Python
Python is TantraDev's default for data pipelines, ML inference, and back-office tooling. The library surface (Pandas, NumPy, scikit-learn, FastAPI, PyTorch) is wide enough that 'we'll prototype in Python' rarely turns into 'we have to rewrite it' — and where it does, the contract layer is small and explicit. We pair Python with strict type hints and Pydantic on the API edge.
Concepts that travel with this one.
Architecture rarely lives in isolation — these are the terms that come up in the same conversation.
Node.js
Node.js is the V8-based JavaScript runtime for server workloads — non-blocking I/O, single-threaded event loop, and a package ecosystem that covers nearly every adapter you would need to talk to a payment provider, a queue, or a database. TantraDev uses Node (with TypeScript) for API services where I/O dominates compute and team velocity matters as much as raw throughput.
TensorFlow
TensorFlow is Google's ML framework, strongest for production-grade model serving (TensorFlow Serving), mobile inference (TFLite), and large-scale training pipelines. TantraDev uses TensorFlow when the model leaves the notebook — when something has to be quantised for an Android phone, served behind a latency SLO, or trained on a multi-GPU cluster. For experimentation we are equally at home in PyTorch.
Building a system where Python is the load-bearing decision?
30 minutes on the phone, one page in your inbox — what to build, what to skip, what it will cost. You keep the audit even if we are not the right fit.