nCompass Technologies is live · custom pricing · last verified 31 Oct 2026 · viability monitor
nCompass Technologies
nCompass Technologies is an AI performance engineer that reads your GPU profiling traces and proposes ranked, diff-verified fixes for CPU-GPU bottlenecks.
Worth a serious look if your team already generates nsys or ncu traces and has a pile of unexplained slowness. The ranked fixes with recoverable-microsecond estimates, diff-verified so you know output didn't change, target exactly the work that eats engineering weeks — the Ubicloud result (25% higher inference throughput, 25% lower median per-token latency on Kimi K2.6) is the kind of number that justifies the seat. Compare against free Nsight tooling first: nCompass layers on top of Nsight Systems and Nsight Compute, so you still need those installed either way.
Verdict generatedVerified 48m ago · liveness 68/100 · cite: rightaichoice.com/tools/ncompass-technologies
- Performance engineers profiling large GPU workloads daily
- ML platform teams debugging production LLM serving latency
- Teams optimizing generative recommender and ranking models at scale
- Developers already working in Claude Code, Cursor, or VS Code who need GPU-specific optimization
- Teams with no bottleneck backlog who are fine reading raw nsys and ncu output
- Anyone needing a full MLOps platform (model registry, experiment tracking, CI/CD)
- Teams doing CPU-only performance work
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Skip nCompass if your team already reads nsys and ncu output fluently and has no backlog of unexplained GPU slowness — you would be paying to automate a diagnosis you already do by hand.
That makes the real budget question a scoping one: what a seat costs against the engineering weeks the ranked, diff-verified fixes recover. Ask their team for numbers before you model the ROI.
In short
nCompass Technologies — nCompass Technologies is an AI performance engineer that reads your GPU profiling traces and proposes ranked, diff-verified fixes for CPU-GPU bottlenecks. Best for Performance engineers profiling large GPU workloads daily, ML platform teams debugging production LLM serving latency, Teams optimizing generative recommender and ranking models at scale. Pricing is on request (custom quotes).
What is nCompass Technologies?
nCompass Technologies — nCompass Technologies is an AI performance engineer that reads your GPU profiling traces and proposes ranked, diff-verified fixes for CPU-GPU bottlenecks. Best for Performance engineers profiling large GPU workloads daily, ML platform teams debugging production LLM serving latency, Teams optimizing generative recommender and ranking models at scale. Pricing is on request (custom quotes).
nCompass Technologies is an AI performance engineer for platform teams running generative recommender, ranking, and LLM serving at scale. You point it at the profiling traces your team already produces — .nsys-rep, .ncu-rep, or .diff.json captures from nsys, ncu, perf, torch, or rocprof — and it reads them, locates the bottleneck, and proposes a fix at the system, framework, or kernel level. The work happens against real captures rather than synthetic benchmarks: one published sample walks a 29.16-second nsys trace covering 456,677 kernels and reports that 96.9% of GPU activity sits on a single stream with only 923 ms of idle time, which rules out launch-bound behavior and points the team somewhere useful. The agent is reachable in plain language from the terminal or editor, and three IDE surfaces carry the analysis inline: VS Code and Cursor extensions for trace viewing and diffing, plus a Claude Code plugin for trace agent workflows. TraceDiff is the trust mechanism — it verifies a kernel rewrite produces identical output before anyone believes the speed claim. Fixes arrive as ranked lists with recoverable microseconds attached, so a platform lead can argue priority with numbers. An `npx ncompass` CLI handles push, pull, list, and share for trace files too large to pass around in chat. Named users do the credibility work: a vLLM contributor on the Claude Code plugin, Perforated AI cutting runs from 88 to 82 seconds and 184 to 147 seconds with about 30 lines changed, and Ubicloud reporting 25% higher inference throughput plus 25% lower median per-token latency on Kimi K2.6 after nCompass found three stacked optimizations, including a custom MoE communication kernel up to 2.6x faster than NCCL. Analysis runs on existing workloads with no code changes, and only traces are uploaded — code and data stay local. This is a focused performance tool for teams that already have a bottleneck backlog, not an MLOps platform. It sits alongside Nsight Systems and Nsight Compute rather than replacing them.
Key features
- Plain-language performance query agent over profiling traces
- Bottleneck detection across system, framework, and GPU kernel levels
- System-level stall and synchronization diagnosis from trace captures
- Kernel development verified with TraceDiff output comparison
- Ranked fix lists with recoverable microseconds per item
- Trace sharing via ncompass CLI push, pull, list, and share
- VS Code extension for inline trace viewing and diffing
- Cursor extension for inline trace viewing and diffing
- Claude Code plugin for trace agent workflows
- Supports .nsys-rep, .ncu-rep, and .diff.json trace formats
- Connects to perf, nsys, torch, ncu, and rocprof profilers
- Trace Viewer, Trace Diff, Kernel Analyzer, and Bottleneck Finder IDE panels
- No-code-change analysis of existing GPU workloads
- Code and data stay local; only traces are uploaded
- CLI invoked via npx ncompass
Behind the verdict
The pitch is narrow and that is the point. nCompass does not try to be your experiment tracker or model registry; it takes a trace you already captured and tells you where the microseconds went. That is a real gap: general coding agents will happily rewrite a kernel, but they lack the profiling tooling and GPU expertise to know which kernel is worth rewriting, and the vendor says exactly that on its homepage.What makes the product trustworthy is the verification loop rather than the suggestion. TraceDiff compares the output of a rewritten kernel against the original before anyone believes the speed claim — without that step, a kernel benchmark is just an optimistic number. On top of it, ranked fix lists carry recoverable microseconds per item, which turns a platform lead's prioritization argument into arithmetic. The .nsys-rep, .ncu-rep, and .diff.json formats plus support for nsys, ncu, perf, torch, and rocprof mean you are feeding it the traces your team already produces rather than adopting a new instrumentation layer.The workflow surfaces are chosen sensibly for the audience. VS Code and Cursor extensions put Trace Viewer, Trace Diff, Kernel Analyzer, and Bottleneck Finder panels inline, a Claude Code plugin covers trace agent workflows, and `npx ncompass` handles push, pull, list, and share so a 500 MB nsys capture does not travel by email. The public customer evidence is concrete and attributable — Perforated AI going from 88 to 82 seconds and 184 to 147 seconds with about 30 lines changed, Ubicloud finding three stacked optimizations including a custom MoE communication kernel up to 2.6x faster than NCCL.The honest constraints: results are only as good as the trace you collect, so garbage in still applies. CPU-only performance work is out of scope. Trace sharing means traces leave the machine, so teams with strict data-locality rules need to confirm what gets uploaded — though code and data stay local, which softens the concern. And if your team is content reading raw nsys and ncu output, you are the tool's comparison set, not its customer. Treat nCompass as an accelerator on top of the Nsight stack, not a replacement.
Limitations
- nCompass is a specialist tool for GPU performance profiling and optimization, not a full MLOps platform — you still need separate tools for experiment tracking, model registry, and CI/CD.
- It works on traces, so you must run nsys, ncu, perf, torch, or rocprof yourself and feed it supported formats; results are only as good as the trace data you collect.
- It focuses on GPU workloads and does not address CPU-only performance problems.
- The vendor states plainly that general coding agents lack profiling tooling and GPU expertise, which is the gap nCompass fills — but that also means it is an addition to your agent stack, not a replacement for a general coding assistant.
- Because traces are uploaded for sharing and analysis, teams with strict data-locality requirements should confirm what leaves the machine before adopting, even though code and data stay local.
as of 2026-10-17
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Is nCompass Technologies still active?
nCompass Technologies is live · custom pricing · last verified 31 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is nCompass Technologies? 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 →What do users say about nCompass Technologies?
What people actually say about nCompass Technologies — is it worth it?
Our community sentiment research written Jul 3, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about nCompass Technologies— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 31 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified nCompass Technologies 11 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.
- — changed SSL certificate
- — changed domain expiry
- — changed who it suits, switching out, our verdict, setup time, who should skip it, pricing fit, summary, description, features, hidden costs, integrations, limitations, switching in
- — changed who should skip it, summary, our verdict, who it suits, features, description
- — changed pricing page
- — changed setup time, who it suits, description, our verdict, features, hidden costs, integrations, limitations, switching in, switching out, who should skip it, pricing fit, summary, use cases
Showing the 6 most recent of 11 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does nCompass Technologies cost in 2026?
nCompass Technologies: Pricing is on request (custom quotes). Pricing last verified 31 Oct 2026.
Where the pricing makes sense
The company stage and team size where nCompass Technologies's pricing actually pencils out — and where peers do it cheaper.
That makes the real budget question a scoping one: what a seat costs against the engineering weeks the ranked, diff-verified fixes recover. Ask their team for numbers before you model the ROI.
Has nCompass Technologies' pricing changed?
No plan price for nCompass Technologies has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick nCompass Technologies?
Pick nCompass Technologies if you are Performance engineers profiling large GPU workloads daily, ML platform teams debugging production LLM serving latency, Teams optimizing generative recommender and ranking models at scale, Developers already working in Claude Code, Cursor, or VS Code who need GPU-specific optimization.
Look elsewhere if you are Teams with no bottleneck backlog who are fine reading raw nsys and ncu output, Anyone needing a full MLOps platform (model registry, experiment tracking, CI/CD), Teams doing CPU-only performance work, Teams that cannot share trace files outside their own infrastructure.
Skip it if Skip nCompass if your team already reads nsys and ncu output fluently and has no backlog of unexplained GPU slowness — you would be paying to automate a diagnosis you already do by hand.
GeneratedReal-world workflow fit
Concrete scenarios for the personas nCompass Technologies actually fits — and what changes day-one when you adopt it.
Your LLM serving p99 regressed after a model swap and nobody has time to read a 29-second nsys capture by hand. You drop the .nsys-rep into nCompass, ask in plain language where the time went, and it reports that 96.9% of GPU activity sits on one stream with only 923 ms of idle time.
Outcome: You get a ranked fix list with recoverable microseconds attached, so you can staff the highest-value item instead of guessing at launch overhead.
You rewrite a hot MoE communication kernel and want to ship it, but need proof it computes the same thing. You run TraceDiff against the pre-rewrite capture.
Outcome: Output comparison confirms identical results while the new kernel runs up to 2.6x faster than NCCL, so the speed claim survives code review.
A training run that used to take 184 seconds now takes longer. You open the Cursor extension, use the Bottleneck Finder and Trace Diff panels inline, and apply about 30 lines of backend changes without touching the main training script.
Outcome: The run lands at 147 seconds with the main scripts untouched, and teammates get the trace via an `npx ncompass` share link instead of a file attachment.
Use cases
- Find which GPU stream is eating runtime in a long LLM generation trace and get a ranked list of fixes
- Verify that a hand-rewritten kernel produces identical output with TraceDiff before shipping it for speed
- Share large nsys or ncu traces with remote teammates via a CLI-generated link instead of emailing files
- Diagnose distributed inference setups where a custom collective kernel can beat default NCCL communication
- Cut multi-hour training runs by applying targeted backend changes without touching the main training scripts
- Explain an unexpected slowdown in a quantized model using a per-kernel budget instead of guesswork
Setup time & first value
How long it actually takes to get something useful out of nCompass Technologies — broken out by persona, not the marketing-page minute.
If you already produce nsys or ncu traces, first value is quick: install the VS Code or Cursor extension (or the Claude Code plugin), push an existing .nsys-rep with `npx ncompass`, and ask your first question. Teams that need to instrument from scratch should budget time for a profiling pass before nCompass has anything to read.
Switching to or from nCompass Technologies
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From raw Nsight Systems workflows: keep capturing with nsys as before and route the same .nsys-rep files into nCompass for plain-language analysis and ranked fixes.
- →From raw Nsight Compute workflows: keep .ncu-rep captures and add kernel-level analysis plus TraceDiff verification on top.
- →From ad-hoc Slack trace swapping: replace file attachments with `npx ncompass` push, pull, list, and share.
- →From a general coding assistant: keep it for code edits and add nCompass for the profiling and GPU-expertise layer it lacks.
- ↗To raw Nsight Systems / Nsight Compute: stop uploading traces and read the same captures directly, at the cost of manual interpretation.
- ↗To a full MLOps platform: pair nCompass's profiling output with a separate experiment-tracking and model-registry tool rather than replacing it.
Integrations
Resources & guides
Tutorials & Learning
nCompass API demo
nCompass Technologies
Demo of nCompass API at high concurrency rates
nCompass Technologies
YouTube returned 6 videos for “nCompass Technologies”, and we withheld 4: 4 did not mention nCompass Technologies. Showing the 2 we can prove are about nCompass Technologies.
Official links
What are the best nCompass Technologies alternatives?
The closest alternatives to nCompass Technologies we list are Augment Code, Replit's Ghostwriter Chat and Kombai. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Augment Code | Paid | Pricing not published | — |
| Replit's Ghostwriter Chat | Freemium | Pricing not published | — |
| Kombai | Freemium | Pricing not published | — |
Alternatives to nCompass Technologies
View allAugment Code
Cosmos by Augment Code orchestrates agent loops for code review, ticket-to-PR, security fixes and incident triage.
Replit's Ghostwriter Chat
A conversational AI coding assistant that reads your Replit project files before it answers, right in the browser IDE.
Tools that pair well with nCompass Technologies
Common stack mates teams adopt alongside nCompass Technologies, with the specific reason each pairing earns its keep.
Augment Code
Cosmos by Augment Code orchestrates agent loops for code review, ticket-to-PR, security fixes and incident triage.
Replit's Ghostwriter Chat
A conversational AI coding assistant that reads your Replit project files before it answers, right in the browser IDE.
Kombai
Kombai is an AI design engineer that generates standout UI code matching your repo's components, tokens, and hooks.
nCompass Technologies vs its closest rival
nCompass Technologies's closest head-to-head is with Spider Cloud — Spider Cloud is for extracting web data for AI; nCompass is for accelerating GPU inference. They aren't direct competitors. Choose Spider Cloud if you need a low-cost, high-performance scraping API with AI-ready outputs. Choose nCompass if your bottleneck is GPU inference speed and cost. Verdict generated
Head-to-head comparisons
Ncompass Technologies vs Spider Cloud
Spider Cloud is for extracting web data for AI; nCompass is for accelerating GPU inference. They aren't direct competitors. Choose Spider Cloud if you need a low-cost, high-performance scraping API with AI-ready outputs. Choose nCompass if your bottleneck is GPU inference speed and cost.
GeneratedNcompass Technologies vs Temporal Ai
Temporal AI and nCompass serve entirely different needs. Choose Temporal if you need a durable execution platform for orchestrating complex, long-running workflows with fault tolerance. Choose nCompass if you're focused solely on accelerating GPU inference with zero code changes. They are complementary rather than competitive.
GeneratedNcompass Technologies vs Voyage Ai
Choose Voyage AI if your primary need is high-accuracy retrieval for domain-specific RAG pipelines. Choose nCompass if you already have models and need to cut GPU inference costs without changing code. They serve entirely different purposes—Voyage provides embedding models, nCompass optimizes inference hardware—so pick based on whether your bottleneck is retrieval quality or deployment cost.
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