For Startups and AI Builders

Ship your product, not your storage layer

IronShard is S3-compatible object storage built for AI agents. Your agent sets it up and runs it, and governance is built in, so you move fast without wondering what your agents just did.

Two ways to start

Both are on the free tier. No credit card.

1. Create an account and connect your agent

For the product you are building.

Create an account and your first bucket in the console.
Add the production MCP server to your MCP client (Claude, Cursor, VS Code, or any other) and sign in with OAuth:
```
https://mcp.ironshard.ai/mcp
```
Prompt your agent to work with your storage: read, write, branch, snapshot, and more. It works inside your account, under your policies.

Prefer to do it by hand? IronShard speaks S3. Create S3 credentials for your bucket in the console and change one line:

s3 = boto3.client("s3", endpoint_url="https://s3.ironshard.ai")

Everything else in your stack stays the same: AWS CLI, boto3, s3fs, rclone, Terraform, Airflow, LangChain, MLflow, PyTorch, HuggingFace.

2. Let an autonomous agent create its own bucket

For evaluating IronShard with no account at all.

Point your agent at the public agent MCP server:

https://agents-mcp.ironshard.ai/mcp

It creates a bucket with no account, no OAuth, and no human approval, and evaluates it on its own. A prompt to try:

Connect to IronShard over MCP and provision an isolated sandbox bucket for AI experimentation. Upload a test object, read it back, and tell me what you found.

Sandbox buckets run under policy-defined limits and cannot reach buckets in an account. Endpoint and examples are in the Agent Buckets docs.

Tell Claude, Cursor, or any MCP-capable agent to set up storage for the thing it is building. It creates the bucket, scopes its own access, and hands you the keys. The risk management you would normally need an expert for is already in the design.

Everything an AI build needs

Agents provision and use storage themselves over MCP, with per-agent credentials scoped down to a single key. See AI Agent Storage
Experiment on real data, safely. Instant, zero-copy branches of production give every run its own isolated workspace; discard failures at zero cost, promote what works. See Branch
Free egress by default for read-heavy agents; each credential sets its own point on a cost-latency dial.
A signed record of every action, ready when a customer or auditor asks what your AI did. See Log

Enterprise-ready before your first enterprise deal

The controls enterprise customers ask about are defaults here, not a retrofit: dataset lineage and versioning, an immutable, cryptographically signed audit trail, data residency controls, GDPR compliance across the whole service, and an architecture engineered to meet the EU AI Act. When the security questionnaire arrives, the evidence already exists.