Why it matters
- Eliminates LLM app infrastructure boilerplate — persistent memory, vector storage, and multi-tenancy come built-in.
- Serverless hosting means LLM apps scale automatically without provisioning or managing servers.
- Multi-tenant workspaces allow building SaaS AI applications where each user has isolated state.
- Python-first SDK design fits naturally into existing ML and AI development workflows.
Key capabilities
- Serverless Python hosting: Deploy AI apps as HTTP endpoints without server management.
- Persistent memory: Built-in conversation history and state that persists across sessions.
- Vector storage: Built-in embedding search for RAG applications — no separate vector database needed.
- File storage: Store documents, images, and other files with a simple API.
- Streaming responses: First-class support for streaming LLM output to clients.
- Multi-tenancy: Workspace isolation for SaaS apps where each user has separate data.
- LangChain integration: Use Steamship's storage and hosting with LangChain chains and agents.
- API authentication: Built-in token-based auth for securing your deployed AI endpoints.
Technical notes
- SDK: Python;
pip install steamship - GitHub: github.com/steamship-core (client SDK open source)
- Hosting: Steamship managed cloud; serverless Python execution
- Memory: Key-value and vector storage included
- Pricing: Free tier; pay-as-you-scale for compute and storage
- Founded: 2021 by Ted Benson and Evan Conrad; YC W22
Ideal for
- Developers building multi-user LLM applications (chatbots, AI assistants) who don't want to manage state/memory infrastructure.
- Startups prototyping AI products who need serverless hosting + built-in state management quickly.
- Teams building SaaS AI products where each customer needs isolated data and conversation history.
Not ideal for
- GPU-intensive model training or custom inference — RunPod or Modal are better for compute-heavy tasks.
- Applications needing full infrastructure control or on-premise deployment.
- Simple single-user scripts — the multi-tenancy overhead isn't worth it for personal tools.