Cost
The bill only goes up
Tuned once at launch, never again. You pay for capacity you do not use.
Backed by
Running your own AI costs more than it should.
Sparset's agents tune, deploy, and run your AI models in production, cutting inference costs while your team stays on the product.
Running AI models is expensive and hard
Four problems start the day your model goes live.
Team
It needs a team you do not have
Serving, scaling, and on-call are full-time jobs that ship nothing.
Control
Your data leaves the company
On a cloud model API, every prompt, output, and workflow leaves the company.
Placement
It only runs where you built it
What works on one cloud or GPU does not carry over to the next.
Sparset runs your AI models in production, cheaper and faster.
Our platform takes care of the operations so your team does not have to: our agents tune inference around your workload, deploy your model, and keep it healthy around the clock, on your infrastructure or ours.
Inference is not a niche workload. As Jensen Huang put it, everything AI does runs on it.
“AI now has to think. In order to think, it has to inference. AI now has to do, in order to do, it has to inference. AI has to read, in order to do so, it has to inference. It has to reason, it has to inference.”
Roomates cut its inference cost by 99%
Roomates turns selfies into avatars. Moving off Gemini to Sparset cut the cost of each image from 4¢ to 0.05¢ ($0.0005), and the time to generate one from 12 seconds to 3.
“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, RoomatesRead the Roomates storyThree ways to work with Sparset
Governance and compliance controls are available on Stack and Edge.
Stack
On our infrastructure
We run your models on GPUs reserved for us. Your app calls a private endpoint, and there is nothing to host.
Edge
On your infrastructure
We run your models in your own cloud or data center. Requests and responses never leave it.
Forge
Your own specialized model
We build a smaller model for your task, from your data or synthetic data, and test it on your real work first.
How it works
Our agents plan, optimize, deploy, operate, and review your model, then do it all again every day.
Plan
Plans the infrastructure around your model and your requirements
Optimize
Makes your model cheaper and faster, tested against official benchmarks and your targets
Deploy
Puts it live once you approve, with a rollback ready
Operate
Catches problems around the clock and fixes them within limits you set
Review
Checks it daily and brings you tested improvements to approve
You own it. We run it.
Run it on your cloud, your hardware, or our GPUs. You decide who has access. We do the work.
Stays yours
Your data
It stays under your control. We only use the access you grant, and never train on your prompts or outputs.
Your infrastructure
Hardware you own, or GPUs you rent from a cloud or from us. Switch without rebuilding.
Your model
Weights, fine-tunes, and adapters stay yours.
Your application
It calls a private endpoint. Nothing to rewrite.
We run
Serving
Tuning and deployment. No serving stack to build.
On-call
Watched around the clock. Approved fixes run in seconds.
Cost
Reviewed daily. You see the measured before and after, then decide.
Testing
Every change is tested against your targets first.
No dedicated team required
Building it in-house
Pick a serving framework, wire up an orchestrator, add monitoring and an on-call rota, then retune it by hand as traffic changes. It takes a dedicated infrastructure team, and the work starts over every time the model changes.
Sparset
Our agents run the whole loop as one system: plan, optimize, deploy, operate, and review. Your setup gets cheaper and faster every day, and when your model or traffic changes, it is retuned for you. There is no dedicated team or forward-deployed engineer to staff: your engineers approve the changes and stay on your product.
Questions
Do you see our production data?
Only through the access you grant, whether you run on Stack or Edge. We never train on your prompts or outputs, and you can opt in to governance and compliance controls on either.
Do we need an infrastructure team?
No. Our agents do the serving, scaling, and on-call work. You need someone to approve changes, not a team to run them.
What can Sparset change without asking us?
Only the recovery actions you approve at setup: restarts, traffic shifts, failover, and rollback, within limits you set. Everything else is tested first and waits for your approval. Every action is logged.
How do we know it is actually cheaper?
We measure your current setup first, then test every change on traffic like yours. You get before-and-after numbers, with the tradeoffs named.
What happens if we stop working with Sparset?
Your model stays yours, and we document the setup so your team can take it over. If another approach fits better, we will say so up front.
How is Sparset priced?
It depends on the workload, the scale, and whether you bring GPUs or use ours. We scope it after looking at your model, so the price reflects what it takes to run.