Probe0 vs Datadog LLM Observability
Datadog watches LLM traffic and tells you what it cost. Probe0 sits in the request path on your own machine and changes what the traffic costs before it leaves. One is a telemetry backend for a production service; the other is an LLM proxy for the coding agents running on your laptop.
A SaaS observability module inside Datadog that instruments your application with an SDK and turns every LLM call into a trace with spans, tokens, latency and estimated cost.
| Feature | Probe0 | Datadog LLM Observability | Edge |
|---|---|---|---|
| Where your prompts go | Nowhere. No Probe0 server exists; the ledger stays on your disk. | Prompt and response payloads land in Datadog's SaaS backend. | Probe0 |
| Sits in the request path | Yes. Local proxy, so it can answer, reroute or stop a call. | No. SDK instrumentation observes; it cannot serve a response. | Probe0 |
| Caching | Exact cache on local disk plus a semantic cache with a strict similarity floor. Hits never touch the network. | None. Records provider cache-token breakdowns as span metadata. | Probe0 |
| Cache safety around tool calls | Semantic cache refuses to match anything carrying tool calls. | No cache, so nothing to guard. | Probe0 |
| Routing | Local Routing to Ollama or LM Studio, Model Tiering, weak answers retried upstream. | No routing or failover. It observes gateways like LiteLLM rather than being one. | Probe0 |
| What a budget limit does | Hard cap per run and per day. Warns, then pauses the run. | Monitors, forecasts and anomaly alerts. Pages a human; spend continues. | Probe0 |
| Covering the coding agent CLIs | One proxy and one certificate, once. Claude Code, Codex and Cursor route through it with no per-tool config. | SDK in Python, Node or Java inside code you control. Closed-source CLIs cannot be instrumented. | Probe0 |
| Attributing cost to a process | Every call records the process that made it, plus per-module savings and plan-tier advice. | Attribution by service, environment and trace, from spans your code emits. | Probe0 |
| Depth of tracing | HTTP boundary only: model, tokens, real cost, latency, calling process. | In-process span waterfalls across tool, retrieval and agent steps, correlated with APM and logs. | Competitor |
Datadog's span waterfall genuinely sees inside a running application, correlating a cost spike with the container, the host, the GPU and the database query on the same request, and nothing I built substitutes for that on a production service. But the machine I care about is a laptop running Claude Code in one terminal, Codex in another and Cursor in the background, all burning tokens on the same repository. Datadog would have told me the number after the fact. Probe0 changes the number while the request is still on the machine: Exact Cache and Semantic Cache answer from local disk, Local Routing sends straightforward edits to a model already loaded in Ollama or LM Studio and retries on the cloud when the answer comes back weak, Model Tiering starts cheap and escalates only when needed, Request Coalescing collapses simultaneous identical calls into one upstream, and Spend Guard warns and then pauses at a hard cap.
The instrumentation model is the practical wall. LLM Observability works by putting an SDK inside code you control, in Python, Node or Java, with auto-instrumentation across many frameworks. Claude Code, Codex and Cursor are not code you control, which is why a proxy is the only place to stand for this workload. Probe0 asks for one local proxy and one certificate, once, and after that every agent CLI on the machine is covered with no per-tool setup. Each module switches on and off independently and reports what it saved on its own, so a claim about Semantic Cache can be judged separately from a claim about Local Routing. The ledger records model, tokens, real cost, latency and which process made each call, which is how I found out that one background agent accounted for most of a week, and it knows enough about actual usage to say when you are paying for a plan tier above what you use.
There is also the matter of where the prompts go. Datadog's backend is SaaS-only, so prompts and responses, which for a coding agent means large chunks of your source tree, leave the machine. Sensitive Data Scanner and client-side span processors exist to redact, and for a company with a compliance function that is the right trade. For one developer it is a lot of exposure to buy telemetry. Probe0 has no server component at all, cache hits never reach the network, and sign-in is Google or GitHub with no password. The honest limits: macOS only, private beta, no team deployment, and provider coverage means what coding agents actually call rather than a hundred-provider catalogue. The tracing gap is real too. I see the request boundary and infer the rest; Datadog sees the agent's internal structure because it is instrumented from inside.
The verdict
If your spend comes from AI coding agents on your own machine, pick Probe0: it is the only one of the two that can cut the bill rather than chart it, and it does that without your source tree leaving the laptop. Datadog LLM Observability is the better pick in one situation, and it is a real one: an LLM feature in production across many services, where you need in-process span waterfalls correlated with your infrastructure and access controls a procurement team will accept. Running both is coherent, Datadog on the service you ship and Probe0 on the agents that write it, but for the laptop the choice is Probe0.
Questions
- Can Datadog LLM Observability cache or block LLM requests?
- No. It is SDK instrumentation, not a gateway, so nothing sits in the request path. It can record cache status and token counts reported by the provider or by an upstream gateway, and it can alert you when cost crosses a threshold, but it cannot serve a cached response or stop a request on budget.
- Is Probe0 a Datadog LLM Observability alternative?
- Only for the single-developer, coding-agent case. Probe0 records the same class of data locally and adds caching, local LLM routing and hard spend caps, but it has no fleet aggregation, no in-process span waterfalls, no RBAC and no evaluation tooling. For production services across many machines, Datadog is the right tool.
- Do my prompts leave my machine with either tool?
- With Datadog, yes: the backend is SaaS-only and prompt and response payloads land in Datadog's cloud, with Sensitive Data Scanner and SDK-level processors available for redaction. With Probe0 there is no server, so the request ledger and both caches stay on local disk.
- Does either one support local models like Ollama?
- Probe0 routes work to a model already running in Ollama or LM Studio and retries on the cloud when the local answer looks weak. Datadog has first-class vLLM instrumentation plus GPU and KV-cache infrastructure monitoring, but no native Ollama integration, and its cost estimation only covers hosted text models.