Probe0 vs Unify
Unify built its reputation on hosted routing: send a prompt to their cloud, and their scoring plus live benchmark data picks an endpoint for you. Probe0 is an LLM proxy that runs on your laptop, sees only your own coding agent traffic, and cuts cost with caching and local models before a request ever leaves the machine.
A hosted platform from unify.ai that launched as a cloud LLM router with live provider benchmarks, and whose site today markets AI agent teammates for business workflows.
| Feature | Probe0 | Unify | Edge |
|---|---|---|---|
| Cross-provider benchmark data | None. I only see your machine. | Ran continuously refreshed public provider benchmarks. | Competitor |
| Where it runs | Local process on your Mac. No Probe0 server. | Hosted cloud service. | Probe0 |
| Where prompts go | Routing decided on-device. Cache hits never touch the network. | Prompt goes to the platform to be routed. | Probe0 |
| Response caching | Exact cache on disk plus a local semantic index. | Not documented in any first-party source. | Probe0 |
| Cache safety during tool calls | Semantic cache refuses any match carrying tool calls. | No caching documented, so no policy either. | Probe0 |
| Local model routing | Routes to Ollama or LM Studio, retries weak answers on cloud. | No documented open-weight or local model support. | Probe0 |
| Spend control | Hard cap per run and per day. Warns, then pauses. | Prepaid credit pool that runs out. | Probe0 |
| Cost attribution per process | Local ledger: model, tokens, cost, latency, calling process. | Platform-side usage, not per-process on your machine. | Probe0 |
| Setup for one developer | One proxy plus a certificate. Claude Code, Codex and Cursor all covered. | API key and SDK, wired per application. | Probe0 |
Unify's live benchmarking of provider endpoints, refreshed on a short cycle and aware of region, is a fleet-scale data product that one laptop cannot reproduce. Past that, the case for Probe0 on a developer machine is straightforward. Probe0 runs as a local process with no server behind it, so routing decisions and cache lookups happen on-device and a repeat request costs nothing rather than costing less. One proxy and one certificate cover Claude Code, Codex and Cursor at once, with no per-tool wiring. The spend cap is a hard cap per run and per day that warns and then pauses, instead of a credit pool that drains while you are asleep. And the recording ledger attributes every call to the process that made it, which is the number you need when three agents are running and the bill triples.
One caveat worth stating plainly, since this page names a real company. The router and benchmark product described above matches Unify as it looked in 2024. Today unify.ai markets AI agent teammates that run business tasks across integrated apps, their docs no longer carry routing, benchmark or model-catalog sections, and their Python package now describes itself as an observability and eval client. If you are shopping for a hosted LLM router in 2026, check whether that surface is still operating before you build on it. I have written this comparison against the router category rather than asserting anything about what is live at unify.ai this week.
The axis I care about is different anyway. Probe0 assumes one developer, one machine, and traffic that comes from AI coding agent CLIs. That assumption buys things a hosted gateway cannot get. An exact cache on local disk answers a repeat request without a network call at all, so the marginal cost is zero rather than a lower per-token price. 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 mid-run corrupts the run and that is worse than paying. Request coalescing collapses simultaneous identical calls into one upstream call, which matters when three agent processes ask the same question at once. Local routing sends trivial work to a model already loaded in Ollama or LM Studio and retries weak answers on the cloud automatically. Each module is switchable on its own and reports what it saved, and the ledger shows model, tokens, real cost, latency and which process made the call, so you can see whether the plan tier you pay for matches what you actually use. There is no platform fee stacked on your token spend, because there is no platform.
The verdict
Pick Unify if cross-provider benchmark data and managed failover are the point, and you want an endpoint that answers identically from CI, servers and teammates' machines. For a single developer on a Mac running Claude Code, Codex or Cursor, Probe0 is the one I would install: local caching, local model routing and a cap that actually pauses move the bill more than a marginally cheaper endpoint does, prompts stay on the machine, and the ledger tells you which process spent what. Probe0 is macOS only and in private beta, so check that first, then run it.
Questions
- Is Probe0 a good Unify alternative?
- For a single developer running AI coding agents on a Mac, yes. Probe0 covers LLM cost control through caching, local LLM routing and hard spend caps, all on-device. It is not an alternative if you need a hosted endpoint your whole team and your CI can call.
- Does Unify still offer LLM routing?
- Their router and live benchmark platform is what Unify was known for around 2024. As of writing, unify.ai markets AI agent teammates instead, and the routing, benchmark and model-catalog sections are absent from their docs. Confirm the router is still operating before you depend on it.
- Does Probe0 send my prompts to a server?
- No. Probe0 runs entirely on your machine and there is no Probe0 server. Routing decisions happen on-device, the exact cache never touches the network, and requests only leave your Mac when they go to the model provider you already use.
- Can a local LLM proxy really cut cost more than a hosted router?
- It attacks a different lever. A router finds you a cheaper endpoint for a call you still make; a local proxy can avoid the call entirely through exact and semantic caching, coalescing duplicates, or answering from a model already running in Ollama. The recording ledger shows you exactly which module saved what.