Probe0 vs Finout
Finout reads the invoice. Probe0 sits in the request path on your laptop and decides whether the expensive call happens at all. One is org-wide accounting after the spend, the other is LLM cost control at the moment of the call.
A hosted enterprise FinOps platform that merges cloud, Kubernetes, SaaS and AI provider bills into one normalized cost dataset for allocation, budgeting and chargeback.
| Feature | Probe0 | Finout | Edge |
|---|---|---|---|
| Where it runs | Local proxy on your Mac, no Probe0 server | Hosted SaaS, agentless, read-only cloud role | Even |
| One normalized bill across cloud, Kubernetes and SaaS | Out of scope | AWS, GCP, Azure, Kubernetes, Snowflake, Datadog in one view | Competitor |
| Can it stop a call? | Spend Guard warns, then pauses the run at the cap | Budgets and alerts, no request denial | Probe0 |
| Caching | Exact cache on local disk, plus a tool-call-safe semantic cache | None, it never sees requests | Probe0 |
| Model routing and fallback | Local Routing and Model Tiering, cheap model first | No gateway or interception layer | Probe0 |
| Local models (Ollama, LM Studio) | Routes to a model already loaded locally | No invoice exists, so nothing to ingest | Probe0 |
| Attribution for one developer's agents | Per request: model, tokens, real cost, latency, calling process | Aggregated by workspace, key and model, refreshed daily | Probe0 |
| Setup for one developer | One proxy, one certificate, then every agent CLI is covered | Admin API keys, cloud role, hosted onboarding | Probe0 |
| Where your prompts go | Ledger and caches stay on disk, cache hits skip the network | Usage and billing data leaves for the vendor's cloud | Probe0 |
Finout is genuinely the better product for one job: it pulls OpenAI Usage and Costs through a read-only Admin key and Anthropic through Admin or Analytics keys, then lands that authoritative invoice in the same normalized bill as AWS, Kubernetes, Snowflake and Datadog, with Virtual Tags, chargeback and SOC 2 Type II behind it. That job is finance allocating a multi-cloud bill, and it is not the job you have when a coding agent is chewing through tokens on your laptop right now. For that, I would run Probe0: one local proxy and one certificate, after which Claude Code, Codex and Cursor all route through it with no per-tool config, and the ledger of what each process actually cost lives on your own disk.
What Finout does not do is intervene. It sits after the fact on invoices and usage APIs, never in the request path, and the docs say so plainly: read-only, no actions that create or modify resources. So a runaway agent loop shows up as an anomaly alert, at workspace or key or model granularity, minutes to a day after the money is gone. There is no cache, because it never sees a request to cache. There is no fallback model, because there is no gateway. A per-model budget threshold sends a notification; it does not refuse the next call. And the granularity that matters when a coding agent is misbehaving, which prompt, which process, how many tokens, how long it took, is not there because billing data does not carry it.
Probe0 is built for the other end of that, and every module is switchable on its own and reports what it saved. Local Routing sends work to a model already running in Ollama or LM Studio and retries weak answers on the cloud. Model Tiering tries the cheap model first. The exact cache lives on local disk and never touches the network; the semantic cache uses a local vector index with a strict similarity floor and refuses to match anything carrying tool calls, because a wrong hit there corrupts a run. Request Coalescing collapses simultaneous identical calls into one upstream call. Spend Guard enforces a hard cap per run and per day. Recording keeps real cost, latency and the process behind each call, which is also what lets it tell you when you are paying for a plan tier above what you use. The honest limits: macOS only, private beta, sign-in via Google or GitHub, not a team gateway, and provider coverage is what coding agents call rather than a hundred-provider catalog.
The verdict
For a developer running coding agents on a Mac, Probe0 is the one to install: it caches on disk, routes to a local model, tiers down to the cheap one, and pauses the run at a hard cap instead of emailing you about it afterwards. Finout is the better buy in exactly one situation, and it is a real one: a finance or platform team that needs org-wide ground truth, retroactive allocation and chargeback across AWS, Kubernetes and AI providers. If that is not your week, the money you want back is the money Probe0 stops you from spending.
Questions
- Is Probe0 a Finout alternative?
- Only on the control axis. Finout is retrospective billing analytics for a whole organization, and Probe0 is a local LLM proxy for one developer's machine that caches, reroutes and caps calls before they are made. If you need chargeback and multi-cloud allocation, Probe0 does not replace Finout.
- Can Finout stop an AI coding agent from overspending?
- No. Finout offers budgets, per-model thresholds and ML-baselined anomaly alerts, but it is read-only and out of band, so it can tell you a loop burned tokens and cannot block the next call. Probe0 Spend Guard sets a hard cap per run and per day, warning first and then pausing.
- Does either tool track spend on local models?
- Finout ingests bills, and Ollama or LM Studio produce no bill, so local inference is invisible to it beyond cloud GPU infrastructure cost. Probe0 does local LLM routing directly, sending work to a model already loaded locally and retrying weak answers on the cloud.
- How accurate is Probe0 cost data compared to a billing integration?
- Probe0 records real cost per request from the tokens it observed, with model, latency and the calling process attached, which is far more granular than Finout's daily aggregated view. It only sees traffic that passed through the proxy on that machine, so for a full organizational total the provider billing API remains the source of truth.