Steamship

Deploy AI agents to production without managing servers, containers, or infrastructure scaling. Automatically handles resource allocation, scaling, and uptime management through a serverless cloud platform.

Connect to multiple large language model providers (OpenAI, Cohere, Llama) through unified abstractions, eliminating the need to write provider-specific API code.

Build and test AI agents on a generous free tier without requiring payment, enabling risk-free prototyping and learning.

Integrate vector search and semantic similarity capabilities into agents through built-in vector database connections, enabling RAG and memory systems without manual database setup.

Manage file uploads, storage, and processing within agents without building custom file infrastructure. Handles document parsing, storage, and retrieval for agent workflows.

Build agents using pre-built abstractions and patterns that handle orchestration, state management, and control flow without writing boilerplate infrastructure code.

Automatically capture, store, and visualize agent execution logs, errors, and performance metrics through built-in observability tools designed for AI workflows.

Automatically expose deployed agents as HTTP API endpoints with request/response handling, authentication, and rate limiting built-in.

Automatically persist and retrieve agent state across invocations, enabling agents to maintain context and memory without manual database management.

Start with pre-configured agent templates for common use cases (chatbots, search agents, etc.) that can be customized and deployed immediately.

Extend agent capabilities through pre-built plugins for common integrations (APIs, tools, services), though with a smaller ecosystem than alternatives.