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: some tools observe, and a few actually optimize. Here is the landscape in 2026.
| Tool | Cross-agent view | Allocation (team / repo / dev) | Cuts token cost | InfoSec posture | Best for |
|---|---|---|---|---|---|
| PointFive (TokenShift) | Yes: Claude Code, Cursor, Windsurf | Team, repo, developer | Yes: deterministic, no extra model call | On-device, no code access | Standardizing cost and policy across agents |
| Helicone / Langfuse / Portkey | Per app | Per request or app | Via routing and caching | Varies (often SaaS) | App-level LLM tracing |
| LiteLLM | Per gateway | You build it | Routing only | Self-hosted | Self-hosted routing and metering |
| Datadog LLM Observability | Within Datadog | Via tags | No | Enterprise SaaS | Datadog-standardized teams |
| Native dashboards (Anthropic, Cursor, GitHub) | No (per tool) | Per tool | No | Vendor-controlled | Per-tool starting point |
1. PointFive (with TokenShift)
Extends deep waste detection from the cloud to the coding agent, giving one cross-agent view of token spend across Claude Code, Cursor, and Windsurf, then cutting it. Spend is allocated by team, repo, and developer, and TokenShift applies deterministic token optimization with no extra model call in the compression path, so cost falls without changing which model developers use.
- One cross-agent view, not one dashboard per vendor.
- Agentless and on-device: 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 observability platforms (Helicone, Langfuse, Portkey)
Log, trace, and price individual LLM calls, with request-level visibility that is valuable for debugging prompts and understanding per-call cost. They are built for application teams instrumenting their own LLM features and give fine-grained traces of each request. They are less oriented toward cross-agent developer spend across third-party coding tools, which sits outside the application they instrument.
Best for: application-level LLM tracing and per-request cost.
3. LiteLLM
An open-source proxy that routes calls across model providers and meters usage through a single gateway. Platform teams use it to standardize access, enforce keys, and capture usage data across models on their own infrastructure. It provides the routing and metering layer, but the allocation and optimization logic on top is yours to build.
Best for: teams wanting a self-hosted routing and metering gateway.
4. Datadog LLM Observability
Brings LLM call monitoring, latency, and cost signals into an existing Datadog footprint, alongside the rest of your telemetry. It is a natural fit for teams already standardized on Datadog that want LLM usage in the same pane as their infrastructure metrics. Value depends on already running Datadog, and its lens is observability rather than active cost reduction.
Best for: teams already standardized on Datadog.
5. Native provider dashboards (Anthropic, Cursor, GitHub)
Each vendor exposes its own usage and billing view, which is the quickest way to see spend for a single tool. They are accurate for that tool and require no setup. The limitation is structural: no single vendor dashboard shows spend across all the agents your developers use, so the cross-agent picture stays fragmented.
Best for: a per-tool starting point before you need one combined view.
What to look for in 2026
- One view across agents, not one dashboard per vendor.
- 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.