The bill only goes up
Most setups are tuned once at launch and never again. You keep paying for capacity you do not use.
Running your own AI
costs more than
it should.
Sparset takes over the work of runningAI models in production: tuning,hardware, deployment, and on-call.On hardware you already own.Sparset takes over the work of running AI models in production:tuning, hardware, deployment, and on-call. On hardware you already own.
Backed by
Getting a model working is the easy part
Keeping it running is the part nobody budgets for. Four costs start the day it goes live, and none of them stop.
It eats your engineering time
Serving, scaling, monitoring, rollback. Months of work your customers never see, and it is never finished.
It breaks when nobody is watching
Somebody gets paged at 2am and fixes it by hand. Nothing about that fix stops it happening again.
It only runs where you built it
What works on a rented GPU does not carry over to your own data center. So teams build it twice, or give up on one.
Sparset runs your AI models in production so your team does not have to. Every time someone uses an AI feature you have built, a computer has to run your model to answer them, and at real volume that becomes one of the largest lines in your infrastructure bill. We make those runs cost less and finish faster, keep the service healthy around the clock, and keep improving it every day it runs, on your own servers or in your own data center. Your team decides what we may change on our own and what waits for your approval.
Roomates stopped paying by the image
Roomates turns selfies into avatars. Each one used to cost 4¢ and take 12 seconds. On Sparset each takes 3 seconds, and the bill is a fixed $400 however many they make.
“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
What changes, and what does not
Changing how you run AI in production is a real decision. Here is exactly what it touches, and what it leaves alone.
What stays yours
Your hardware
Sparset runs on the GPUs you already own or rent. There is no migration into a Sparset cloud, because there is no Sparset cloud to migrate into.
Your model
You keep your model, its weights, and any fine-tuning or custom adapters you have built. We do not swap in one of ours.
Your application
Your product talks to a private endpoint, the way it already talks to any other internal service. No rewrite on your side.
Your data
Sparset is built to touch as little production data as possible, and never trains on your prompts or outputs. On Edge deployments, requests and responses do not leave your environment at all.
What we take on
The serving stack
Choosing hardware, tuning the model and the runtime, deploying it, and keeping the whole thing healthy stops being anyone on your team’s second job.
The on-call pager
We watch the service and the hardware under it around the clock, and the recovery actions you authorized in advance run in seconds rather than waiting for someone to wake up.
The cost review nobody has time for
Efficiency is looked at every day instead of at launch and never again. You see the measured before and after, not a promise.
The testing before anything ships
Quality, speed, throughput, reliability, and cost are all checked against targets you agreed to, before a change is ever put in front of you.
What you get instead
What happens to those same four costs once we are the ones operating the model.
The bill goes down
Cost is reviewed every day, not once at launch. When something cheaper works, you see the numbers before you decide.
Your engineers get their time back
No serving stack to build or maintain. Deployment, monitoring, and recovery are one system we run.
You decide what runs on its own
You set what Sparset may do without asking and what waits for a person. Anything that waits arrives with its reason and its way back.
It runs wherever you need it
Start on one rented GPU. Move to your own racks or data center without rebuilding anything.
How it works: five stages, running in a loop
Plan, optimize, deploy, operate, review. Then start again, every day the model is live.
Plan
We look at your model, your traffic, your targets, and your budget, then pick the hardware and serving setup that fit. Use GPUs you already have, or we can arrange capacity through our partners.
Optimize
We make the model cheaper and faster, then test it against the targets you agreed to. If it misses one, it does not move on.
Deploy
Once you approve it, the tested setup goes live with a rollback ready. Nothing reaches production without your sign-off.
Operate
We watch the service and the hardware around the clock. When something breaks, the fixes you approved at setup run within the limits you set, and each one is logged.
Review
At least once a day, we check how the model is really behaving and look for improvements. Every proposal comes tested, with its benefit, its tradeoff, and its way back, for you to approve. A daily review does not guarantee a daily improvement.
Why not build it yourself?
Building it yourself
Pick a serving framework. Wire it into your orchestrator. Add monitoring, alerting, and an on-call rota. Then find out the autoscaling does not match your real traffic, and patch it by hand at 2am. When the model changes, or the engineer who built it leaves, you do it all again.
Sparset
One system does all of it, and we own the whole operating loop: planning, optimization, deployment, operation, and review, instead of five tools taped together. Because it runs as one loop, your setup is reviewed and improved every day, not once a quarter. And the same system moves from a rented GPU to a private data center without being rebuilt.
What your team stops doing
Six jobs that come off your engineers’ plate the day Sparset takes over the model.
Building the serving stack
No more stitching a serving framework to an orchestrator, bolting on monitoring, and maintaining all of it forever. That work does not get delegated, it stops existing.
Getting paged at 2am
Worker restarts, traffic shifts, failover, and rollback run on their own inside limits you approve up front. Everything outside those limits waits for you, and every action leaves a record of what ran and why.
Guessing at GPU spend
Compression, compilation, runtime tuning, and hardware choice are reviewed continuously, instead of being decided once at launch and never revisited.
Scrambling when traffic moves
Demand shifts constantly. The configuration adjusts to match it, inside limits you set, rather than turning every busy week into a manual scaling exercise.
Testing releases by hand
Model quality, latency, throughput, reliability, and cost are checked against agreed criteria before anything is promoted to production.
Falling behind after launch
Production behavior is reviewed at least every 24 hours, and proposed changes come tested and explained. A daily review does not guarantee a daily improvement, but nothing goes unlooked-at for a quarter either.
One platform. Two ways to run it.
Which one you need depends on where the model has to run, and who has to sign off on it.
Stack
For engineering and platform teams running AI in production on their own GPUs, whether those sit in the cloud or in their own racks.
What’s included:
- Plan
- Optimize
- Deploy
- Operate
- Review
Edge
For enterprises that need the model inside their own data center, under their own security, audit, and compliance requirements.
What’s included:
- Plan
- Optimize
- Deploy
- Operate
- Review
- On-premises
- Governance controls
FAQ
Every time someone uses your AI feature, a computer has to run your model. At volume, that is usually the expensive part. Sparset takes over that step: we pick the hardware, make the model cheaper and faster, deploy it, watch it around the clock, and keep improving it, all on infrastructure you own or rent.
No. Sparset runs on the GPUs you already own or rent, in the cloud or in your own data center. There is no Sparset cloud. If you need capacity, we can arrange it through our partners, and the deployment still belongs to you.
No. You keep your model, including any fine-tuning or adapters. Your application calls a private endpoint, the same way it calls any internal service. Roomates kept its own custom adapter through the whole move.
Only the recovery actions you authorize at setup: restarting a worker, shifting traffic, failing over, and rolling back, each within limits you set. Everything else, including optimization and model changes, is tested first and waits for your approval. Every automatic action is logged, so you can read what ran and why the next morning.
We watch the service and the hardware around the clock. If the problem is inside the limits you set, the fix you authorized runs immediately and you read about it afterwards. If it is outside them, your team is alerted with the context. We never make an unapproved change to your production system.
No. It takes the infrastructure work off them so they can work on the product. Your team keeps control of the application, the requirements, and where the approval line sits.
As little as possible, and we never train on your prompts or outputs. On Edge deployments, requests and responses never leave your environment. Any telemetry we need is agreed with your team before anything is deployed.
We measure your current setup first, then test every change on a workload that matches your real traffic. You get before-and-after numbers under stated conditions, with the tradeoffs named.
The deployment runs on your infrastructure with your model, so it stays with you. We document the setup so your team can take it over. And if a simpler approach or another provider is a better fit, we will tell you up front.
Both. Stack and Edge run models you already have. Forge builds custom and fine-tuned models for a specific workload. We check compatibility before an engagement so the right path is clear from the start.
It depends on the workload, the scale, what you need us to operate, and whether you bring GPU capacity or we arrange it. We look at the model and the workload first, so the price reflects what it actually takes to run, not a list price.