Cloud Storage for the Agentic Age
An S3-compatible bucket your agents connect to over MCP.
- Instant branches of production they can safely work on
- Zero-egress reads by default
- Per-agent access control
- A signed record of everything they touch
Real data
Zero production risk
- Snapshot production at any moment, instantly and at zero copy
- Agents branch it in seconds, copy-on-write, and work in isolation
- Train, test and experiment freely while production stays untouched
- Promote what works, or roll back to any point in time
Free egress by default
Tune for speed when you need it
Read-heavy agents stay on a zero-egress mix, so automated reads never run up a bill. Latency-sensitive ones optimize for speed across the providers closest to their workloads, set by their credentials.
Each credential sets its own point on the dial. No surprise read bills, no one-size-fits-all trade-off.
See pricingAgents get accessYou keep control
Every file your AI touchedSigned and on record
- Built into the storage layer and always on
- A complete, immutable record, cryptographically signed
- Structured JSON, searchable and export-ready
- Ready when the audit question arrives
Multi-Cloud Architecture
How a single file becomes private, provider-agnostic, and always available.
Your data is encrypted before it ever leaves your device.
Files are encrypted before they are split and erasure-coded. Each shard alone is meaningless but fully recoverable.
Shards are stored across your chosen jurisdictions, supporting compliance and resilience.
Files are decrypted only for verified users. Every reconstruction is secure and auditable.
Keep your S3 toolsChange one line
IronShard is S3-compatible: your existing SDKs, scripts, and tools work by swapping a single endpoint.
Already have data? Import it over the S3 API.
import boto3 # The only change: swap the endpoint URL s3 = boto3.client( "s3", endpoint_url="https://s3.amazonaws.com", ) # Everything else stays the same s3.download_file("my-bucket", "datasets/dataset.parquet", "local.parquet")
Works with every tool you already use
Command line
- AWS CLI
- rclone
- s3fs
Python and ML
- boto3
- PyTorch
- LangChain
Pipelines
- Airflow
- Prefect
- Apache Spark
Infra and MLOps
- Terraform
- DVC
- MLflow
Start over MCP or by hand
Both are on the free tier. No credit card.
Create an account and your first bucket
Console →Connect your agent
Add IronShard to your agent and let it provision and run storage for your workload.
- 1Add the production MCP serverAdd it to your MCP client (Claude, Cursor, VS Code or any other) and sign in with OAuth:
https://mcp.ironshard.ai/mcp - 2PromptOnce connected, prompt your agent to work with your storage: read, write, branch, snapshot, and more.
Use S3 credentials
Swap one endpoint in your code. Your SDK, your tools, and your API calls stay the same.
- 1Get your S3 credentials Create S3 credentials for your bucket in the console.
- 2Swap the endpointPoint any S3-compatible SDK at IronShard (AWS SDK, boto3, rclone):
s3 = boto3.client( "s3", endpoint_url="https://s3.ironshard.ai", )
Autonomous agent sandbox
Point your agent at the public agent MCP server. It creates a bucket with no account or OAuth and evaluates it on its own.
https://agents-mcp.ironshard.ai/mcp