Every model's
strengths.
One API.
Not every task needs the chief of surgery.
But when one does, you can't send the intern.
A few teams wire up a cheap model for the easy stuff and an expensive one for the hard stuff, then call it done. The rest use one model for everything and pay top price. Neither has ever measured which model actually wins on their work. Three questions make the gap obvious.
Question 1
How do you know what's hard?
Hard isn't a property of the task. It's a property of the model. The request that's trivial for one model breaks another. If you've never measured which tasks break which model, your easy/hard split is a guess wearing a confident tone.
Question 2
Which version are you running?
Open models score very differently across releases and quantizations. The gap between two builds of the same model can be wider than the gap between two models. If you can't name the exact release you're on, you don't know what you're measuring.
Question 3
What happens next Tuesday?
A new model ships and your hand-wired setup goes stale. When do you notice? When do you test the newcomer against your actual work? Most teams never do, so they keep paying for work a new open model would crush.
Autark runs that test harness for you. Tasks built from real work, not public benchmarks. Blind-judged, continuously. Every new model enters the system the day it ships. You keep building. We keep you on the best model.
We don't guess. We prove it. Continuously.
Every task type we handle is backed by a continuous blind evaluation pipeline. This methodology is the defensible asset.
Step 1
New model drops
Enters evaluation automatically. Tested against every task type in our matrix: blind, graded, cost-weighted.
Step 2
We prove it's better
A judge model scores every response against a task-specific rubric. It doesn't know which model wrote what. No bias.
Step 3
The system improves
Your next request gets better, automatically. No code changes, no migration. Your results improve on their own.
Every new model is a performance upgrade.
Models will never be uniformly good at everything. Every architecture has different failure surfaces. Every training corpus creates different blind spots. Every release reshuffles the landscape. This chaos is permanent: and permanent chaos means permanent optimization work. Our evaluation pipeline absorbs every new model in hours. You get the upgrade without touching a line of code.
Extract the invoice number, due date, and line-item totals from this email thread, and flag anything that doesn't match the quote.
4 of 30+ models
Illustrative — Autark continuously evaluates 30+ frontier and open models across every task type, not just the four shown.
Every model has strengths.
We know which ones.
Cheaper at equivalent quality
Every task handled by the right model. Simple queries never touch expensive models. Complex reasoning gets the power it needs. You're not paying frontier rates to extract a date from an email.
Quality that improves on its own
Every new model that launches enters our evaluation pipeline. If it beats the current leader for a task type, it becomes the new default. Your code never changes. Your results get better.
Nothing breaks when the ground shifts
Models get deprecated. Providers have outages. Quantizations drift. Every one of those is a problem you don't have to monitor, because the harness catches it before your users do.
Autark Cloud
Serverless AI inference behind a single OpenAI-compatible endpoint. Every request is handled by the best model for the task. Autark never stores or trains on your prompts.
- OpenAI-compatible: one base URL change, no rearchitecting
- The best model for every task: no manual selection required
- No prompt storage: processed in memory and discarded
- GDPR DPA included as standard. -private tiers add zero-retention routing
from openai import OpenAI
client = OpenAI(
api_key="autark_sk_YOUR_KEY",
base_url="https://api.autark.ai/v1",
)
response = client.chat.completions.create(
model="autark-pro",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)85.7%
LiveBench
Outperforms any single model
across 682 tasks
~20%
of the cost
Frontier-quality, without
breaking the bank
Zero
data retention
Every tier has a private option.
Your prompts never train a model.
You define the objective.
We handle the wiring.
We work with the world's leading open-weight models. That means fast, secure inference, wherever your data and your users need it, without depending on any single lab.
Open-weight models
Autark isn't a model provider.
It's what sits above them.
Buying from one model provider means accepting their ceiling. Autark sits above every model, combining their strengths into one API.
Your data doesn't live here.
That's the architecture.
Autark stores nothing and trains nothing on your data. Prompts are processed in memory and discarded. No prompt content is ever written to disk, logged, or used to build a model.
Model servers differ in what they can promise. Standard tiers route to the fastest available server, which may include providers without a zero-retention commitment. Every tier has a -private variant that restricts routing to servers that contractually guarantee zero retention. Same models, same integration, narrower path.
No storage, ever
Prompt content is processed in memory and discarded. Never written to disk, never logged.
GDPR DPA included
Standard, not an enterprise upgrade. Data processed and gone.
HIPAA & SOC 2
Covered through infrastructure partners' BAAs. Ask for details.
Subprocessors, certifications and data-processing terms live in the Trust Center.
Pay for performance.
Not for a logo.
Three capability levels plus specialist models, pay only for tokens you use. Get an API key, make a few calls, compare quality and cost against your current provider. The intelligence is automatic. You'll see the difference in your first session.
Common questions.
Anything not covered here? Talk to us directly.