Tools & frameworks

You need sub-millisecond access to small hot data — caches, counters, queues, rate limits, session state.

Your data is document-shaped, your schema evolves, or you want vector search and operational storage in one place.

Any deep learning work — training from scratch, fine-tuning, research, or deploying custom models.

Tabular data, prototyping, feature engineering pipelines, and almost any classical ML baseline.

You're maintaining an existing TF codebase, targeting mobile/edge via TFLite, or serving models through TF Serving at scale.

Any supervised learning problem on tabular data — especially classification, regression, and ranking.

You need reproducible environments for training, serving, or local development — especially anything involving CUDA, Python versions, or system libraries.

You run multiple services at nontrivial scale and need rolling deploys, autoscaling, and a uniform way to manage them.

You're exploring a third-party API, debugging your own service, or sharing request collections with teammates who don't live in a terminal.

Validating or serializing structured data in Python — API payloads, configuration, LLM outputs, anything with a schema.

You have a well-defined task with examples and want the framework to automatically search over prompts, few-shot demos, and even fine-tunes.

Loading, fine-tuning, or running any pretrained transformer model in Python.

You need a quick LLM application scaffold with ready-made integrations for vector DBs, document loaders, and LLM providers.

You're building an agent with branching logic, retries, checkpoints, or human-in-the-loop steps and want explicit control over the flow.

You're building a RAG system over a corpus of documents and want ready-made loaders, indexers, and query engines.

Debugging, evaluating, and monitoring LLM chains or agents in dev and production — especially if you're already on LangChain/LangGraph.

You need experiment tracking and a model registry without buying a full MLops platform, and you want something self-hostable.

You have a RAG pipeline and need quantitative metrics beyond eyeballing outputs, especially for regression testing and comparison.

You need a typed, async Python HTTP service — especially one that serves ML models, proxies LLM calls, or exposes a RAG pipeline.

You want to run an open LLM on your laptop or a small server with zero setup — demos, prototypes, offline work.

You're self-hosting an open-weights LLM and care about throughput, latency, and GPU utilization.

You need durable, high-throughput event streaming with multiple independent consumers replaying history.

Prototyping, local development, or small production apps where a lightweight embedded store is enough.

You already run Postgres and your vector workload is modest to medium — tens of millions of vectors, single-digit ms queries.

You need a production vector store and don't want to operate one yourself. Especially good when you need serverless scaling.

You want an open-source, self-hostable vector store with hybrid (vector + keyword) search out of the box.

You want the fastest path from a Python function to a clickable demo, especially for ML models with image, audio, or text I/O.

You want a quick internal tool or demo around a model and don't want to touch frontend code.

You're running Spark-scale ETL, training models on terabytes, and want notebook + job + MLflow in one place.

You want a no-ops analytics warehouse with strong governance, concurrent BI workloads, and simple SQL semantics.