Why we use Python
Python is our language of choice for AI/ML workloads, data pipelines, and automation. Its ecosystem for machine learning (PyTorch, TensorFlow, scikit-learn) and LLM orchestration (LangChain, LlamaIndex) is unmatched.
Key benefits
- Dominant AI/ML ecosystem — PyTorch, TensorFlow, Hugging Face
- LangChain and LlamaIndex for LLM orchestration
- FastAPI delivers performance comparable to Node.js/Go for APIs
- Rapid prototyping — 3-5x faster initial development for data-heavy features
- Strong data science libraries (pandas, numpy, scipy)
- Excellent for integrating with research and academic ML models
Python — Frequently Asked Questions
Python vs Node.js for AI applications?
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Python for anything involving ML models, embeddings, or data processing. The AI ecosystem is built in Python. Node.js for the user-facing API layer and real-time features.
Is Python fast enough for production APIs?
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Yes — FastAPI with async/await handles high-concurrency workloads efficiently. For compute-heavy tasks, we use worker pools and offload to dedicated ML serving infrastructure.
How do you deploy Python AI services?
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Containerized with Docker, orchestrated with Kubernetes, served via FastAPI or dedicated ML serving (TorchServe, Triton). GPU instances for inference where needed.
Ready to build with Python?
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