Your LLM costs are eating
your product margins

distil labs lowers the cost of your AI workloads by 80%, without compromising quality.

The optimization that never makes the roadmap

Testing prompts, benchmarking models, building routing logic and deploying specialized models takes time, and product work always comes first.

Your team knows it could reduce inference costs, but you default to the safest option: sending everything to an expensive frontier model. As usage grows, that shortcut burns through your margin.

What it would take

Test prompts
Benchmark models
Build routing logic
Deploy specialized models

What you do instead

Send everything to the frontier model

Why teams switch to distil labs

Lower cost, same accuracy

A custom SLM tuned to your task matches frontier accuracy from a 100x smaller model. At consumer scale that is up to 80% lower cost per request, and margin that survives growth.

Low-latency intelligence

Small models answer in a fraction of the time and run close to your stack. When every 100 milliseconds is audible to a caller, the latency ceiling is yours to control.

Control your throughput

Dedicated endpoints with throughput that is yours 24/7. A model a fraction of the size multiplies documents per GPU hour, and cost per document falls with it.

How it works

1
Observe your real workload
Route 1% of production traffic to your distil labs endpoint. It forwards every request to your current LLM, so responses are unchanged, and captures the traces that show what your workload actually does.
```
client = OpenAI(
-   base_url="https://api.openai.com/v1",
+   base_url="https://ead9fb75.i.distillabs.ai/v1",
    api_key="your-distil-api-key",
)
```
2
distil labs builds the alternatives
It takes roughly 10 minutes of your effort and a day to execute the pipeline: platform creates the evaluation set from your traffic and executes synthetic dataset generation, fine tuning, quantization, and deployment to prepare an optimized endpoint. The result is evaluated against your current model on accuracy, costs and latency.
```
$ distil traces create-from-endpoint <endpoint-id>
✓ Traces created with ID: <trace-id>

$ distil seed-dataset create-from-traces <trace-id>
✓ Seed dataset created with ID: <seed-dataset-id>

$ distil training-dataset create-from-seed-dataset <seed-dataset-id>
✓ Training dataset created with ID: <training-dataset-id>

$ distil slm create-from-training-dataset <training-dataset-id>
✓ SLM created with ID: <slm-id>

$ distil deployment create-from-slm <slm-id>
✓ URL and API key
```
distil labs7 stages
1
Evaluation set
relabel a sample of traffic with frontier LLM
Create synthetic data↻ per batch
2
Generate batch of data
create synthetic examples for your task
3
Validate batch of data
filter, dedupe, drop low-quality samples
4
Match distribution to target
focus generation on underrepresented sections
5
Post-training (SFT + RL)
fine-tune, then reinforce
6
Model quantization
compress weights, hold accuracy
7
Optimized deployment
replace OpenAI endpoint
3
Approve and scale to 100%
Your evaluation runs automatically, the results speak for themselves. You move to 100% traffic when you're confident.
80% lower cost per request
4
Continuously improve
distil labs retrains and redeploys your model, so you always get the most efficient endpoint.
monitorretrainredeploy

How it works

Step 1 of 4

Observe your real workload

Route 1% of production traffic to your distil labs endpoint. It forwards every request to your current LLM, so responses are unchanged, and captures the traces that show what your workload actually does.

client = OpenAI(
-   base_url="https://api.openai.com/v1",
+   base_url="https://ead9fb75.i.distillabs.ai/v1",
    api_key="your-distil-api-key",
)

Step 2 of 4

distil labs builds the alternatives

It takes roughly 10 minutes of your effort and a day to execute the pipeline: platform creates the evaluation set from your traffic and executes synthetic dataset generation, fine tuning, quantization, and deployment to prepare an optimized endpoint. The result is evaluated against your current model on accuracy, costs and latency.

$ distil traces create-from-endpoint <endpoint-id>
✓ Traces created with ID: <trace-id>

$ distil seed-dataset create-from-traces <trace-id>
✓ Seed dataset created with ID: <seed-dataset-id>

$ distil training-dataset create-from-seed-dataset <seed-dataset-id>
✓ Training dataset created with ID: <training-dataset-id>

$ distil slm create-from-training-dataset <training-dataset-id>
✓ SLM created with ID: <slm-id>

$ distil deployment create-from-slm <slm-id>
✓ URL and API key

Step 3 of 4

Approve and scale to 100%

Your evaluation runs automatically, the results speak for themselves. You move to 100% traffic when you're confident.

Step 4 of 4

Continuously improve

distil labs retrains and redeploys your model, so you always get the most efficient endpoint.

80% lower cost per request

distil labs

0 / 7

Evaluation set

relabel a sample of traffic with frontier LLM

Create synthetic data↻ per batch

Generate batch of data

create synthetic examples for your task

Validate batch of data

filter, dedupe, drop low-quality samples

Match distribution to target

focus generation on underrepresented sections

Post-training (SFT + RL)

fine-tune, then reinforce

Model quantization

compress weights, hold accuracy

Optimized deployment

replace OpenAI endpoint

What you get

Quality, cost and latency stop competing

With off-the-shelf models these three pull against each other. Higher accuracy costs more and answers slower. Cheaper or faster gives up accuracy. Routing traffic between providers does not escape this: it only picks a different point on the same curve.

An SLM built for your workload moves the curve: it holds your accuracy bar at a fraction of the cost and the latency.

An optimized model endpoint

You call one OpenAI-compatible endpoint. Everything behind it is optimized to your use case: specialized SLM for your task, prompt optimization and caching, and quantized, tuned serving.

Closed models stop at prompt engineering. Custom models unlock every layer of the stack and you can see the difference.

Optimize without giving up control

Set the constraints that matter to your product, independent of closed-source providers. Models you rely on never get deprecated, your throughput does not vary based on the day of the month, price per request never increases.

You control your IP, where the model lives, and where it runs.

Quality bar
Latency bar
Cost limits and fallback policies
Data residency requirements
Private deployment

From model selection to workload optimization

Traditional approach	distil labs
Optimized for a benchmark	Optimized for your task
IP and economics tied to your provider	You own the model layer: immune to deprecations, price changes, rate limits
Prompt engineering and caching	The full open-weight optimization stack
Hire an ML team, or don't optimize	ML expertise without hiring an ML team
Revisit the decision occasionally	Continuously adapts as the workload changes

Find out what your workload should cost

We identify where you are overspending, evaluate the optimizations available, and show you the lowest-cost configuration that meets your quality bar. It starts with an export or one day of traffic.

Get started Talk to us →

Wondering what happens to that export? Security and data handling

What Our Customers Say

We needed a small model that could power our product on an IBM P11, entirely on-premises. distil labs’ fine-tuned models allowed us to ship a self-contained solution where the SLM and our graph platform coexist on the same hardware. For customers in regulated industries, this means AI-powered query generation with complete data privacy – nothing ever leaves their environment.

David J. Haglin

Co-Founder and CTO at Rocketgraph

Using distil labs, we were able to spin up highly accurate custom small models tailored to our workflows in no time. Those models cut our inference costs by 68% without sacrificing quality. The distil labs team was incredibly supportive as we got started and helped us get to production smoothly.

Lucas Hild

Co-Founder & CTO at Knowunity

The distil labs platform accelerated the release of our cybersecurity-specialized language model, KINDI, enabling faster iterations with greater confidence. As a result, we ship InovaGuard improvements sooner and continuously boost investigation accuracy with every release.

Samir Bennacer

Co-Founder and CTO at Octodet

30M+ people use distil labs models today

From our blog

How Knowunity used distil labs to cut their LLM bill by 68%

Knowunity, an edtech startup processing hundreds of millions of AI requests monthly, used distil labs to train a custom small language model that cut inference costs by 68% while improving classification accuracy from 81% to 93%.

Why training on production traces fails (and what to do instead)

Training directly on production traces doesn't work as well as you'd expect. We tested across five scenarios and synthetic data from traces scores up to 26 percentage points higher in accuracy.

The 10x Inference Tax You Don't Have to Pay

Benchmarking fine-tuned small language models (0.6B-8B) against 10 frontier LLMs across 8 datasets shows that task-specific SLMs match or beat frontier models at 10-100x lower inference cost.

All posts →

Your LLM costs are eatingyour product margins

The optimization that never makes the roadmap

Why teams switch to distil labs

Lower cost, same accuracy

Low-latency intelligence

Control your throughput