Object storage for AI companies
Storage that scales.Costs that don't.
Checkpoints overwrite. Restores pull terabytes. The surprise is rarely the capacity rate. Storivo is usage-based object storage for AI companies that train or serve models.
Objects can stack. The line we care about is whether overwrite and restore stay on a readable bill.
01 / The bill
The result we are after is a bill that still holds after the next restore.
There is no customer wall yet. The first useful proof is the workload itself: what you store, how often it lands, and where the invoice jumps.
Storivo is being shaped with a few design partners at companies that train or serve models. The engagement starts with that bill, not a platform tour.
02
Capacity follows the run
Grow and shrink with the dataset. First version is instantly scalable capacity, not a high-throughput training disk.
03
The bill is restore and churn
Cheap capacity is already sold. The work is a readable invoice when checkpoints overwrite and restores cross clouds.
04
Built for the people who feel it
Infrastructure, ML platform, and finance leads. Later markets wait until those first workloads are understood.
05 / What moves
Three objects that refuse to stay small.
01
Training sets
The pile that grows every run. Cheap capacity first, not a training filesystem.
02
Checkpoints
Versions land in bursts. Restore patterns are the bill, not the file count.
03
Model artifacts
Weights, exports, and the leftovers of serving. Keep them addressable as the lab moves.
The line
Storage that scales. Costs that don't.
Give AI companies storage that scales with their data and stays price-predictable, so training and serving are not gated by a cloud invoice.
06 / Who
First conversations are with labs that already feel the transfer line.
Beachhead: AI companies that train or serve models. Tech companies with bursty workloads, financial institutions, and individual creators come later.
- InfrastructureOwns the object path the training loop already speaks.
- ML platformFeels checkpoint cadence and restore time as the cluster scales.
- FinanceSees storage and transfer as a line that refuses to sit still.
We are not claiming a cheaper clone of R2, Wasabi, or B2. Those products already sell S3-compatible storage with simpler or zero egress. The work is whether a bill can stay readable when AI objects overwrite and restore, without becoming another me-too bucket.
07 / Method
Founder-led, a few partners, then a product.
01
Send the workload
Dataset size, checkpoint cadence, restore pattern, and where the current bill hurts.
02
Sit in the numbers
A short working conversation. No marketplace listing, no self-serve signup, no docs-led funnel yet.
03
Decide if it counts
The activation goal is still open. A partner engagement counts when something real is stored or moved.
08 / Access
Request access.
If you train or serve models and storage is starting to gate the work, write us. This is a controlled launch, not an open signup.
Prefer mail? admin@novaaetus.com