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.
Backend
Python
FastAPI services, ML pipelines, and data engineering in Python
#1AI/ML language globally
330K+PyPI packages
60KFastAPI req/sec
35+Years in production
HOW WE USE IT
Python in our stack
Python powers our AI and data backend work — FastAPI for high-performance APIs, Pandas/Polars for data processing, PyTorch/scikit-learn for model development, and Celery for async task queues. Our Python services are typed, tested, and Docker-containerized.
CAPABILITIES
What we deliver
- FastAPI high-performance REST and streaming APIs
- Type hints and Pydantic models throughout
- Celery + Redis async task queues
- SQLAlchemy ORM with PostgreSQL
- pytest test suites with coverage enforcement
- Docker containerization and CI/CD
USE CASES
How we apply Python
ML Inference API
FastAPI service wrapping a PyTorch model with batch inference endpoints, health checks, and Prometheus metrics.
Data Processing Pipeline
Celery workers consuming from Redis queues to process and transform high-volume data for downstream ML.
LLM Orchestration Service
Python service orchestrating multi-step LLM workflows with caching, retry logic, and cost tracking.
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.
Python is the undisputed standard for AI/ML — PyTorch, TensorFlow, scikit-learn, HuggingFace, LangChain, and every major AI library is Python-first. For backends that serve ML models, process data pipelines, or orchestrate AI workflows, Python eliminates the integration friction of cross-language bridges. We use Python for ML systems, data pipelines, and AI backends. We use Go for high-concurrency infrastructure services and Node.js for real-time and JavaScript-stack APIs.
A production Python backend includes: FastAPI for async API endpoints with automatic OpenAPI documentation, Pydantic for request/response validation, Celery or ARQ for background tasks, Redis for caching and message queuing, SQLAlchemy with async support for database operations, pytest with coverage enforcement, and Docker-based deployment on Kubernetes or ECS. We apply type hints throughout for maintainability and use Black and Ruff for code quality.
A production Python API with authentication, core business logic, and database integration typically takes 6-10 weeks. An ML inference service — model loading, request processing, caching, and monitoring — takes 3-6 weeks on top of an existing model. A complete data pipeline from ingestion to serving takes 6-12 weeks depending on data complexity.
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
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