Already running a model on your GPUs
You want less serving and operations work, and an ongoing approach to efficiency. During evaluation we confirm model, infrastructure, and access compatibility for your setup.
Stack operates production inference for your team.
Bring your model and your requirements. Run on GPU infrastructure you own or rent, or discuss capacity with Sparset. Sparset handles the operating loop for as long as the deployment runs; your team keeps approval authority.
What Stack does for your team
Stack is Sparset’s inference operations platform for engineering and platform teams. It brings planning, optimization, deployment, operation, and ongoing review into one operating loop, on GPU infrastructure you own or rent, with capacity available to arrange through Sparset’s partners. The operating work moves to Sparset. The decisions stay with you: what to validate against, which recovery actions may run on their own, and which changes need your approval.
Where you’re starting from
Three situations that belong in a Stack conversation, and what we confirm in each.
A model ready to deploy, no capacity yet
You want to go from a model to production without arranging every component yourself. Sparset can help arrange GPU capacity through its partners as part of planning your deployment.
On a hosted API, considering your own deployment
You want to evaluate different economics and operational control. This is a migration evaluation: whether a given model can be run in a dedicated deployment is tested, not assumed.
From first deployment to everyday operation
Sparset handles the operating loop. Your team sets the requirements and approves production changes.
Plan
Assess your model and workload to choose hardware and a serving configuration. Your team defines requirements and targets.
Optimize
Tune and test the configuration against quality, performance, reliability, and cost criteria. Your team agrees on what the deployment must achieve.
Deploy
Put the tested configuration into production after approval. Your team approves the production change.
Operate
Monitor continuously and run authorized recovery within your limits. Your team sets recovery permissions and boundaries.
Review
Review production at least every 24 hours and test potential improvements. Your team accepts or rejects proposed changes.
What runs on its own, and what needs your approval
Recovery you authorize at setup runs inside its limits. Every other change is a proposal until you approve it.
Runs on its own
Worker restarts, traffic shifts, failover, and rollback run automatically, inside the limits you authorize at setup. After every one, you get a record of what ran and why, readable by someone who was asleep when it happened.
Needs your approval
Optimization and configuration changes, including demand-driven reconfiguration and model promotion, wait for you. Each arrives as a tested proposal that explains its benefits and its tradeoffs. Recovery is never a pretext for an unrelated, unapproved change.
Who decides
Your operator sets the requirements and the recovery limits, and approves every other change. Stack takes the execution and maintenance work; the decisions stay with your team.
Cost per generation down 80%
Roomates runs production avatar generation on Sparset.
Customer evidence from one deployment, not a guaranteed result for every workload. The way to know is to evaluate yours.
What buyers ask
The true answers, including where the answer is “we confirm that during evaluation.”
We don’t own GPUs.
Rented capacity is in scope, and partner capacity can be discussed as part of planning your deployment. Packaging and billing for that capacity are worked out per deployment.
We already have a serving setup.
Stack’s value is the ongoing operating work. Support for your exact setup is confirmed during evaluation, not assumed.
We can’t allow arbitrary production changes.
Recovery is bounded by what you authorize at setup. Optimization and configuration changes wait for your approval.
We can’t trade model quality for savings.
Quality is part of the agreed validation criteria, alongside latency, throughput, reliability, and cost. No fixed quality-retention percentage is promised.
How do we know this saves money?
By evaluating your workload. Roomates’ result is customer evidence, not a guaranteed outcome for every deployment.
We need an SLA or specific compliance controls.
Specific commitments come out of a deployment and commercial review. Stricter governance requirements are a reason to evaluate Edge.
When to evaluate Edge instead
Edge is for inference operations under stricter governance, control, and on-premises requirements. It runs the same five-stage loop; what differs is the deployment boundary and the controls around it.
“Sparset helped us free ourselves from Gemini pricing and constraints, dividing our avatar generation cost by five, while allowing more than ten times the volume, for a quarter of the time per generation. The team is also very reactive and very easy to work with.”
Valentin PyCo-founder and CTO, Roomates