Most AI tasks no longer need a frontier model
Frontier LLMs made it incredibly easy to add AI to a product. But that convenience comes with a cost.
Every task gets sent to the same expensive model. Agents become slower as they make more calls. AI bills grow with usage. Sensitive data leaves your infrastructure. And when a provider changes its pricing, limits or policies, your product has to adapt.
For a long time, the alternative was painful too: run open models yourself, accept lower quality, and build the infrastructure to serve them reliably.
That trade-off is disappearing.
Open models can now handle many of the tasks inside modern AI applications, from extraction and classification to search, reranking, document processing and increasingly complex generation. All at near-frontier quality.
Today we’re launching SIE to make those models easy to use in production.
Near-frontier quality at 3% of the cost
For each task page on this site, we run an open model on SIE and the models teams use today through the same test, and price both at list price. Fourteen of those tests include GPT-6.1 Sol, OpenAI’s frontier model, at its best-scoring setting. Across them, the open models reach a median 98% of GPT-6.1 Sol’s score at 3% of its cost.
Open models win on speed too. Classifying a support message takes 13.1 ms on SIE and 1.48 s on GPT-6.1 Sol; a chat reply takes 0.74 s against 2.49 s. An agent pays that difference on every call, so a ten-call task pays it ten times.
An open-source inference cluster under your control
Sending every task to a provider’s model means depending on its prices and its policies. A service you could only rent from us would move that dependency to us.
So SIE is open source, Apache 2.0 on GitHub. You run it in your own cloud, where your data stays, and you can read every line that touches it. Nobody can raise its price or retire the version your product runs on.
SIE also keeps up with new open models. It serves over 200 today, from a 22-million-parameter reranker to GLM-5.3 Flash with 321 billion parameters. Each one is a config file in the repository, so adding the next good model takes a pull request, not a wait for a vendor’s roadmap.
One GPU serves many models
A typical AI product calls an embedding model, a reranker, a classifier and an LLM, often for a single user request. SIE loads them on demand onto shared GPUs behind one API, so you stop paying for a GPU per model that sits idle most of the day. The Native Inference page shows how SIE runs your LLM and the smaller models on the same cluster.
Start on Serverless, then move to your own cloud
You don’t need GPUs to start. SIE Serverless runs SIE as a hosted API, so you can test a model on your own data in minutes. New accounts get $20 in free credits with no card required, and teams can apply for $500 more in launch credits from the console.
The Python SDK connects to Serverless by default:
from sie_sdk import SIEClient
client = SIEClient(api_key="sk-sie-…")When you move to your own cloud, your calls stay the same. Only the address changes:
client = SIEClient("https://sie.your-company.internal")Terraform modules cover AWS, GCP, Azure and Alibaba Cloud. The Serverless page has a prompt you can give your coding agent to deploy it for you.
Teams that want our engineers alongside them choose Native Inference. We help pick the open models that pass your evals and post-train one where none does, and once SIE runs in your cloud, we agree service levels with you. SIE stays Apache 2.0, so your team can always run it without us.
Start with one task
Pick the task that costs you the most today and run it on SIE. Each task page has the measured comparison and the code to switch: search, rerank, document to Markdown, structured output, guardrails, chat and more. The quickstart gets a local server running in two minutes.
If SIE saves you money, star the repo and tell us what you moved.
The Superlinked team