Overview
Tigris is a globally distributed S3-compatible object storage service that allows you to store and access any amount of data for a wide range of use cases. Tigris automatically and intelligently distributes your data close to the users, and removes the need for you to worry about the complexities of data replication, and caching.
How to use Tigris
Most teams adopt Tigris by configuring existing AWS S3 or Google Cloud Storage SDKs with Tigris access keys and a Tigris endpoint. In many cases, applications can switch to Tigris with no code changes beyond configuration.
Tigris also offers native Storage SDKs that provide direct access to Tigris-specific features like client uploads and bucket forks and snapshots. For AI-assisted development, the Tigris MCP server lets AI coding agents interact with your Tigris buckets directly.
What Tigris stores
Tigris stores objects—such as application assets, model weights, media files, and ML artifacts—that are consumed by databases, analytics systems, vector search engines, and AI pipelines. Tigris focuses on durable object storage and does not currently provide databases or query engines. However, Tigris can replace a traditional CDN for many use cases due to its automatic global replication.
When to choose Tigris
You're building AI and data-intensive workloads that span clouds or providers. If you train on GPU neoclouds, run inference across multiple providers, or want to avoid lock-in to a single cloud, Tigris gives you a single, globally replicated object store. Data is stored and replicated close to where it's accessed, reducing latency and eliminating egress fees when data moves between clouds.
You need a shared data layer for AI systems. Tigris is commonly used to store model weights, checkpoints, embedding files, feature data stored as objects, and training datasets that are consumed by external training frameworks, inference services, vector databases, and analytics systems. Because Tigris does not charge egress fees, large datasets can be reused freely across environments.
You want isolated environments for agents and experiments. Bucket forks let AI agents, experiments, and evaluation runs work against isolated copies of the same underlying data without collisions. Even very large datasets can be forked instantly, making it practical to run parallel experiments at scale.
You care about predictable costs for data-heavy workloads. With no egress fees, Tigris lets you move and reuse data without surprise bills. This is especially valuable for AI training, batch processing, analytics, and media workloads where data movement dominates cost.
You're migrating from another S3-compatible provider. Shadow buckets keep your existing storage and Tigris synchronized, enabling zero-downtime migration. Applications can switch over gradually, often with only configuration changes.
Typical use cases include:
- Storage for machine learning models and datasets
- Storage for real-time applications and AI-powered services
- Web content and media (images, video, static assets)
- Storage for IoT applications and globally distributed data ingestion
- Data analytics, big data, and batch processing
- Backups and archives
What's next
Ready to dig deeper? Explore the full set of Tigris features — including global distribution, snapshots and forks, zero egress fees, and more.