Deci
Automated NAS and inference optimization for NVIDIA hardware.
Deci delivers real inference speedups for NVIDIA GPU users, but its custom pricing and hardware lock-in limit broader appeal. Consider it only if you're all-in on NVIDIA and need maximum performance from your models.
Verified 17d ago · liveness 93/100 · cite: rightaichoice.com/tools/deci
- AI teams deploying models on NVIDIA GPUs needing latency/throughput optimization
- Developers wanting automated NAS to design efficient model architectures
- Enterprises optimizing inference costs for production ML pipelines
- Computer vision workloads on edge devices
- Teams using AMD or other non-NVIDIA hardware
- Projects requiring full explainability or white-box models
- Simple models where manual optimization is sufficient
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Skip Deci if you use AMD hardware, need transparent upfront pricing, or can achieve sufficient speedups with free tools like TensorRT or ONNX Runtime.
Custom pricing requires a sales call; there's no published list price, so you can't budget without engaging the vendor.
Deci's pricing is custom and likely expensive, targeting enterprises that derive significant value from inference speedups. For smaller teams or individual developers, it's overkill compared to free tools like TensorRT or ONNX Runtime. There is no self-serve tier, so expect a sales-led process with annual contracts.
In short
Deci — Automated NAS and inference optimization for NVIDIA hardware. Best for AI teams deploying models on NVIDIA GPUs needing latency/throughput optimization, Developers wanting automated NAS to design efficient model architectures, Enterprises optimizing inference costs for production ML pipelines. Contact Sales pricing.
Viability Score
How likely is Deci to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Automated neural architecture search (NAS)
- INT8 and FP16 quantization
- Hardware-aware optimization for NVIDIA GPUs
- Model compression and pruning
- NVIDIA TensorRT integration
- PyTorch support
- TensorFlow support
- ONNX support
- Benchmarking and performance profiling
- Deployment to cloud, edge, and mobile
- Custom training with NAS-driven architectures
- Computer vision model optimization
- NLP model optimization
- Deci AI Studio for model development
- Automatic compilation pipeline for target hardware
About Deci
Deci automates deep learning model optimization for NVIDIA hardware using proprietary Neural Architecture Search (NAS) to co-design architectures that are smaller, faster, and more accurate. Aimed at AI developers and data scientists deploying models in production, Deci's platform includes automated model optimization, INT8/FP16 quantization, and hardware-aware NAS that tailors models to specific GPUs. It integrates with NVIDIA TensorRT, CUDA, PyTorch, TensorFlow, and ONNX, supporting deployment to cloud, edge, and mobile devices. Deci differentiates by delivering up to 10x inference speedups without accuracy loss, particularly for computer vision and NLP workloads, but its value is tied to NVIDIA hardware and pricing remains custom, which can be a hurdle for smaller teams.
Behind the Verdict
Deci is a solid choice for teams already committed to NVIDIA GPUs who need to squeeze every bit of performance out of their models. The automated NAS and hardware-aware optimization can save months of manual tuning. However, its custom pricing makes it less accessible for smaller teams or those evaluating multiple vendors. Compared to other optimization tools like TensorRT alone, Deci offers a more automated, NAS-driven approach but at a higher cost and dependency on NVIDIA. In practice, the value is clearest for large-scale deployments where inference cost reduction justifies the investment. Where it bites: if your hardware mix includes AMD or Intel GPUs, Deci won't help, and the lack of transparent pricing can be a barrier to initial evaluation.
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Real-world workflow fit
Concrete scenarios for the personas Deci actually fits — and what changes day-one when you adopt it.
You have a YOLOv5 model running on NVIDIA Jetson Xavier that needs to hit 30 FPS for real-time detection.
Outcome: Upload the model to Deci AI Studio, run hardware-aware NAS and INT8 quantization, and within a few hours get a compiled TensorRT engine achieving 35 FPS.
You need to reduce BERT inference latency on a cloud GPU to under 15ms to meet SLA.
Outcome: Use Deci's automated NAS to find a smaller architecture, apply FP16 quantization, and deploy via TensorRT, cutting latency from 50ms to 12ms.
You want to run a custom CNN on a phone SoC with a strict 30ms latency budget.
Outcome: Use Deci's automated search to design a hardware-aware architecture, prune and quantize it, and export to ONNX; the optimized model runs at 28ms.
Use Cases
- Optimizing a ResNet-50 model for real-time object detection on NVIDIA Jetson Xavier
- Reducing BERT inference latency from 50ms to 15ms on a cloud GPU
- Compressing a YOLOv5 model to run at 30 FPS on a mobile SoC
- Migrating a research prototype to production with automated quantization and compilation
- Profiling and benchmarking multiple model variants to select the fastest one for your hardware
- Automating NAS to design a custom architecture for a specific edge device
Models Under the Hood
as of 2026-07-06
Limitations
- Pricing is custom and requires contacting sales, which complicates budget planning.
- The platform is heavily PyTorch-oriented; TensorFlow users will find limited support.
- Some advanced features have a learning curve, and community resources are sparse compared to open-source alternatives like TensorRT or ONNX Runtime.
- Deci's optimizations are NVIDIA-centric, so users on AMD or other hardware see little benefit.
as of 2026-06-29
Where the pricing makes sense
The company stage and team size where Deci's pricing actually pencils out — and where peers do it cheaper.
Deci's pricing is custom and likely expensive, targeting enterprises that derive significant value from inference speedups. For smaller teams or individual developers, it's overkill compared to free tools like TensorRT or ONNX Runtime. There is no self-serve tier, so expect a sales-led process with annual contracts.
Setup time & first value
How long it actually takes to get something useful out of Deci — broken out by persona, not the marketing-page minute.
For an existing PyTorch model, expect to be profiling within hours after sales onboarding. Complex NAS searches can take a few hours, but the automated pipeline reduces manual tuning dramatically. First optimized model deployment typically achievable in one to three business days.
Switching to or from Deci
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From TensorRT manual tuning: Upload your PyTorch model and let Deci automate architecture search and quantization, potentially saving weeks of manual optimization.
- ↗To ONNX Runtime: Export Deci-optimized models as ONNX and run them with ONNX Runtime on different hardware.
Integrations
Resources & Guides
Tutorials & Learning
Official links
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Common stack mates teams adopt alongside Deci, with the specific reason each pairing earns its keep.
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