Probe0 vs vLLM
vLLM runs open-weight models fast on GPUs you own. Probe0 sits in front of the coding agents already installed on your Mac and controls what they spend. These live on different layers of the stack, so the useful question is which layer your actual problem is on.
An open-source, Apache-2.0 inference and serving engine that turns open model weights plus GPUs into a high-throughput, OpenAI-compatible HTTP endpoint.
| Feature | Probe0 | vLLM | Edge |
|---|---|---|---|
| What it is | Local proxy for coding agent CLIs | Inference and serving engine | Even |
| Raw serving throughput | Runs no models itself | PagedAttention plus continuous batching | Competitor |
| Hardware to run it | A Mac you already own | GPUs; roughly 24GB VRAM for a 7B model | Probe0 |
| Providers your coding agents actually call | Anthropic, OpenAI and Google traffic | Open weights you host, not commercial APIs | Probe0 |
| Setup for one developer | One proxy plus a certificate; every CLI routes through it | vllm serve locally; Kubernetes and Helm in production | Probe0 |
| Response-level caching | Exact and semantic cache skip the call outright | Prefix KV reuse cuts compute, still runs the model | Probe0 |
| Dollar spend cap | Hard cap per run and per day; warns, then pauses | None in the engine; add a gateway in front | Probe0 |
| Which process spent the money | Ledger names the process behind every call | Request metrics, no per-process cost | Probe0 |
| Tool-call safety in caching | Semantic cache refuses any request carrying tool calls | No response cache, so no such guard | Probe0 |
vLLM is excellent at the thing it exists for: PagedAttention keeps the KV cache in fixed-size blocks instead of one contiguous slab, which kills fragmentation and is why vLLM reports multiples of Hugging Face Transformers throughput on a single GPU, with the gap widening under concurrency. What that buys you is cheaper tokens on hardware you own, and none of it is reachable from a laptop. My coding agents call Anthropic, OpenAI and Google, and vLLM has nothing to say about that traffic, because it serves open weights you host and does not proxy commercial APIs. Probe0 handles that layer. Claude Code, Codex and Cursor all route through one local proxy after a single certificate install, with no per-tool configuration, and the ledger records model, tokens, real cost, latency and the process behind each call.
The parts that actually cut a bill are unglamorous. Spend Guard enforces a hard cap per run and per day, warning first and pausing the run when it hits, which is the difference between finding out about a runaway loop now and finding out on the invoice. Exact Cache lives on local disk and a hit never touches the network. 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 an agent run rather than merely wasting a request. Request Coalescing collapses simultaneous identical calls into one upstream. Model Tiering tries a cheap model first. Local Routing hands work to a model already loaded in Ollama or LM Studio and retries weak answers on the cloud. Every module toggles independently and reports what it saved, so a module that earns nothing is easy to turn off. Probe0 also reads your real usage and tells you when you are paying for a plan tier above what you use.
vLLM's own deployment guides put LiteLLM, Portkey or Envoy in front of it for budgets and quotas, because the engine takes a static API key and stops there. Probe0 is that layer, scoped to one developer on one machine, and it runs entirely on your Mac with no Probe0 server anywhere. Standing up vLLM properly means a Kubernetes cluster, a device plugin, persistent volumes for weights, Prometheus dashboards and someone who keeps it alive; that pays back for a team serving real inference load and never pays back for a solo dev whose bill comes from Claude Code and Cursor. The two also compose: if you already run vLLM, point Local Routing at your own models while Probe0 measures and caps whatever still leaks out to paid APIs. Probe0 is macOS only, in private beta, and sign-in is Google or GitHub.
The verdict
Pick vLLM if you own or rent GPUs and your problem is serving open weights at throughput; nothing here replaces PagedAttention. For everyone else — one developer on a Mac whose costs come from coding agents calling commercial APIs — Probe0 is the choice: a per-process ledger, caching that skips the call, and a cap that pauses instead of emailing you, all local, with no infrastructure to keep alive.
Questions
- Is Probe0 a vLLM alternative?
- Only if your goal was cost control rather than serving models. vLLM is an inference engine that runs open weights on GPUs; Probe0 is an LLM proxy that sits in front of AI coding agents and manages the calls they make to Anthropic, OpenAI and Google. If you need to host a 70B model, Probe0 cannot help.
- Can vLLM cap how much I spend on API calls?
- No. vLLM has no notion of dollars, budgets or virtual keys, and its documentation consistently pushes budget enforcement to a gateway layer in front of it. Probe0 enforces a hard cap per run and per day, warning first and pausing the run when the cap is reached.
- Can I use Probe0 with a vLLM endpoint?
- Probe0 routes to local models through Ollama and LM Studio today. It is built around what coding agent CLIs actually call, so treat direct vLLM endpoint support as something to ask about rather than something documented.
- Does vLLM prefix caching do the same thing as Probe0 caching?
- No. vLLM prefix caching reuses KV blocks so the model does less compute, but the model still runs. Probe0 exact and semantic caching returns a stored response and skips the call, which is what removes the cost on a paid API. The semantic cache refuses to match requests carrying tool calls, because a wrong hit there would corrupt an agent run.