For Startups and AI Builders

Ship your product, not your storage layer

IronShard is S3-compatible object storage built for AI agents. Point your agent at it and it provisions its own bucket, or swap one endpoint in your existing stack. Governance is built in, so you move fast without wondering what your agents just did.

Start in minutes

Building with an agent? Connect over MCP and ask it:

Connect to IronShard over MCP and provision an isolated sandbox bucket for AI experimentation, with zero-egress reads and a signed audit trail I can verify.

The agent creates its own bucket through the public Agent MCP server: no account, no OAuth, no human approval. Endpoint, tools, and examples are in the Agent Buckets docs.

Prefer your own tools? IronShard speaks S3. 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.

Don't code? Your agent does

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. Zero-copy branches give every run its own isolated workspace; discard failures at zero cost, promote what works. See Branch
• A live copy of production to build against, always current, never touching the real thing. See Mirror
• Zero 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, and an architecture engineered to meet GDPR and the EU AI Act. When the security questionnaire arrives, the evidence already exists.