A patch
$ git apply autooptm.patch $ python your/entry.py …
A standard diff with no dependency on our service. Your code runs exactly as it did before.
Make your code
run faster
Point it at a repo or upload a zip. Get back a patch.
Your first run is free. See the measured speedup on your code; pay only to unlock the patch
before
937.8ms
→
after
249.2ms
same command · same inputs · same output
Measured on public repositories · end to end · output verified
23.41×csvkit
14.65×networkx
13.15×SwinIR
9.16×xarray
8.56×CLIP
7.59×CycleGAN / pix2pix
7.48×litgpt
5.82×open_clip · CLAP
5.24×statsforecast
5.22×DINOv2
4.90×BasicSR
4.58×coqui TTS
3.80×gated_attention
3.75×sapiens
3.69×Wan 2.1 VAE
3.44×vision · ResNet-50
3.42×gaussian-splatting
3.41×mmaction2 · TSN
3.37×sapiens2
3.35×Real-ESRGAN
3.31×OpenPCDet
3.29×PaddleOCR
3.29×CUT3R
3.03×sentence-transformers
2.68×moviepy
2.65×pytorch-image-models
2.62×yolov5 · detect
2.61×shallow-vs-deep-alignment
2.53×DeepSpeed · inference
2.48×nerfstudio
2.26×transformers · GPT-2
2.25×DiT
2.23×YOLOv12
2.13×Wan 2.2 VAE
1.98×LightningDiT
1.90×CoTracker
1.86×PySceneDetect
1.84×GroundingDINO
1.82×SAM 2
1.77×stable-diffusion-webui
1.65×REPA-E
1.64×HunyuanVideo VAE
1.63×RIFE
1.63×SAM 3
1.58×vggt
1.56×WhisperLive
1.56×fast3r
1.49×bd3lms
1.47×hifi-gan
1.41×nanoGPT
1.41×MiniMax-H3 VAE
1.39×GFPGAN
1.35×VibeVoice
1.28×TabICL
1.24×yolov5 · train
1.22×mmdetection · Faster R-CNN
1.22×nwm
1.19×whisperX
1.18×darts
23.41×csvkit
14.65×networkx
13.15×SwinIR
9.16×xarray
8.56×CLIP
7.59×CycleGAN / pix2pix
7.48×litgpt
5.82×open_clip · CLAP
5.24×statsforecast
5.22×DINOv2
4.90×BasicSR
4.58×coqui TTS
3.80×gated_attention
3.75×sapiens
3.69×Wan 2.1 VAE
3.44×vision · ResNet-50
3.42×gaussian-splatting
3.41×mmaction2 · TSN
3.37×sapiens2
3.35×Real-ESRGAN
3.31×OpenPCDet
3.29×PaddleOCR
3.29×CUT3R
3.03×sentence-transformers
2.68×moviepy
2.65×pytorch-image-models
2.62×yolov5 · detect
2.61×shallow-vs-deep-alignment
2.53×DeepSpeed · inference
2.48×nerfstudio
2.26×transformers · GPT-2
2.25×DiT
2.23×YOLOv12
2.13×Wan 2.2 VAE
1.98×LightningDiT
1.90×CoTracker
1.86×PySceneDetect
1.84×GroundingDINO
1.82×SAM 2
1.77×stable-diffusion-webui
1.65×REPA-E
1.64×HunyuanVideo VAE
1.63×RIFE
1.63×SAM 3
1.58×vggt
1.56×WhisperLive
1.56×fast3r
1.49×bd3lms
1.47×hifi-gan
1.41×nanoGPT
1.41×MiniMax-H3 VAE
1.39×GFPGAN
1.35×VibeVoice
1.28×TabICL
1.24×yolov5 · train
1.22×mmdetection · Faster R-CNN
1.22×nwm
1.19×whisperX
1.18×darts
Same command, timed end to end, output checked. Results vary with the GPU and the inputs.
A number
3.75×
Your command and your inputs, sampled repeatedly before and after, with the median reported.
03 Why us
| AutoOptm | ClaudeOpenAICodexQwenDeepSeekPrompting an LLM yourself | Hiring an engineering team | |
|---|---|---|---|
| Speedup | 2.48×median · 50+ projects measured | ~1.4×typical | Variesdepends on who and how long |
| Cost | $3.5a third of runs cost this or less · median $4.60 · free if no speedup | $9+coding plan + rented GPU · ~$19 on API pricing | $4k–8k~$100/hour × 1–2 weeks |
| Time to result | 1–3 h2.3 h median · unattended | 1–2 hyour own time, prompting and watching | 1–2 wkscheduling, meetings, hand-off |
| Your time | Submit and walk away | All of it | Meetings, Q&A, review |
| Output changes | Never; out-of-tolerance changes revert | Unchecked | Up to the engineer |
| Proof | Frozen harness, median of repeated runs | Build it yourself | Self-reported |
| No speedup | Not charged | Tokens still billed | Hours still billed |
04 Integrations
CLI
$ pipx install autooptm $ autooptm login $ autooptm run . --entrypoint infer.py --gpu auto $ autooptm unlock <jobId> $ git apply autooptm.patch
A free estimate first, then it runs to the end and prints the speedup and price. You review the file list before anything uploads.
runestimatedecidestatusunlockcancelbalancedatasetskeysloginlogoutinstall-skill
MCP
{
"mcpServers": {
"autooptm": { "command": "autooptm-mcp" }
}
}Works in Claude Code, Cursor or any MCP client. Anything that spends credits waits for your OK.
optimize_submitoptimize_estimateoptimize_decideoptimize_statusoptimize_waitoptimize_cancelunlock_patchdownload_patchaccount_balance
Agent skill
$ pipx install autooptm $ autooptm install-skill
Then just tell Claude Code: "optimise this repo with AutoOptm".
Self-hosted
coming soon
Runs on your own GPUs. Tokens only, no machine charge.
05 Who this is for
ML engineer at a tech company
½the compute, same quality
Halve the step time; run twice the experiments on the same budget.
Grad student
2×experiments per week
Twice as fast on one GPU is a second GPU for free.
Indie developer
2×the QPS, same GPU
The same 4090 serves twice the requests at half the unit cost.
Competitive ML practitioner
+3more models per night
They train three models a night; you train six.
06 Code security
Your code is deleted when the run ends
Runs only in a disposable sandbox
One isolated sandbox per run, destroyed after; our dispatcher never runs your code.
No repository access
No tokens, no GitHub App. Paste a public URL or upload a .zip.
Results are yours alone
Reports and patches need sign-in to download; your code and data never train models.
The full terms are in the privacy policy.
07 Pricing
Metered on actual usage
Quoted first, charged after
0Free estimate
2 creditsContinue: deposit
quote − 2Unlock the patch
No speedup, or the failure is ours: the deposit comes back. Refund details →
Trial pack
$3
10 credits · $0.30 / credit
- The best rate we offer, once per account
- ≈ 2–3 full runs on your own repository (a run takes about 2–5 credits)
- Credits valid 24 months
- Private code supported (zip upload)
Usage pack
$5 +
$0.77 / credit · +30% in beta · any amount
- Credits valid for 24 months
- Private repos, results kept private
- Buy more as needed, no commitment
- A run takes about 2–5 credits ≈ $1.5–4, depending on code size, complexity, what the run finds and the speedup
- Card or WeChat Pay, any amount; WeChat Pay requires a QR scan on desktop
$0.50–$5000. Card or WeChat Pay; WeChat Pay requires scanning a QR code on desktop.
Pro +50% credits · beta
$28 / mo
42 credits · $0.67 / credit
- 50% bonus credits per dollar during the beta
- ≈ 10–15 full runs per month
- Private repos · 5 concurrent
Max +60% credits · beta
$99 / mo
158.4 credits · $0.63 / credit
- ≈ 40–60 full runs per month
- Private repos · priority queue · 5 concurrent
Max+ +70% · beta
$199 / mo
338.3 credits · $0.59 / credit
- ≈ 85–130 full runs per month
- Top queue priority · 5 concurrent
- For heavy individual use: large repos, many parallel runs
Beta bonus · top-ups +30% · Pro +50% · Max +60% · Max+ +70%
Subscription credits reset each period · packs last 24 months
Packs: WeChat Pay (desktop QR) or card · Subscriptions: card only · WeChat users can buy the 30-day pack at the same price and bonus
See how much faster your code can run
The estimate is free and ready in seconds. See the projected speedup first, then decide whether to spend the two credits.