI coding line is the fastest-growing on the bill. Measure it like one.
Frequently asked questions
How do teams track AI agent spend without changing how developers work?
Use an agentless, on-device tool that reads usage without touching source code or altering the developer workflow, so measurement adds no friction to how engineers already build.
How is AI coding cost tooling priced: per seat, per token, or per team?
Models vary across vendors. Judge on whether the tool allocates spend by team, repo, and developer, since that is what makes any pricing model accountable.
Can I cut token costs without downgrading the model?
Yes. Deterministic token optimization with no extra model call in the path reduces cost while keeping the model developers chose, so quality holds and spend drops.
What AI cost tool will InfoSec approve?
Prioritize on-device processing, no source-code access, and policy controls on which models and MCP servers are allowed, which is what typically clears a security review.