Storage for the
New Cloud
Vendor-neutral object storage built for AI-native, GPU-first systems — across clouds and neoclouds.
The New Cloud
Built for the Infrastructure Era After Legacy Cloud.
Legacy cloud wasn't built for AI-native, GPU-first workloads. Infrastructure assumptions from 2006 don't hold when compute is distributed, bursty, and multi-cloud.
Pain Points
- Infrastructure shaped by 2006-era assumptions
- Storage tightly coupled to single-cloud ecosystems
- Region-bound architectures limiting flexibility
- Enterprise-first features slowing innovation
- Vendor lock-in reducing strategic leverage
- Compute mobility blocked by data gravity
What Tigris Does
- Vendor-neutral storage layer
- Multi-cloud by design
- Separates storage from compute
- Avoids legacy constraints
- Aligns with AI-native infrastructure patterns
“The Old Cloud optimized for stability. The New Cloud demands mobility.”
Unstructured Data at Scale
Built for AI Data Gravity.
Pain Points
- Petabytes of training data
- Billions of small objects
- Checkpoints and model artifacts
- Generated outputs (images/video/audio)
- Continuous ingestion from multiple pipelines
- Data fragmented across clouds due to GPU availability
What Tigris Does
- Handles massive unstructured object workloads
- Scales to billions of objects efficiently
- Simplifies artifact and checkpoint management
- Unified layer across clouds
- Reduces operational overhead
“Traditional storage assumes predictable growth. AI-native systems grow explosively.”
Performance
Keep GPUs Fed. Deliver Globally.
AI workloads are parallel and bursty. Object storage must hydrate data locally fast enough to keep GPUs saturated. Inference requires global low-latency access.
Pain Points
- GPU clusters stalled by storage bottlenecks
- Parallel workers overwhelming object storage
- Data loader inefficiencies wasting expensive GPUs
- Inference latency degrading UX
- Media served globally from region-bound storage
- Performance inconsistency across clouds
What Tigris Does
- High parallel throughput for GPU clusters
- Optimized for distributed training patterns
- Globally distributed object access
- Consistent performance across clouds
- Keeps GPUs saturated and inference fast
“The Old Cloud optimized for web traffic. The New Cloud must optimize for bursty, parallel hydration into GPU clusters.”
Cost Control
Optionality Is the Real Cost Optimization.
In AI-native systems, cost isn't just storage price — it's compute mobility, egress, duplication, and lost leverage.
Pain Points
- Egress costs exploding
- Training data duplication
- Dev/test model artifact bloat
- Paying premium GPU rates because data is stuck
What Tigris Does
- Reduces dependence on egress-heavy architectures
- Cross-cloud portability without duplication
- Preserves ability to move workloads to cheaper GPU environments
“If data isn't portable, your GPU strategy isn't either.”
Build for the New Cloud.
Storage designed for AI-native generative media systems — across clouds.