Operation
We manage your model every day, not just on launch day.
The operationsplatform for AIrunning in production.The operations platform forAI running in production.
Agents plan, optimize, deploy,and operate your models oninfrastructure you already run.Agents plan, optimize, deploy, and operate your modelson the servers and infrastructure you already run.
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
Recovery actions you authorize during setup — worker restarts, traffic shifts, failover, and rollback — run on their own inside the limits you set. Everything else, including optimization and configuration changes, waits for your approval.
Service and hardware health are monitored continuously. Authorized recovery actions respond within the limits agreed during setup, and you get a record of what ran and why.
Those are components you still have to assemble, tune, and staff. Sparset owns the whole operating loop — planning, optimization, deployment, operation, and review — as one system rather than five tools you maintain.
Planning, optimization, and validation run against the new model the same way they did for the current one. You see the tested comparison before anything is promoted.
No. It removes the platform work your engineers would otherwise build and maintain, so they stay on the product instead of the serving stack. The people who run it keep the controls: they authorize which recovery actions can run on their own, set the limits those run inside, and approve everything else.
Configuration adjusts to match traffic inside the limits you set, instead of requiring a manual scaling fire drill.
Multiple models run as separate jobs on the same platform, each with its own targets, limits, and approval rules.
The platform operates your serving infrastructure. Production request and response payloads stay in your environment.
Pricing depends on the edition, the scale of your deployment, and whether you bring your own GPU capacity. Book a call and we will walk through it against your workload.