Graphsignal Profiler vs ScreenplayIQ
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Graphsignal Profiler | ScreenplayIQ |
|---|---|---|
| Best For | AI engineers optimizing inference performance in production | Screenwriters and studio executives predicting box office returns |
| Key Feature | Continuous high-resolution CUDA kernel profiling | Box office prediction from narrative structure |
| Integration | NVIDIA, PyTorch, vLLM, SGLang, TensorRT-LLM, Claude Code, dstack, GitHub | PitchTrailer |
| Output | Profiling timelines, token throughput breakdowns, error monitoring | PDF reports, beat sheets, heatmaps |
| Latest News | CUDA Profiler for Production Inference (2026-06-23) | No recent news |
GPU profiler that serves inference measurements as local JSON so AI agents can autonomously optimize LLM serving performance.
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AI screenplay analysis that returns a logline, comps, character arcs, and proofed fixes on your draft
Visit WebsiteWhat real users say: Graphsignal Profiler vs ScreenplayIQ
Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.
Graphsignal Profiler
2 mentions across 2 sources · 45% positive — mixed (averaged across 2 sources)
Hacker News, GitHub
What users praise
- • 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
What frustrates them
- • 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
Researched Jul 3, 2026
ScreenplayIQ
No verifiable community signal. We scanned public discussion on Oct 14, 2026 and found posts matching the name “ScreenplayIQ”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.
Who should pick which
- Solo screenwriterPick: ScreenplayIQ
The Pro plan ($19/mo) provides unlimited script analyses with market comparisons, helping improve marketability.
- Studio executivePick: ScreenplayIQ
The Studio plan ($49/mo) supports collaborative evaluation of multiple scripts with predictive ROI.
- ML inference engineerPick: Graphsignal Profiler
Graphsignal provides GPU/accelerator profiling to optimize token throughput and latency in production.
- AI research teamPick: Graphsignal Profiler
Continuous profiling and autodebug telemetry loops accelerate inference optimization experiments.
- Script coverage analystPick: ScreenplayIQ
Quantitative beat-sheet and genre analysis saves time and adds data-backed insights.
Frequently Asked Questions
Can ScreenplayIQ analyze TV pilots or short films?
No. It's designed for feature films only; TV pilots and short films are not supported.
Does Graphsignal Profiler require altering my inference code?
It runs as a sidecar using the `graphsignal-run` CLI or `graphsignal.watch()` Python wrapper, so minimal code changes are needed.
What is the context window limit for scripts in ScreenplayIQ?
Scripts over 150 pages may exceed the AI's context window, limiting analysis.
Does Graphsignal support training profiling?
No, it focuses exclusively on inference profiling, not training.
Can I export ScreenplayIQ reports to PDF?
Yes, PDF export is available.
What integrations does Graphsignal support?
It integrates with NVIDIA, PyTorch, vLLM, SGLang, TensorRT-LLM, Claude Code, dstack, and GitHub.
Is there a free tier for Graphsignal?
Yes, a free tier is available, but it may be limited for production-scale use.
What's the latest feature added to Graphsignal?
On 2026-06-23, a CUDA Profiler for Production Inference was added, providing low-overhead kernel attribution and host sync wait detection.
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Last reviewed: July 3, 2026