Probe0 vs Opik
Opik watches your LLM calls and scores them. Probe0 sits in the path of those calls on your own machine and changes what they cost before they leave it.
Comet's Apache-2.0 LLM observability, evaluation and prompt-optimization platform, available as hosted cloud or a self-hosted container stack.
| Feature | Probe0 | Opik | Edge |
|---|---|---|---|
| Position in the request path | In the path, local proxy on your Mac | Beside the path, SDK ingests traces | Probe0 |
| Response caching | Exact disk cache plus local semantic index | None, it cannot cache what it only observes | Probe0 |
| Routing and model tiering | Ollama or LM Studio first, cheap model first, cloud retry | No routing or fallback | Probe0 |
| Spend limits | Hard cap per run and per day, warns then pauses | Cost tracking only, nothing blocks a call | Probe0 |
| Setup for one developer's coding agent CLIs | One proxy plus one certificate, no per-tool config | Instrument each app with an SDK or integration | Probe0 |
| Where request data goes | Stays on the Mac, no Probe0 server exists | Comet cloud, or a Docker stack you run yourself | Probe0 |
| Attributing a call to what ran it | Ledger names the process behind every call | Spans grouped by project and trace | Probe0 |
| Cost reporting | Local ledger: model, tokens, real cost, latency | Per-span USD rollups, dashboards, custom price tables | Even |
| Evaluation and prompt work | None | Datasets, LLM-as-judge, pytest gates, prompt optimizer | Competitor |
Opik owns evaluation, and I am not going to pretend otherwise: datasets, versioned experiments, LLM-as-a-judge metrics for hallucination and RAG quality, pytest integration so a bad prompt change fails CI, a prompt registry with a playground, and an Agent Optimizer running roughly six algorithms to rewrite a prompt for you. That is a different job from the one I have. My problem is that Claude Code, Codex and Cursor are running on my laptop all day and the bill is the output. Probe0 is built for exactly that: one local proxy, one certificate installed once, and every coding agent CLI on the machine routes through it with no per-tool configuration and no account to provision. Nothing about it needs a container stack running before I can start work.
The structural gap is that Opik is a passive recorder. It is not in the request path by design, and Comet markets that as a virtue. The consequence follows directly: a repeated call cannot be served from cache, an expensive model cannot be downgraded, a run cannot fail over to a local model, and a budget cannot be enforced. Opik's cost features are estimates rolled up from token counts and a pricing table, shown to you after the money is gone, and its alerts fire on trace errors and feedback scores rather than a spend threshold. Self-hosting the local build means a Docker Compose stack where ClickHouse alone wants about 8 GB of RAM for development, and that OSS build ships without user management, which Comet's own docs state plainly.
Probe0 does the part that happens before the money leaves. Each module switches on and off by itself and reports what it saved: Local Routing sends work to a model already loaded in Ollama or LM Studio and retries weak answers on the cloud, Model Tiering tries the cheap model first, Exact Cache lives on disk so a hit never touches the network, Semantic Cache holds a strict similarity floor and refuses to match anything carrying tool calls because a wrong hit corrupts an agent run, Request Coalescing collapses simultaneous identical calls into one upstream request, and Spend Guard warns and then pauses at a hard cap per run or per day. Recording keeps the whole ledger locally, down to which process made each call, and that record is what lets it tell you when your plan tier sits above your real usage. There is no Probe0 server, so none of it leaves the machine. The honest limits: macOS only, private beta, sign-in through Google or GitHub on a young account system, no team deployment, and provider coverage means what coding agents actually call rather than a hundred-provider catalog.
The verdict
Pick Probe0 if you are one developer whose coding agents are the line item that keeps growing. It caches, routes to a local model, tiers down, and stops at a cap you set, all on your own Mac with nothing to operate and no data leaving it. The one case where Opik is the better buy is building an LLM product where the open question is quality: whether a prompt or model change helped, judged against datasets and gated in CI. That is a real job and Probe0 does not do it. If your problem is instead the bill your agents generate every day, Probe0 is the thing in the path that can act on it.
Questions
- Is Opik an LLM proxy or gateway?
- No. Opik is an observability and evaluation backend that ingests traces from your application through an SDK or OpenTelemetry. Your app still calls providers directly, so Opik cannot cache, route, fail over or block a request.
- Can Opik cap or limit my LLM spend?
- It can show spend, not stop it. Opik estimates per-span cost from token counts and a pricing table and rolls it up into dashboards, and its alerts trigger on errors and feedback scores rather than budget thresholds. For enforcement you need something in the request path, which is what Probe0's Spend Guard does with a hard cap per run and per day.
- What is a good Opik alternative for cutting AI coding agent costs?
- Opik is the wrong shape for that problem because it never touches the call. Probe0 is a local LLM proxy that every coding agent CLI on your Mac routes through, with caching, model tiering, local LLM routing to Ollama or LM Studio, and a hard spend cap.
- Does Opik work with local models like Ollama?
- Yes, for tracing. Opik documents an Ollama cookbook plus vLLM and other OpenAI-compatible endpoints, so calls to a local model get recorded like any other. It does not decide to send work to a local model, which is the difference from Probe0's Local Routing.