Best AI usage trackers for local LLM users (2026)
A locally loaded model does not consume a Claude or Codex subscription allowance. Its useful signals are context, memory, activity and generation speed. That changes which kind of tracker is worth using.
Disclosure: We make AI Usage Tracker. We recommend competitors when their documented features better fit the job.
| App or tool | Best for | Price | What it measures |
|---|---|---|---|
| AI Usage TrackerOur app | Ollama and LM Studio beside cloud-agent readings on Mac | $9.99 once | Provider limits, resets and agent activity; local runtime metrics |
| Ollama’s own tools | Checking and managing the Ollama runtime directly | Use the tools in your installed runtime | Loaded local models and runtime inspection |
| LM Studio’s own tools | Managing LM Studio models and server behavior | Use the tools in your installed runtime | Model management and server/runtime inspection |
| ccusage | Reviewing token metadata recorded by a supported client | Free, open source | Local token counts and estimated model costs |
On this page
Measure the runtime that is actually running
A client, a model server and a quota API see different parts of the work. The local runtime knows what is loaded and generating. A coding client may record tokens for its own sessions. A cloud provider knows the account allowance it enforces.
Keep those observations separate. Do not compare a cloud allowance ring with a local context ring as if both mean how much AI you have left.
AI Usage TrackerOur app
- Best for
- Ollama and LM Studio beside cloud-agent readings on Mac
- Price
- $9.99 once
- Runs on
- macOS 15+, Apple silicon and Intel
Our app detects supported loaded models. Ollama details can include memory, context and unload time. Optional speed and thinking measurement uses a local relay; clients must send the relevant traffic through it.
LM Studio activity comes from its SDK socket, with speed and daily counts derived from server logs. OpenAI-compatible responses without their own timing are measured approximately and marked accordingly. You do not have to route LM Studio traffic through our relay.
Trade-off: It is macOS 15+ only as a sold installer. Monitoring is not a universal benchmark, and it cannot observe data the runtime or chosen client does not expose.
Ollama’s own tools
- Best for
- Checking and managing the Ollama runtime directly
- Runs on
- Your supported Ollama installation
The Ollama CLI reference documents its model and runtime commands. Use the runtime’s own tools when the task is loading, unloading or inspecting a model.
A separate visual monitor is optional. Direct runtime inspection can be enough for someone who runs a model occasionally and already works in the terminal.
Trade-off: This is a runtime workflow rather than one shared usage view for your cloud subscriptions.
LM Studio’s own tools
- Best for
- Managing LM Studio models and server behavior
- Runs on
- Your supported LM Studio installation
The LM Studio developer documentation describes its APIs and server capabilities. Start in LM Studio when you need to change what is loaded or how the server is configured.
The native tools are the better place for model management. A notch is useful for awareness while you are working somewhere else.
Trade-off: Do not assume every client response carries timing or context data sufficient for identical speed measurements.
ccusage
- Best for
- Reviewing token metadata recorded by a supported client
- Price
- Free, open source
- Runs on
- Command line; requires a supported JavaScript runtime
The source list tells you which coding clients have usable local data. Check it before assuming a model server’s requests will appear in the report.
If the client stores suitable token metadata, a report can explain activity over time without becoming the tool that manages the model.
Trade-off: A local model’s API-price equivalent is not a cloud charge and does not measure electricity, hardware cost or runtime performance.
Our recommendation
For a persistent Mac glance across supported runtimes, our app is a fit. For loading and configuring models, use the runtime’s own tools. For benchmarking, use a controlled workload and consistent timing rather than treating a last-response speed reading as a comparative benchmark.
A practical way to choose
- Identify the model server and the client sending requests.
- Check which source supplies context, tokens and timing.
- Use the native runtime controls for management and a monitor for awareness.
Questions
Does Ollama speed appear with no client configuration?
Loaded-model detection can work directly. Optional speed and thinking capture requires the documented local relay path.
Can a context ring tell me how much free cloud usage remains?
No. Local context is a runtime measurement; cloud account allowance is separate.
Sources and scope
Prices and documented features checked on . We reviewed official product pages, documentation, store listings and our own app’s implementation. Competing apps were not installed or benchmarked. Recommendations are our assessment of those documented capabilities.
Provider data, paid plans and platform features can change. Missing documentation is not proof that a feature is absent. See our selection method.