PyTorch development company
Krapton is a PyTorch development company that takes models from notebook to production: data pipelines, training loops that scale across GPUs, evaluation you can trust, and inference services sized for your latency and budget.
- Senior engineers · IST + EST overlap
- NDA on day 1
- 24-hour reply
- Projects shipped
- 50+
- Code ownership
- 100%
- Client rating
- 4.8/5
- First reply
- 24h
PyTorch development services from data to deployment
Krapton is a PyTorch development company for teams that need models they own rather than another API call: domain-specific vision systems, speech and audio models, recommendation rankers, and open-weight language models adapted to private data. The hard part of PyTorch work is rarely the model definition. It is the surrounding engineering: DataLoader pipelines that keep GPUs busy, mixed-precision training with bf16 autocast, gradient accumulation and checkpointing, distributed runs with DDP or FSDP, and evaluation sets that reflect real traffic instead of a benchmark. Our engineers write that code, profile it with torch.profiler and Nsight Systems, and track every run in Weights & Biases or MLflow so results are reproducible. For serving, we compile with torch.compile, export through torch.export or ONNX, and deploy on Triton Inference Server, vLLM or ExecuTorch for devices. If gradient-boosted trees or a hosted model would solve the problem with less effort, we recommend that instead.
PyTorch development services
Custom model development
We design and train CNN, transformer and encoder-decoder architectures in PyTorch for vision, audio, text and ranking problems, starting from pretrained backbones whenever transfer learning beats training from scratch.
LLM fine-tuning
Open-weight language models are adapted with LoRA or QLoRA through PEFT and TRL, trained on curated instruction data, and compared against the base model on your own evaluation set.
Distributed training setup
Multi-GPU and multi-node jobs launched with torchrun, sharded with FSDP, checkpointed with torch.distributed.checkpoint, and scheduled on Kubernetes, Slurm or managed services such as SageMaker and Vertex AI.
Inference optimization
We profile latency, then apply torch.compile, quantization with torchao, batching and KV-cache tuning, or export to ONNX Runtime and TensorRT where the deployment target benefits.
Model serving and MLOps
Models ship behind Triton, vLLM or a FastAPI service with versioned artifacts, canary rollouts, GPU autoscaling and drift monitoring that compares live inputs with the training distribution.
On-device and edge models
For mobile and embedded targets we prune and quantize models, then deploy with ExecuTorch or convert through coremltools for Apple devices, checking accuracy after every compression step.
Why teams choose Krapton for PyTorch
Senior engineers, fixed scope, and code you own — the things agencies promise and rarely deliver.
Evaluation before architecture
We build the evaluation set and baseline first, so every model change is judged against numbers that match your use case rather than a leaderboard score.
Reproducible training runs
Seeds, data versions, configs and code commits are logged per run, and checkpoints are stored with metadata, so any result can be traced to its inputs and rerun later.
GPU spend treated as engineering
We size batch, precision and parallelism to the hardware, use spot or preemptible instances with resumable checkpoints, and stop runs early when validation curves flatten.
Honest about simpler options
Tabular churn models often do better with LightGBM, and many text tasks only need a hosted API with retrieval. We say so before proposing a custom PyTorch build.
How a PyTorch project runs with Krapton
01
Problem and data audit
We define the target metric, inspect label quality and class balance, and check whether enough representative data exists before any GPU time is booked.
02
Baseline and experiments
A simple baseline sets the bar; then tracked experiments test architectures, augmentations and hyperparameters, with results reviewed with your team at each milestone.
03
Optimize and package
The chosen model is compiled, quantized if accuracy allows, exported for the serving runtime and load tested against your latency and throughput targets.
04
Deploy and monitor
We roll out behind a canary, log predictions for audit, watch drift and error slices, and schedule retraining when live performance degrades.
The PyTorch stack we build with
PyTorch development: questions answered
Why hire a PyTorch development company instead of using a hosted AI API?
Hire a PyTorch development company when you need a model you control: one trained on private data, deployed in your own cloud or on devices, or tuned for a task hosted APIs handle poorly. Vision inspection, audio classification, ranking and domain-specific language models are common examples. If a hosted model with retrieval meets your accuracy, latency and privacy needs, Krapton will recommend that simpler route first.
PyTorch or TensorFlow for a new project?
PyTorch is the default choice for most new deep learning projects. Most recent research code, Hugging Face model implementations and open-weight LLM tooling target PyTorch first, which shortens the path from paper to product. TensorFlow still makes sense if you already run TFX pipelines or rely heavily on TensorFlow Lite (now LiteRT) deployments. JAX is worth considering for TPU-centric research. For an existing TensorFlow system, we weigh migration effort against the benefits before recommending a switch.
Is TorchScript still the right way to deploy PyTorch models?
No, for new work torch.export is the recommended path. TorchScript is in maintenance mode, while torch.export produces a graph that feeds ExecuTorch for devices, AOTInductor for compiled server binaries and other backends. Many teams also export to ONNX for ONNX Runtime or TensorRT. We keep existing TorchScript models running where they work and migrate them when a model is retrained or its runtime changes.
How do you make PyTorch training and inference faster?
Profile first, then fix the actual bottleneck. Training is often data-bound, so we tune DataLoader workers, pinned memory and file formats before touching the model, then add bf16 autocast, torch.compile and FSDP where they help. Inference gains usually come from batching, quantization with torchao, KV caching for transformers and choosing the right serving runtime. Each change is measured against accuracy so speed never quietly costs quality.
What determines the cost of PyTorch development services?
Data and compute shape the budget more than code does. The main drivers are how much labeling or cleaning your data needs, whether you fine-tune an existing model or train from scratch, model size and GPU hours, the latency target in production, deployment targets such as mobile, and ongoing retraining. GPU and cloud bills are paid to your provider directly, and we estimate them per experiment phase before training starts.
Ready to build? Tell us what you have in mind.
Send a short brief and a senior engineer will reply within one business day with next steps — no sales fluff.