Hello Model

Open-source / self-hosted for machine learning

The service to use for each part of an ML system on Open-source / self-hosted, and why it helps your model.

ComponentOpen-source / self-hosted serviceWhy it helps your model
Data storageLocal disk / MinIO (S3-compatible) / PostgreSQL
  • No cloud bill, and data never leaves your hardware
  • MinIO speaks the S3 API, so code moves to the cloud unchanged later
  • PostgreSQL is a solid home for structured data
NotebooksJupyterLab or VS Code
  • Free, and runs on any laptop or server
  • Full control over packages and extensions
  • Works offline
GPU trainingA local NVIDIA GPU, or rented GPUs (RunPod, Lambda, Vast.ai)
  • An owned GPU has no hourly cost after purchase
  • GPU rental marketplaces are often cheaper than the big clouds
  • Choose the exact GPU model you need
ML platform & registryMLflow (tracking + model registry)
  • Open-source, vendor-neutral experiment tracking and registry
  • The same MLflow API works locally and on Databricks or Azure ML
  • Easy to self-host with Docker
Serverless servingDocker container on a small VM (or Fly.io / Render)
  • Simple and cheap for low or steady traffic
  • A container runs the same anywhere
  • Predictable flat monthly price
GPU servingTriton Inference Server / vLLM / BentoML on a GPU box
  • Request batching squeezes the most throughput out of a GPU
  • vLLM is a leading engine for serving LLMs fast
  • No per-request fees
Batch predictionsCron or Prefect/Airflow job running a Python script
  • Just a scheduled Python script, easy to understand
  • No vendor lock-in
  • Runs on servers you already have
PipelinesPrefect, Dagster or Airflow
  • Open source with large communities
  • Workflows are plain Python
  • Run locally or move to any cloud later
Vector databaseQdrant, Chroma or PostgreSQL + pgvector
  • Free and open source
  • Chroma is great for prototypes; Qdrant scales to production
  • pgvector reuses an existing PostgreSQL database
LLM accessOllama or vLLM serving an open-weights model (Llama, Qwen, Mistral)
  • Prompts and documents never leave your network
  • No per-token charges, only hardware costs
  • Pick, and even fine-tune, any open-weights model
MonitoringPrometheus + Grafana + Evidently AI
  • Industry-standard open-source monitoring
  • Evidently produces ready-made drift and quality reports
  • Build a dashboard for any metric
Privacy controlsEverything stays on your hardware; encrypt disks and restrict network access
  • Full control over where data lives
  • Works in air-gapped environments
  • You set the encryption and access rules (and own the security work)