Graphsignal Profiler

We've seen plenty of profilers that stop at dashboards. Graphsignal actually closes the loop: it profiles, suggests flag changes, and even redeploys via autodebug. That's rare and genuinely useful for teams running serious LLM inference in production. Pick this when you're spending real money on GPU-hours and want to squeeze out latency and cost. The auto-flags feature alone can find configurations you'd never tune by hand. If you're running vLLM or SGLang with NVIDIA or AMD GPUs, you're in prime territory. Pass if you're doing training instead of inference — this is not a training profiler. Also, if you want out-of-the-box APM for a web app, look elsewhere. Graphsignal assumes you know your inference stack. There's a CLI and Python API, and you'll need to get comfortable with them. The closest alternative is dev-time CUDA profilers like NVIDIA Nsight. But Nsight is built for development, not production — it's heavy and not designed for continuous monitoring. Graphsignal is lightweight enough to run alongside live workloads, which is its whole point. One caveat: the free tier caps at 100 GPU-hours per month. That's fine for small experiments but will run out fast on busy production clusters. Pricing is usage-based at $0.08 per GPU-hour, which can add up, but the savings from optimization usually outweigh it. In practice, teams report finding kernels they didn't know were slow and flag settings that cut latency by double digits. If that's your goal, Graphsignal is worth a serious look. If you want something that runs itself without oversight, autodebug is close, but you'll still need to interpret the results and decide when to trust it. Overall, we'd reach for Graphsignal when we care about every millisecond and every dollar spent on inference. It's a specialized