Steamship

Steamship is a platform to build, host, and deploy AI agents with LLMs. Learn Steamship features, pricing, and use cases for fast AI tool deployment.

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Steamship is a cloud-based platform that allows developers and startups to quickly build, deploy, and scale AI agents powered by large language models (LLMs). Focused on simplifying backend infrastructure, Steamship provides tools and templates to help users create APIs, chatbots, and custom AI tools without the need to manage servers, containers, or DevOps pipelines.

Ideal for rapid prototyping and product launches, Steamship supports popular LLMs like OpenAI’s GPT models and offers built-in support for memory, vector search, and plugin frameworks. The platform’s goal is to be the “Heroku for LLM agents”—abstracting the complexity of AI infrastructure and allowing developers to focus entirely on logic and user experience.

Whether you’re building a personal AI assistant, a SaaS AI feature, or a custom chatbot, Steamship provides the speed and simplicity to get your idea live in minutes.


Features

1. One-Line Deployment
Deploy your AI agent with a single command. Steamship handles all infrastructure, including hosting, scaling, and API endpoints.

2. Built-In Memory Management
Add memory to your agents with simple decorators, enabling conversation history, session tracking, and long-term memory.

3. Plugin Architecture
Build modular AI workflows by chaining together tools like LLMs, embeddings, vector stores, and external APIs using Steamship’s plugin framework.

4. Vector Search (Embeddings)
Easily integrate embeddings-based retrieval with support for semantic search across documents, user history, or product catalogs.

5. Auto API Creation
Every agent is automatically converted into an API with REST endpoints—no manual API development required.

6. Templates and Starters
Steamship offers pre-built templates for common use cases like chatbots, customer service agents, and note-taking assistants to accelerate development.

7. Logging and Debugging Tools
Track prompt inputs, outputs, plugin usage, and memory state through an intuitive logging system.

8. Hosted Notebooks and Scripts
Develop agents directly in the browser using interactive code notebooks, similar to Jupyter but hosted in the cloud.


How It Works

Step 1: Create a Project
Start with one of Steamship’s templates or build from scratch using Python SDKs. Projects are containerized automatically.

Step 2: Define Your Agent Logic
Add LLM calls, memory structures, plugin chains, or external API integrations using decorators and functions.

Step 3: Deploy with One Command
Push your project to Steamship using the CLI or browser interface. The system automatically exposes an API endpoint for immediate testing.

Step 4: Connect Front-End or External Tools
Integrate with web apps, Slack, Discord, or other UIs via the auto-generated REST API.

Step 5: Scale and Monitor
Steamship handles uptime, scaling, and request management. Developers can view logs, update code, and manage versions from a central dashboard.

This developer-first workflow dramatically reduces time-to-market for AI products.


Use Cases

1. Custom AI Chatbots
Rapidly build and deploy chatbots with memory and personalization for websites, Slack, or customer support systems.

2. SaaS Feature Prototyping
Embed AI features like summarization, Q&A, or personalized assistants into SaaS products quickly and securely.

3. AI Side Projects and MVPs
Hackathon teams and indie developers can use Steamship to spin up demos and MVPs in minutes without setting up servers.

4. Knowledge Retrieval Agents
Combine embeddings and search tools to build agents that search through documents, manuals, or support databases.

5. Personal Productivity Tools
Build AI tools for task management, journaling, meeting notes, and personal knowledge bases with minimal backend effort.

6. AI Developer Toolkits
Technical creators and educators can use Steamship to build custom GPT-powered tools for niche workflows or educational purposes.


Pricing

As of June 2025, Steamship offers flexible pricing plans suitable for individuals, startups, and enterprise teams:

Free Tier:

  • 500 API calls/month

  • One active agent

  • Access to all templates and plugins

  • Community support

Pro Plan – $29/month:

  • 10,000 API calls/month

  • Priority execution

  • Up to 5 agents

  • Premium support

Scale Plan – $99/month:

  • 100,000 API calls/month

  • Multiple concurrent agents

  • Vector search and embeddings support

  • Priority build and deploy pipelines

Enterprise Plan – Custom Pricing:

  • Unlimited usage

  • Dedicated infrastructure (on request)

  • Advanced monitoring and security

  • Team collaboration features

Users can sign up and deploy their first agent within minutes at https://www.steamship.com.


Strengths

  • Developer-Centric: Designed specifically for developers with strong Python support and intuitive SDKs.

  • Rapid Deployment: Drastically reduces infrastructure setup time with one-command deployment and instant API generation.

  • Integrated Tools: Combines LLMs, memory, embeddings, and plugin systems in a unified environment.

  • Scalable Infrastructure: Built to grow with your project—from prototype to production.

  • Template Library: Helps users start quickly with pre-built workflows and agents.

  • Free Plan Available: Offers enough credits to test and deploy small projects without upfront costs.


Drawbacks

  • Python Dependency: Currently optimized for Python; developers using other languages may find limited support.

  • Learning Curve for Non-Technical Users: Geared towards developers; lacks no-code or low-code interfaces for non-programmers.

  • Limited UI Components: Front-end integrations (e.g., UI kits or embeddable widgets) must be built externally.

  • Agent Memory is Session-Based by Default: Long-term or multi-user memory requires manual configuration or plugin setup.


Comparison with Other Tools

Steamship vs. LangChain:
LangChain offers an open-source framework for chaining LLMs and tools, but requires custom deployment. Steamship handles infrastructure and hosting out of the box.

Steamship vs. Vercel or Heroku:
Vercel/Heroku provide app hosting but not AI-specific tools. Steamship focuses exclusively on AI agent deployment with built-in memory and plugins.

Steamship vs. OpenAI Functions/Assistants API:
OpenAI offers building blocks but no hosting. Steamship provides backend infrastructure, agent orchestration, and persistent memory.

Steamship vs. Replit:
Replit is a cloud IDE for code hosting. Steamship is tailored for LLM agents and automates deployment and scaling for AI applications.


Customer Reviews and Testimonials

Early users and developers have praised Steamship for its simplicity and effectiveness:

  • “We went from idea to deployed agent in under 30 minutes. Steamship made it ridiculously easy.” – Indie Hacker

  • “It’s like Heroku for AI agents. We love the auto-API and memory tools.” – CTO, AI SaaS Startup

  • “Built my own GPT-powered research assistant over the weekend. Would have taken weeks otherwise.” – AI Developer

Steamship has been featured on Product Hunt and is gaining attention in the AI builder community for its focus on rapid prototyping.


Conclusion

Steamship is a developer-focused platform that dramatically simplifies the process of building, deploying, and scaling AI agents powered by LLMs. By removing the infrastructure and DevOps barrier, it enables faster innovation and allows developers to focus entirely on building great AI experiences.

If you’re building an AI tool, agent, or chatbot and want to get it online quickly with memory, search, and APIs included—Steamship is one of the fastest ways to go from idea to deployment.

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