Open Source
We deploy open-weight models when you need full control, lower cost at scale, or AI that runs entirely on your own infrastructure.
Open-weight models have closed much of the gap with closed labs, and for the right workloads they are the better answer. Llama and Mistral give us strong models we can run ourselves, vLLM gives us fast, efficient serving, and the Hugging Face ecosystem gives us the models, datasets, and tooling to fine-tune and ship without depending on any single vendor's API.
We use open source where control, cost, or data residency make it the clear choice, and we are honest when a hosted frontier model is simply better for the task. Running models yourself trades some convenience for real ownership: no per-token bill that scales with success, no data leaving your walls, and no roadmap you do not control.
What we use
- Llama and Mistral open-weight models for self-hosted, owned deployments
- vLLM for high-throughput, low-latency, cost-efficient model serving
- Hugging Face models, datasets, and tooling for fine-tuning and evaluation
- LoRA and full fine-tuning to specialize models to your domain and data
- Quantization and right-sizing to fit your hardware and latency budget
We deploy open-weight models on your own infrastructure or private cloud, served with vLLM behind an API that matches the same provider-agnostic layer we use everywhere else, so an open model and a hosted one are interchangeable in your application. We fine-tune on your data inside your environment so nothing sensitive leaves, size and quantize models to your real hardware and latency targets, and set up the monitoring and autoscaling a production service needs. You end up owning the weights, the serving stack, and the entire pipeline, with no external dependency on a model API.
Questions, answered
For many workloads, yes. The gap has narrowed sharply, and for tasks like classification, extraction, and domain-specific generation a fine-tuned open model often matches or beats a general hosted one. We benchmark on your real data and recommend honestly when a closed model is still the better call.
Everything. The weights, the serving stack, the fine-tuning pipeline, and the infrastructure all live in your environment. There is no external model API in the path, so there is no per-token bill that grows with usage and no vendor roadmap you depend on.
Let's build the intelligence that moves your business.
Tell us where you're headed. We'll show you what's possible, and exactly how we'd get there together.