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For product & engineering · AI-driven product development

Open-Source LLMs &
Cost Optimization

Cut model costs without losing quality, and keep your data inside your own cloud.

Open-source models tuned, served and routed so you pay only for the capability each task needs.

Schedule a discovery call See case studies
The Challenge

Model bills grow faster than usage. Every request goes to the largest closed model, sensitive data leaves the business, and one vendor's price or policy change ripples through the product.

The model router

Pay for the model each
request actually needs.

Frontend · your product
Backend · model router
Inject
request → ◆ cache · 0 hits → ⇢ router
router.log
Waiting for the first answer…
Your monthly model bill
—lower spend
Every request to a frontier model—
Routed across open models—
Where requests go
The serving stack Parts light up as requests flow through the router above.
Your cloud · data stays here
only hard tasks leave → Frontier model API

Custom training

From an open base model
to your model.

Training pipeline · your GPUs
Recipe
Base model
Method
Framework
Training losswaiting for trainingstep 0 / 2,400
Task eval · your test set
training.log

Deployment

Serve it on the engine that
fits the workload.

General purpose

vLLM

High-throughput serving with continuous batching and prefix caching. Serving on OpenAI-compatible API.

Lowest latency

TensorRT-LLM

Compiled kernels and CUDA on NVIDIA Hopper for the fastest responses at scale.

Multimodal

vLLM-Omni

Text, images, audio and video models served through one pipeline.

Platforms we work with

Open models, served where your data already lives.

What stays the same

  • Your product and user experience.
  • Your cloud account and data controls.
  • The quality bar your users expect.

What changes

  • A lower cost per request.
  • Less dependence on one model vendor.
  • Data that stays inside your environment.

Llama, Mistral, Qwen and other open weight models

Chosen per task, then fine-tuned or distilled and tested against the model you use today.

vLLM and other inference servers

High-throughput serving with batching, quantization and caching to cut cost per request.

AWS, Azure, Google Cloud or on-prem GPUs

Deployed in your own account, so prompts and data never leave your environment.

Closed models where they win

Routing keeps the hardest tasks on frontier models and moves the rest to smaller open models.

What the agent does

From signal to action, with
your team in control.

Measure the spend

Cost, latency and quality per task, so you know where the money goes.

Pick and tune models

Open-weight models fine-tuned or distilled for your tasks, and tested against the model you use today.

Serve efficiently

Deployed in your cloud or on-prem, with quantization, batching and caching.

Route by task

Simple requests go to small models and hard ones to large models, with automatic fallback.

When to call us

Signs this is the right time.

Find a problem you recognize on the left. Read across to see which solutions address it.

If you're seeing... We run open-source models in your cloud We cut cost and latency
A model bill rising faster than usage✓✓
Data that can't leave your cloud✓
Dependence on a single model vendor✓✓
Latency too high for the product experience✓

Take your AI from pilot to production.

Build, validate, and deploy AI that delivers real business impact.

Schedule a discovery call