Storage built for AI that’s already in production
You’re probably not training a foundation model from scratch. You’re deploying one, serving it, and keeping it fast. Summit’s storage is built for that reality: quick retrieval, model artifacts you can version and recall, and vector workloads that hold up at scale.
Recovery
What AI storage looks like now
The bottleneck moved. It used to be raw training data. Now it’s fast retrieval at inference time and keeping your model artifacts organized and recoverable.
You’re already in production
Most teams fine-tune and deploy pre-trained models. The storage question is fast recall of what you already have, not raw training capacity.
GPUs starve on slow I/O
When I/O can’t keep up, your GPUs sit idle and latency climbs. Storage becomes the limiting factor before compute ever does.
Vectors need a new pattern
Embedding stores and vector search need access patterns that traditional block storage was never designed to handle well.
Artifacts need versions
Weights and checkpoints need versions you can recall in seconds. Fine-tuned variants pile up fast, and cold archives won’t cut it.
76% of enterprise AI use cases are now purchased, meaning teams deploy pre-trained models rather than build from scratch, up from 47% in 2024. As that shifts, so does the storage problem: away from raw training data, toward fast inference-time retrieval and model artifact management. Public cloud object costs compound quietly as artifacts and inference logs stack up.
Summit storage options for AI workloads
Block for speed, object for scale, colocation for control, and backup with DR to keep it all protected. Run any mix from one provider.
Object storage
Block storage
Colocation for storage-heavy builds
Backup and DR for AI data
Building an AI app on Summit’s infrastructure? Your storage should live here too
If you’re running inference on Summit GPU or Mac hosting, keeping storage in the same data center cuts the cross-network hop for model loading. S3-compatible object storage plugs into LangChain, LlamaIndex, and Hugging Face straight out of the box.
Recommended storage tiers for deployed AI
Match each part of your stack to the right tier, so hot paths stay fast and everything else stays affordable.
SAN / NVMe
Active inference. Embedding lookups, vector search, and real-time model serving where every millisecond counts.
Object storage
Current model versions, your RAG knowledge base, and recent inference cache. Fast enough to serve, priced to sit.
Managed backup
Prior model versions, training datasets, and audit logs. Versioned, protected, and recoverable the moment you need them.
One provider for the whole storage stack
Block, object, colocation, backup, and DR under one roof, on infrastructure your models can run on and recover from.
Block and object from one provider
Low-latency block for serving and S3-compatible object for artifacts, from one provider on one bill.
DR to a separate data center
DRaaS replicates your AI data to another Summit site, so a single-site failure doesn’t take your models down with it.
Redundant infrastructure to build on
Build and serve your models on infrastructure with redundant power and network underneath every rack.
Pricing that doesn’t punish scale
Costs stay predictable as artifacts and inference logs grow, and reading your own model weights doesn’t trigger an egress fee.
U.S.-based support and Remote Hands
Real engineers in U.S. data centers, on call 24/7, with spare hardware on site for rapid repairs and replacements when a component fails.
22 data centers, 6 continents
Place data where your users and compliance rules need it, with room to grow into petabyte-scale builds.
How it works
Talk it through
Tell us what you’re running and where the storage pinch is.
Map your tiers
We recommend hot, warm, and cold placement for your workloads.
Provision and migrate
We stand up block, object, or colo and move your data over.
Manage and protect
Backups, DR, and monitoring run quietly in the background.
Map your AI storage with an engineer
Tell us what you’re serving and we’ll recommend the right mix of block, object, colocation, and DR.
Public cloud storage vs. Summit
| Public cloud, DIY | Summit | |
|---|---|---|
| Block and object storage | Separate products, separate bills | Both, from one provider |
| S3-compatible object API | Yes | Yes |
| Reading your own model weights | Egress charged per GB | Included on internal reads |
| DR to a separate data center | You architect and run it | DRaaS, fully managed |
| Bring your own NAS or SAN | Not available | Colocation in Summit data centers |
| Managed ops and Remote Hands | Self-service | U.S.-based, available 24/7 |
| Uptime SLAs | Vary by tier | 100% power and network |
What teams store with us
From RAG knowledge bases to fine-tuning datasets, here’s what tends to live on Summit storage.
We were having trouble with our apps not sending data. We called up Summit, and they spent 2 hours talking us through it. It was a simple command line change and they fixed it. AWS won’t do that.
AI storage FAQ
Yes. Low-latency block storage for serving and vector lookups, and S3-compatible object storage for model artifacts, datasets, and logs. You can run both from the same provider on the same bill.
Yes. It uses the S3 API, so pipelines and tools built around LangChain, LlamaIndex, and Hugging Face keep working as they are.
Yes. With DRaaS, we replicate model checkpoints, weights, and datasets to a separate Summit data center, so a single-site failure doesn’t take your AI stack down with it.
Managed backup means we handle your backups and restore data when you ask. DRaaS is broader: replication to a second site, regular testing, monitoring, documented runbooks, and defined recovery targets.
Yes. Colocate your existing storage hardware in a Summit data center for on-premises latency with managed power, network, and U.S.-based Remote Hands.
Yes. Storage runs in Summit data centers, where support and Remote Hands are U.S.-based and available 24/7. We keep spare hardware on site, so repairs and replacements happen fast when a component fails.
You’re not charged an egress fee every time you read your own model weights, so costs stay predictable as artifacts and inference logs grow.
Talk to a storage engineer
Tell us what you’re running and we’ll come back with a storage plan tuned to it.
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