Probe0 vs LocalAI
LocalAI runs the models. Probe0 sits in front of the coding agent CLIs already on your machine and decides where each call goes, then records what it cost. They solve adjacent problems, and a lot of people end up running both.
An MIT-licensed, self-hosted inference server with an OpenAI-compatible API that runs models locally across dozens of backends.
| Feature | Probe0 | LocalAI | Edge |
|---|---|---|---|
| What it actually is | Local proxy for coding agent traffic | Local inference server for models | Even |
| Runs models itself | No. Calls out to Ollama, LM Studio, or the cloud | Yes. 35+ backends, text through audio and image | Competitor |
| Real cost accounting | Per-call ledger: model, tokens, dollar cost, latency, calling process | Usage attribution per key and user, no currency figures | Probe0 |
| Which process spent the money | Every ledger row names the process that made the call | Attribution stops at the API key | Probe0 |
| Spend limits | Hard cap per run and per day. Warns, then pauses | Per-user quotas, no budget enforcement in dollars | Probe0 |
| Cloud fallback when local is weak | Weak local answers auto-retried against the cloud model | No hosted-provider fallback. Local inference only | Probe0 |
| Response caching | Exact cache on disk plus a semantic cache that skips tool-call traffic | Prompt and prefix caching inside the engine, no semantic cache | Probe0 |
| Setup for Claude Code, Codex, Cursor | One proxy and one certificate. No per-tool config | Point each tool at an OpenAI-compatible localhost endpoint | Probe0 |
| Proof each feature is earning its keep | Every module toggles alone and reports what it saved | Request history only, and the docs call it history | Probe0 |
LocalAI is the better inference server, and nothing here disputes that: dozens of pluggable backends covering llama.cpp, vLLM, MLX, whisper and diffusers, across CUDA, ROCm, oneAPI, Metal, Vulkan, Jetson, or plain CPU. What it has no reason to give you is the money. If you are one developer running coding agents on a Mac, the bill does not come from the model on your own disk, it comes from Claude Code and Codex reaching for Anthropic and OpenAI on the hard parts, which they keep doing because the frontier models are still better there. Probe0 is the layer that sees those calls. It writes a ledger row for every one with the model, the tokens, the real dollar cost, the latency, and the process that made the call, so a runaway agent has a name instead of a total.
Spend control is where the difference gets sharp. LocalAI has request history (bounded, persistent, clearable) and per-key usage attribution, but the docs are explicit that this is history rather than real-time metrics, and there is no Prometheus or OpenTelemetry export. There is no dollar figure anywhere, no cap you can set, and no price-aware routing between a local model and a hosted one. Probe0 has a hard cap per run and per day: it warns at a threshold, then pauses the run rather than emailing you about it afterwards. It also reads your actual usage and tells you when you are paying for a subscription tier above what you use, which is money LocalAI has no view into at all.
The install is the other reason I would start here. LocalAI wants you to decide which backend, which quantization, how much VRAM, and in production which database and message bus. Probe0 assumes those choices were made elsewhere. It routes to whatever model Ollama or LM Studio already has loaded, retries against the cloud when the local answer looks weak, tries a cheap model before an expensive one, collapses simultaneous identical calls into a single upstream request, and serves repeats from a local disk cache that never touches the network. The semantic cache holds a strict similarity floor and refuses to match anything carrying tool calls, because a plausible-but-wrong hit mid-run corrupts the run and I would rather lose the hit rate. Everything stays on the machine; there is no Probe0 server to send it to. Setup is one local proxy and one certificate installed once, after which Claude Code, Codex, and Cursor all route through it with no per-tool configuration, and every module toggles independently while reporting what it saved.
The verdict
If your problem is serving models yourself across multiple machines, modalities, or a team, run LocalAI. For a developer whose coding agents are running up a real bill on a Mac, Probe0 is the one to install: one certificate covers all three CLIs, the ledger tells you which process spent what, the cap actually pauses the run, and each module shows its own savings so you can prove it is working. Keep LocalAI underneath it if you like, and let Probe0 decide when the local model gets used and what the cloud calls cost.
Questions
- Can I use Probe0 and LocalAI together?
- Yes. Probe0 routes to a local OpenAI-compatible endpoint, so LocalAI can act as the inference backend the same way Ollama or LM Studio does. LocalAI answers what it can, and Probe0 handles the cloud fallback, the cost ledger, and the spend cap.
- Does LocalAI track how much my AI coding agent costs?
- Not in currency. LocalAI added per-user and per-API-key usage attribution and per-user quotas, but there is nothing spent to account for when inference runs on your own hardware, and there is no budget enforcement in dollars or fallback billing against hosted providers.
- Is Probe0 a real LocalAI alternative?
- Only if what you wanted from LocalAI was LLM cost control rather than an inference server. Probe0 does not run models, has no GPU scheduling, and is macOS-only in private beta. It is an LLM proxy for coding agent traffic, so it competes with LocalAI on routing and caching but not on serving.
- Do I need a GPU to use Probe0?
- No. Probe0 works fine with every call going to a hosted provider, in which case you get the caching, request coalescing, spend cap, and cost ledger. Local LLM routing to Ollama or LM Studio is one module among several and you can leave it off.