All industries
Industry solution
Ahura OS for Data Science & Advanced Analytics
- Built for
- Data Scientist / ML Engineer
- Works through
- Claude Code or Cursor via the Ahura MCP Gateway
The challenge
What gets in the way.
- Training-serving skew when deploying ML models to production environments.
- High costs of always-on vector databases and GPU instances for batch inference.
- Complex glue code to connect local IDEs to remote cloud compute.
The Ahura OS solution
How it works.
Data scientists connect their preferred IDE directly to Ahura OS via MCP. Using the Pickled Pipeline pattern, they train PyTorch models on ephemeral GPUs and deploy sub-second CPU inference on MicroVMs with zero training-serving skew.
Key primitives
- Pickled Pipeline pattern
- vm.run (MicroVM inference)
- gpu.run (ephemeral CUDA)
- Ahura MCP Gateway
Case studies for Data Science
- Safe execution of agent-written codeLet an agent run what it wrote without risk to anything else.
- Entity resolutionWork out which records are the same thing.
- Parameter sweepsFan out thousands of simulation runs and gather results.
Services behind it
- Ahura GPUScale-to-zero acceleration for inference and training.
- Ahura VMA sandbox for every task.
- Ahura AICheap inference for everyday turns.
Works with the MCP gateway for developers, and private partitions. See pricing and security.