Probe0 vs Ollama
Ollama runs models on your machine. Probe0 decides which model a request should go to and records what it cost. They sit at different layers, and the most common setup I see is both installed at once.
A local model runner that pulls and serves open-weight models on your own machine behind an OpenAI-compatible endpoint, with an optional hosted cloud tier for models too large to run locally.
| Feature | Probe0 | Ollama | Edge |
|---|---|---|---|
| Primary job | Local proxy in front of coding agents | Local runtime that serves models | Even |
| Runs and manages open-weight models | No, it calls whatever you already have running | Model library, one-command pulls, Modelfiles, quantization | Competitor |
| Routing across local and cloud | Local first, weak answers auto-retried on the cloud | Serves the model you name, no routing or fallback | Probe0 |
| Cache that survives across runs | Exact hits on local disk, never touch the network | In-flight KV prefix reuse, unloads after idle | Probe0 |
| Tool-call safety in cached answers | Semantic cache refuses to match requests with tool calls | No response cache to guard | Probe0 |
| Spend caps | Hard cap per run and per day, warns then pauses | No dollar cap, cloud tier uses session and weekly quotas | Probe0 |
| Per-process cost attribution | Ledger logs model, tokens, real cost, latency, calling process | None built in, Langfuse or OpenTelemetry bolted on | Probe0 |
| Setup for Claude Code, Codex, Cursor | One proxy and one certificate, no per-tool config | Point each tool at localhost:11434 individually | Probe0 |
| Tells you what each optimisation saved | Every module toggles alone and reports its own savings | No cost model, nothing to report | Probe0 |
Ollama owns model lifecycle on a laptop, and nothing here disputes that: the library, one-command pulls, Modelfiles for context length and system prompt, quantization choices, macOS plus Windows plus Linux, and a hosted Cloud tier for 120B-class models a laptop cannot fit. What it will not do is decide. It serves the model named in the request, so when your coding agent asks Anthropic for a frontier model on a task the 30B model idling on your own GPU would have handled, Ollama never sees the request and your card gets charged. Probe0 sits where that decision happens. Local Routing sends work to a model already loaded in Ollama or LM Studio and retries on the cloud when the answer scores badly. Model Tiering tries the cheap model first. Exact Cache is a disk hit that never opens a socket. Semantic Cache uses a local vector index behind a strict similarity floor and refuses to match anything carrying tool calls, because a near-miss on a tool call corrupts an agent run in a way that is expensive to notice hours later. Request Coalescing collapses simultaneous identical calls into one upstream.
The ecosystem answer to all of that is a proxy, which is why LiteLLM, OpenRouter and Portkey exist. Probe0 is built narrowly for one traffic shape: a coding agent hammering a provider from your terminal. Install one local proxy and one certificate, and Claude Code, Codex and Cursor all route through it with no per-tool config, instead of pointing each one at localhost:11434 by hand and hoping you remember which ones you did. Spend Guard caps a run and a day, warns, then actually pauses rather than emailing you after the fact. Every module switches on and off on its own and reports what it saved, so a claim about local routing is a number you can turn off and watch disappear.
Local routing only saves money if something measures both sides, and Ollama has no visibility into the cloud spend it could be displacing. Probe0's Recording ledger logs model, token counts, real cost, latency and which process made each call, which is how you find out that Cursor's autocomplete traffic was a third of the bill, or that you are on a plan tier above what your usage justifies. All of it stays on the machine. There is no Probe0 server to send traffic to, cache hits never leave the disk, and sign-in is Google or GitHub with no password. Probe0 is macOS only and in private beta, it is not a team gateway, and there is no RBAC or shared policy. Provider coverage is what coding agents actually call rather than a catalog of a hundred names.
The verdict
Probe0 is the one I would install if you are paying for cloud tokens on a Mac: it routes toward the local model, caches repeat work without breaking tool calls, caps the spend before it runs away, and tells you afterward which process spent it. Ollama is the better pick when your problem is getting open-weight models onto the machine in the first place, and it is your only option of the two on Windows or Linux. Keep Ollama running the models. Put Probe0 in front of it to decide what actually costs you money.
Questions
- Can I use Probe0 and Ollama together?
- That is the intended setup. Probe0 detects models already running in Ollama or LM Studio and routes eligible coding-agent requests to them, then retries on a cloud model when the local answer scores below the quality floor.
- Is Probe0 an Ollama alternative?
- Not really, they solve different problems. Ollama runs models; Probe0 is an LLM proxy that decides which model a request goes to and enforces LLM cost control. Probe0 has no model runtime of its own.
- Does Ollama have spend limits or cost tracking?
- Local Ollama inference is free and unmetered, so there is nothing to cap. The hosted Cloud tier uses session and weekly usage windows rather than a dollar budget you set, and there are no built-in cost dashboards. Observability comes from third-party tools like Langfuse or SigNoz.
- Do I have to configure Claude Code, Codex and Cursor separately?
- With Ollama you point each tool at its endpoint yourself. With Probe0 you install one local proxy and one certificate, and every coding agent CLI on the machine routes through it without per-tool config. macOS only, private beta.