Operation
We manage your model every day, not just on launch day.
The inference
layer for
AI
running in production.
Agents optimize inference to useless compute, run faster, and costless across your infrastructure.Agents optimize inference to use less compute,run faster, and cost less across your infrastructure.
Backed by
Sparset is an inference operations platform for teams and enterprises running AI models on servers and in data centers. That means agents that plan, optimize, deploy, and operate your model as one continuous job, GPU cost and reliability managed for as long as it runs, and a boundary your team sets between what runs on its own and what waits for your approval.
See what happens after your model ships
The cost and control problems don’t stop at launch. Sparset keeps operating your model, every day after.
Economics
GPU cost and efficiency get reviewed continuously, not once a quarter.
Control
Your team draws the line between what runs on its own and what waits. Every change that needs you arrives with its reason and its way back.
Range
One platform, from a team’s dedicated GPU cluster to an enterprise’s private data center.
We own the whole operating loop
Running it yourself
Pick a serving framework. Wire it to your orchestrator. Bolt on monitoring, alerting, and an on-call rotation. Discover the autoscaling doesn’t actually match your traffic. Patch it by hand at 2am when it doesn’t. Do it all again the next time the model changes, or the next time someone leaves the team.
Sparset
Sparset is an operations platform, and we own the whole operating loop: planning, optimization, deployment, operation, and review, one system instead of five tools duct-taped together. Because those steps run as one continuous loop, changes get reviewed and improved every day instead of once a quarter, and the same platform carries from a team’s GPU cluster to an enterprise’s private data center without being rebuilt.
One loop. Five stages. Runs continuously.
Plan, optimize, deploy, operate, review, then start again.
Plan
Assess the model, demand, quality and performance targets, budget, and privacy constraints, then choose hardware and a serving configuration to match. Run it on infrastructure you already have; Sparset can help arrange GPU capacity through its partners as part of planning your deployment.
Optimize
Configure the environment, then test model quality, latency, throughput, reliability, and cost against agreed criteria. A configuration that misses one of them does not move on.
Deploy
Once you approve, the tested configuration goes into production with its rollback path ready. Promotion never happens on its own.
Operate
Monitor service and hardware health continuously. When something goes wrong, the recovery actions you authorized during setup, worker restarts, traffic shifts, failover, and rollback, respond within the limits you set, and each one leaves a record of what ran and why.
Review
At least every 24 hours, production behavior gets reviewed for optimization opportunities. Proposed changes are tested and explained, benefits and tradeoffs included, before you approve them. A daily review doesn’t guarantee a daily improvement.
What you can actually do with it
Six things that are only true because Sparset is operating the model, not your own team.
Let incidents recover themselves
Worker restarts, traffic shifts, failover, and rollback run automatically, inside limits you approve up front. Everything else waits for you, and every action leaves a record of what ran and why.
Stop overpaying for GPUs
Compression, compilation, runtime tuning, and hardware selection get reviewed continuously, not fixed once at launch and left alone.
Skip building the platform layer
Planning, deployment, monitoring, and recovery are one workflow instead of software your own engineers have to stitch together and maintain. They stay on the product, not the serving stack.
Scale without the scramble
Traffic and demand shift constantly. Configuration adjusts to match, inside limits you set, instead of triggering a manual scaling fire drill.
Keep improving after launch
Production behavior gets reviewed at least every 24 hours; proposed changes are tested and explained before you approve them. A daily review doesn’t guarantee a daily improvement, but nothing goes unreviewed for a quarter either.
Test before it reaches production
Model quality, latency, throughput, reliability, and cost get checked against agreed criteria before anything gets promoted.
One platform. Two ways to run it.
Choose by how much governance and control your deployment needs.
Stack
For engineering and platform teams operating production inference on their own GPU infrastructure.
What’s included:
- Plan
- Optimize
- Deploy
- Operate
- Review
Edge
For enterprises operating production inference under stricter governance, control, and on-premises requirements.
What’s included:
- Plan
- Optimize
- Deploy
- Operate
- Review
- On-premises
- Governance controls
Cost per generation down 80%
Roomates runs production avatar generation on Sparset.
“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
FAQ
Sparset is the inference layer for AI running in production. We optimize, deploy, and operate models across the infrastructure they run on, combining model optimization, runtime configuration, hardware selection, deployment, monitoring, and recovery into one continuous system.
Sparset can operate across infrastructure you own, rent, or provision through our infrastructure partners. This can range from a dedicated GPU deployment for a team to private and on-premises infrastructure for enterprises with stricter operational requirements.
We optimize the model and the infrastructure around the workload rather than treating serving as a fixed configuration. Depending on the model, this can include compression, compilation, kernel optimization, runtime tuning, serving configuration, and hardware selection. We benchmark changes against your quality, latency, throughput, reliability, and cost requirements before they reach production.
Authorized recovery actions can run automatically within limits your team defines, including worker restarts, traffic shifts, failover, and rollback. Optimization, configuration, and model changes are tested first and presented for approval before being applied to production.
Sparset continuously monitors the health of the deployment and the infrastructure underneath it. Pre-authorized recovery actions can respond immediately when an issue falls within the limits you have set. Actions outside those boundaries are surfaced to your team with the relevant context instead of making an unapproved production change.
No. Sparset removes much of the specialized infrastructure work required to deploy and operate inference so your engineers can focus on the products and systems built around the model. Your team continues to control application logic, requirements, production policies, and approval boundaries.
Both. Stack and Edge operate existing models in production, while Forge develops custom and fine-tuned models for specific workloads. Model, framework, hardware, and deployment compatibility are evaluated before an engagement so we can determine the right approach for your workload.
Sparset is designed to minimize access to production data and does not use customer production prompts or outputs to train models. For controlled Edge deployments, production request and response payloads remain within your environment. Any operational telemetry, access requirements, and data boundaries are defined with your team before deployment.
Pricing depends on the workload, deployment scale, operating requirements, and whether GPU capacity is provided by you or arranged through Sparset partners. We begin by evaluating the model and workload so pricing can be tied to the infrastructure and operating requirements of the deployment.