Cloud capacity, one control plane.
Sinewgrid does not build or own data centers or GPUs. We assemble capacity from hyperscale and GPU clouds, and add the architecture, scheduling and data layer that make it behave like one training system.
One system over many clouds.
A training run is only as reliable as its weakest node, and a company that depends on one cloud depends on that cloud’s worst day. Sinewgrid’s platform is built on the opposite idea: treat capacity from many providers as one pool, and put the intelligence in the layer that schedules it. The control plane knows what each job needs, whether steady training with fast interconnect, interruptible simulation or bursty fine-tuning after an experiment, and places it on the pool that fits, reserved, short-term, on-demand or spot. When a node or a pool fails, it restores the job from a portable checkpoint instead of starting over, and it reports goodput, the share of paid time that becomes training progress, so cost is visible and not assumed. Beside it sits a model-access layer that routes calls to ChatGPT, Claude, Gemini and Grok with budgets, caching and fallbacks. You can use one layer or all of them, run it in your own cloud account, and leave without rewriting your jobs, because we keep job definitions portable on purpose. Cloud and model providers named here are design references, and none has endorsed or partnered with Sinewgrid unless an agreement is listed on this site.
Models
Sinewgrid Cell and Sinewgrid Robotics, plus customer models trained on the same control plane.
In developmentControl plane
Slurm and Kubernetes scheduling across clouds, checkpoint management and portability, automatic failover between capacity pools, experiment tracking and evaluation harnesses. Data pipelines for AnnData files, MCAP robot logs and OpenUSD scenes.
Reference designCloud architecture
Reference designs for each target cloud: network and placement for large jobs, storage tiers for long sequence reads and small-file robot logs, identity and customer-held keys, cost and goodput telemetry. See the architecture.
Reference designModel access
A gateway that routes agent, code-generation and evaluation calls to ChatGPT, Claude, Gemini, Grok and other frontier models, with budgets per project, caching, logging, fallbacks between providers and customer data-handling terms enforced in one place.
Reference designCapacity sourcing
Reserved, short-term and on-demand GPU capacity bought from the major clouds and GPU clouds, under agreements we hold or customer agreements we operate within. Providers and terms are named once an agreement is in place.
PlanningBuilt for two kinds of job at once.
Steady robotics rollouts and bursty biology experiments are placed across capacity pools, and work moves when a pool runs short. Illustrative view.
Use one layer, or all of them.
AI in production for your business
We design, build and run AI services for enterprise teams on large-scale compute, frontier models and tokens, with budgets, logging, caching and data-handling terms agreed per customer. See Enterprise.
A plan for a specific run
We turn your model, data and schedule into a mix of GPU capacity and model tokens, a cloud architecture and a cost range, and we say what evidence backs each number.
The control plane, run for you
Scheduling, checkpoints, failover and goodput reporting across the clouds that hold your capacity, running in your own cloud account where you prefer.
Sinewgrid Cell and Robotics
Train and adapt our models on your data in your tenant, with results returning to your own training loop.
Four steps from a question to a run.
- ScopeModel, data, schedule and any handling or residency rules.
- PlanA capacity mix, cloud architecture and cost range, with the evidence behind it.
- RunTraining on the control plane, with checkpoints and failover in place.
- ReviewA goodput and cost report, then the plan for the next run.
Planning a training run?
Tell us the model, the data and the schedule. We reply with a capacity plan and the evidence behind it.