RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
AI / ML
PyTorch
Custom model training, fine-tuning, and production deployment with PyTorch
70%+AI researchers use it
2×Faster with torch.compile
CUDAGPU acceleration native
4M+Monthly PyPI downloads
HOW WE USE IT
PyTorch in our stack
We use PyTorch for custom model development — from training domain-specific classifiers and regressors to fine-tuning foundation models for specialized tasks. Our PyTorch work spans computer vision, NLP, tabular ML, and time-series forecasting.
CAPABILITIES
What we deliver
- Custom model architecture design and training
- Fine-tuning foundation models (LLaMA, BERT, ViT)
- ONNX export for cross-platform inference
- TorchScript and TorchServe deployment
- Distributed training with PyTorch DDP
- Quantization and pruning for edge deployment
USE CASES
How we apply PyTorch
Document Classifier
BERT fine-tune for domain-specific document classification with 92%+ accuracy on legal or medical text.
Demand Forecasting Model
PyTorch LSTM/Transformer model for time-series demand forecasting with uncertainty quantification.
Visual Inspection AI
Custom CNN for manufacturing defect detection, quantized for edge deployment on industrial hardware.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
Didn't find what you were searching for? Reach out to us at [email protected] and we'll assist you promptly.
PyTorch is the standard for research and custom model development — its dynamic computation graph makes debugging intuitive, the Pythonic API reduces boilerplate, and the HuggingFace ecosystem (Transformers, Diffusers, PEFT) is PyTorch-native. For fine-tuning foundation models, building custom architectures, or any use case where you need to step through forward passes during development, PyTorch is the clear choice. TensorFlow has advantages for mobile deployment with TFLite and for teams already invested in the TF2/Keras ecosystem.
A production PyTorch deployment includes: model serialization with TorchScript or ONNX export for optimized inference, Triton Inference Server or FastAPI for serving, dynamic batching for throughput optimization, quantization (INT8/FP16) for latency and cost reduction, A/B testing infrastructure for safe model rollouts, and drift monitoring with automated retraining triggers. We use TorchServe or custom FastAPI servers depending on the serving requirements.
Fine-tuning a foundation model for a specific use case typically takes 4-8 weeks including data preparation, training, evaluation, and deployment. Building a custom model architecture from scratch takes 8-16 weeks. Deploying an existing PyTorch model to a production serving infrastructure (without training) takes 2-4 weeks.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
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GET STARTED
Want to use PyTorch in your project?
Talk to an engineer about your requirements. Proposal within 48 hours.