Graphsignal Profiler
GPU profiler that serves inference measurements as local JSON so AI agents can autonomously optimize LLM serving performance.
Graphsignal Profiler is built around an agent loop rather than a GUI: the data is JSON on localhost, so an agent can launch, measure, edit a flag or a line of code, and re-measure without a human in the middle. The free tier's 100 profiled GPU-hours per month is real, and Pro's $0.08 per profiled GPU-hour scales with what you actually run. Buy it if you're tuning vLLM, SGLang or TensorRT-LLM and want production-viable overhead alongside telemetry and engine metrics in one document, where torch.profiler and nsys give you per-kernel time but not the rest. Skip it if you need web-app APM or training-workload profiling.
Verified 5d ago · liveness 60/100 · cite: rightaichoice.com/tools/graphsignal-profiler
- AI inference engineers optimizing production latency and throughput on vLLM, SGLang or TensorRT-LLM
- ML platform teams running continuous GPU profiling across models, engines and accelerators
- Developers debugging LLM prefill/decode performance with fine-grained kernel tracing
- Teams using AI coding agents to tune inference engines from live telemetry
- Teams needing traditional application performance monitoring for web or mobile apps
- Users focused on training profiling rather than inference
- Beginners without CLI, Python and inference-engine familiarity
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Skip Graphsignal Profiler if you need web-app APM or training-workload profiling, or if you want a point-and-click onboarding rather than wrapping a launch command with graphsignal-run.
Profiling beyond the free tier's 100 GPU-hours per month is billed at $0.08 per profiled GPU-hour, so continuous always-on profiling on Pro turns into a recurring usage charge.
Free ($0/mo, 100 profiled GPU-hours per month, 1 user) is enough for a single engineer testing an engine locally. Pro at $0.08 per profiled GPU-hour with unlimited users and 30-day retention fits a platform team profiling continuously; the bill scales with how much GPU time you profile, so a team profiling several GPUs around the clock should price it against Enterprise volume discounts. Competing per-kernel tools like nsys cost nothing directly but deliver no telemetry or agent-readable
In short
Graphsignal Profiler — GPU profiler that serves inference measurements as local JSON so AI agents can autonomously optimize LLM serving performance. Best for AI inference engineers optimizing production latency and throughput on vLLM, SGLang or TensorRT-LLM, ML platform teams running continuous GPU profiling across models, engines and accelerators, Developers debugging LLM prefill/decode performance with fine-grained kernel tracing. Free to start; paid plans from $0.08.
What's new in Graphsignal Profiler
Checked 5 days agoAcross the latest 3 updates: 1 feature update and 2 news mentions.
graphsignal-top: a live view of your inference GPUs
graphsignal-top renders the profiler's JSON output as a live terminal screen showing GPU telemetry, engine throughput and latency, and kernels ranked by time, locally or over the network.
Graphsignal vs. torch.profiler and nsys
Explains what Graphsignal adds over per-kernel profilers: comparable JSON snapshots, telemetry in the same document, GPU probes, production-viable overhead, and a runner that wraps any engine.
Understanding Inference for Systems Engineers
A stage-by-stage walkthrough of LLM text generation on one engine: tokenization, the forward pass layer by layer, fp8 weights, prefill, CUDA graphs, sessions and speculative decoding.
What people actually say about Graphsignal Profiler — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
2 mentions across 2 sources (Hacker News, GitHub) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Focused on production inference profiling, not just dev-time CUDA tracing
- +Integrates with major frameworks like vLLM, SGLang, and TensorRT-LLM
- +Provides low-overhead kernel attribution and host sync wait detection
- +Offers continuous, high-resolution profiling timelines for operations
- +Includes LLM generation traces with per-step timing and throughput
- −Sparse community data makes reliability unproven at scale
- −Sidecar setup adds deployment complexity vs agentless solutions
- −No public benchmarks against competitors like NVIDIA Nsight Systems
- −Pricing details beyond freemium are unclear, potential hidden costs
- −Documentation and tutorials appear limited for new users
- • Overages for profiles beyond free tier
- • On-premise setup may require additional hardware or engineering hours
Viability Score
How well maintained and how widely used is Graphsignal Profiler? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Continuous high-resolution GPU profiling timelines for inference engines
- CUPTI and ROCm kernel profiling: cumulative time per kernel, per CUDA graph, per memcpy/memset/synchronization op
- Transfer byte counters for GPU memory operations
- NVML GPU telemetry: utilization, memory, power, clocks, throttling, XID errors
- Process and host metrics alongside GPU signals
- Inference engine metrics imported from Prometheus endpoints (vLLM, SGLang, TensorRT-LLM)
- Error capture from engine console output
- GPU probes: vendorable C++ header to instrument app code and CUDA/HIP kernels
- Lock-free probe recording, inert when the profiler is not running
- Local JSON endpoint at 127.0.0.1:18259/signals for AI agents to read every measurement
- graphsignal-run runner that wraps any engine launch command
- graphsignal-top live terminal view of GPU telemetry, engine throughput/latency and kernels
- Optional production feedback loop via the Signals API; nothing uploaded without an API key
- No prompts or completions recorded
- Designed for agent-driven measure-change-remeasure loops on your own machine
About Graphsignal Profiler
Graphsignal Profiler is a production GPU profiler for teams tuning live LLM inference. You wrap your engine launch command with `graphsignal-run`, which injects the profiling library into the workload and starts a watcher sidecar that collects GPU activity, telemetry, engine metrics and errors, then serves it all as JSON at http://127.0.0.1:18259/signals for as long as the workload runs. The workload itself carries only a low-overhead collection path; analysis and the HTTP endpoint live in the sidecar. What lands in a single snapshot: kernel timing via CUPTI and ROCm (cumulative time per kernel, per CUDA graph, per memcpy/memset/synchronization operation), transfer byte counters, NVML telemetry (utilization, memory, power, clocks, throttling, XID errors), inference engine metrics imported from Prometheus endpoints for vLLM, SGLang and TensorRT-LLM, and captured engine console errors. A vendorable C++ header lets you or an agent instrument your own code and CUDA/HIP kernels with lock-free recording that stays inert when the profiler isn't running. graphsignal-top renders the same JSON as a live terminal screen. Optionally, setting GRAPHSIGNAL_API_KEY uploads signals so agents can read production behavior back through the Signals API and tune the next deployment — without a key nothing is uploaded, and no prompts or completions are recorded. It is aimed at inference engineers who already read a per-kernel table, not at general application monitoring.
Behind the Verdict
Graphsignal's core argument is that production workloads report what they actually did, and that an agent should be able to read that back from wherever it works, find what to fix, change flags or code, and ship the result. The mechanism is deliberately unglamorous: `graphsignal-run` wraps your launch command, injects the GPU activity library into the workload, and starts a watcher sidecar that collects GPU activity, NVML telemetry, engine metrics from Prometheus endpoints and engine console errors, serving everything at a local JSON endpoint on 127.0.0.1:18259. The workload carries only the low-overhead collection path; statistics and the HTTP endpoint live in the sidecar, which is the design choice that makes production overhead plausible. What you get in one snapshot is broader than a one-off capture: cumulative time per kernel, per CUDA graph, and per memcpy/memset/synchronization kind via CUPTI and ROCm, plus transfer byte counters; utilization, memory, power, clocks, throttling and XID errors from NVML; vLLM, SGLang and TensorRT-LLM metrics; and error strings from the engine. The vendorable C++ header for GPU probes means instrumented values sit next to the built-in profiles, with lock-free recording that is inert without the profiler running — you can ship the header and pay nothing when profiling is off. graphsignal-top turns the same JSON into a live terminal screen, locally or over the network. Against torch.profiler and nsys, Graphsignal's own comparison concedes those tools already give you per-kernel GPU time, and argues the delta is comparable JSON snapshots, telemetry in the same document, GPU probes, overhead low enough for production, and a runner that wraps any engine. That framing is honest about what it isn't: it is not a replacement for per-kernel analysis, it's a packaging of per-kernel analysis plus the surrounding context into a machine-readable document. Where it doesn't fit: this is not application performance monitoring for web or mobile apps, and it is not a training-workload profiler. Setup assumes CLI, Python and inference-engine familiarity — graphsignal-run wraps a launch command rather than offering point-and-click onboarding. On the money side, the free tier's 100 profiled GPU-hours per month (one GPU/accelerator profiled for one hour) and Pro's 30-day data retention mean long-horizon trend analysis needs the metered plan or Enterprise, on-premise is Enterprise-only, and continuous always-on profiling on Pro carries a recurring usage-based cost at $0.08 per profiled GPU-hour. Subscriptions bill at the start of each cycle, monthly or annually, and canceled subscriptions are not refunded — they stay active to the end of the cycle. Invoice billing exists only for some annual bundles.
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Real-world workflow fit
Concrete scenarios for the personas Graphsignal Profiler actually fits — and what changes day-one when you adopt it.
Wrap `vllm serve` with graphsignal-run on a staging node, then read 127.0.0.1:18259/signals to compare prefill vs decode kernel share against the published benchmark.
Outcome: You see prefill at an unexpected share and the engine metric showing KV cache pressure, so you change --max-num-seqs and --gpu-memory-utilization and measure again in the same session.
Set GRAPHSIGNAL_API_KEY so signals upload, then have an agent query the Signals API for instances and per-instance signals over the last six hours.
Outcome: Production p95 TTFT rising while the local benchmark is unchanged becomes visible at the instance level, and you can replay the production request log under graphsignal-run before rolling flags out.
Instrument one hot CUDA kernel with the vendorable C++ header, leaving probe recording inert when the profiler is off, and compare probe values next to the built-in kernel profiles.
Outcome: You get your own counter beside CUPTI's cumulative kernel time in the same JSON document, without a separate capture format.
Use Cases
- Profile GPU kernel execution and surface host sync waits in a production inference engine
- Trace LLM generation steps, prefill vs decode split, and token throughput in vLLM deployments
- Monitor GPU utilization, power, clocks, throttling and XID errors across accelerators
- Set up an autonomous optimization loop where an agent changes flags and re-measures locally
- Debug production inference errors with engine console output and device-level failure capture
- Replay a production request log under graphsignal-run to isolate preemption and KV-cache pressure
- Render the same signals as a live terminal screen with graphsignal-top
- Feed profiling context to AI coding agents to tune an engine directly
Limitations
- The free tier allows 100 profiled GPU-hours per month (one GPU/accelerator profiled for one hour), and Pro retains data for 30 days, so long-horizon trend analysis needs the metered Pro plan or Enterprise.
- On-premise deployment is Enterprise-only.
- Setup assumes familiarity with the CLI and an inference engine — graphsignal-run wraps a launch command rather than offering point-and-click onboarding.
- Pro is billed at $0.08 per profiled GPU-hour, so continuous always-on profiling carries a recurring usage-based cost; subscriptions bill at the start of each cycle and canceling does not refund.
as of 2026-10-13
Verification history
We have re-verified Graphsignal Profiler 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Graphsignal Profiler tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
A single inference engineer testing the profiler locally on one GPU before committing to continuous profiling.
What this tier adds
Starting tier: $0 for 100 profiled GPU-hours per month, telemetry for AI agents and observability dashboards, capped at 1 user.
Pro
$0.08 per profiled GPU-hour
Ideal for
A platform team profiling production inference continuously across engines and accelerators, with more than one person needing access.
What this tier adds
Adds continuous production profiling, unlimited users and 30 days of data retention over Free, billed at $0.08 per profiled GPU-hour.
Enterprise
Custom
Ideal for
Organizations that need the profiler running on their own hardware or that profile enough GPU-hours to want volume pricing.
What this tier adds
Everything in Pro plus an on-premise option and volume discounts, priced on contact.
Where the pricing makes sense
The company stage and team size where Graphsignal Profiler's pricing actually pencils out — and where peers do it cheaper.
Free ($0/mo, 100 profiled GPU-hours per month, 1 user) is enough for a single engineer testing an engine locally. Pro at $0.08 per profiled GPU-hour with unlimited users and 30-day retention fits a platform team profiling continuously; the bill scales with how much GPU time you profile, so a team profiling several GPUs around the clock should price it against Enterprise volume discounts. Competing per-kernel tools like nsys cost nothing directly but deliver no telemetry or agent-readable
Setup time & first value
How long it actually takes to get something useful out of Graphsignal Profiler — broken out by persona, not the marketing-page minute.
A single engineer installing the profiler and profiling one local workload can follow the Quick Start and reach a first JSON snapshot in minutes, since graphsignal-run only needs to wrap an existing launch command. Getting a useful agent loop or a production feedback loop going with the Signals API is an afternoon, and it assumes comfort with the CLI, Python and the engine's own flags.
Switching to or from Graphsignal Profiler
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From nsys or torch.profiler: keep your existing workload command and prefix it with graphsignal-run to get the same kernels plus telemetry and engine metrics in one JSON document.
- →From engine defaults: read the profiling and telemetry data before choosing startup flags instead of carrying generic defaults over from an older deployment.
- →From manual log reading: point an agent at 127.0.0.1:18259/signals rather than parsing engine console output by hand.
- ↗To nsys: export or re-run the workload under nsys if you need its deeper per-kernel visualization and are willing to lose the single-document telemetry view.
- ↗To torch.profiler: use PyTorch's own profiler if you only need per-kernel GPU time inside a Python training or inference script.
Integrations
Resources & Guides
- Documentationgraphsignal.com
Docs · Graphsignal Profiler
Full product docs from graphsignal.com
- API Referencegraphsignal.com
Signals Api · Graphsignal Profiler
Methods, params, types from graphsignal.com
- Resourcegraphsignal.com
Blog · Graphsignal Profiler
Helpful link from graphsignal.com
- Resourcegraphsignal.com
Understanding Inference For Systems Engineers · Graphsignal Profiler
Helpful link from graphsignal.com
- Documentationgraphsignal.com
Security · Graphsignal Profiler
Full product docs from graphsignal.com
Tutorials & Learning
YouTube returned 6 videos for “Graphsignal Profiler”, and we withheld 6: 6 did not mention Graphsignal Profiler. We are showing none, because we could not prove any of them are about Graphsignal Profiler.
Official links
Tools that pair well with Graphsignal Profiler
Common stack mates teams adopt alongside Graphsignal Profiler, with the specific reason each pairing earns its keep.
Maxim AI
Maxim AI evaluates, simulates and observes AI agents — with Bifrost, its open-source LLM gateway, handling routing, budgets and guardrails.
Resolve AI
AI SRE platform whose autonomous agents go on-call, triage alerts, investigate incidents and run production tasks alongside your engineers.
LangSmith
LangSmith is agent and LLM observability: trace, monitor, and evaluate AI agents in cloud, BYOC, or self-hosted.
Featured Head-to-Head Comparisons
Graphsignal Profiler vs Spider Cloud
Choose Graphsignal Profiler if you're an AI engineer optimizing inference performance on GPUs/accelerators in production; its new CUDA profiler (June 2026) adds kernel attribution and host sync wait detection. Choose Spider Cloud if you need fast, reliable web data for AI agents or RAG — its Rust engine and 99.9% success rate at $0.003/page make it cost-effective. They solve completely different problems, so pick based on whether you debug model latency or extract web content.
Graphsignal Profiler vs Temporal Ai
Temporal AI and Graphsignal Profiler serve completely different purposes. Temporal is for orchestrating durable, long-running workflows (including AI agents) with automatic fault tolerance, while Graphsignal is for deep-diving into inference performance at the GPU/accelerator level. Choose Temporal if you need reliable multi-step orchestration; choose Graphsignal if you need to optimize production inference latency and throughput. They are complementary – you could use both, but not as alternatives.
Graphsignal Profiler vs Screenplayiq
Choose ScreenplayIQ if you're a screenwriter or producer who needs data-driven box office predictions from scripts. Choose Graphsignal Profiler if you're an AI engineer optimizing inference latency and GPU utilization in production. They serve completely different markets—there's no overlap.
Alternatives to Graphsignal Profiler
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Maxim AI
Maxim AI evaluates, simulates and observes AI agents — with Bifrost, its open-source LLM gateway, handling routing, budgets and guardrails.
Resolve AI
AI SRE platform whose autonomous agents go on-call, triage alerts, investigate incidents and run production tasks alongside your engineers.
Frequently Asked Questions
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