Open-Source AI Deployment
Frontier AI on your own infrastructure
At a glance: open-weight models like Kimi K3 now rival the best closed AI — with a 1M-token context and top rankings for coding and agents. We install and operate them on your own servers or private cloud, so your data never leaves your walls: no third-party API, no data transfer, predictable costs at scale.
What we deliver
- Audit & infrastructure sizing — we benchmark your use cases and size the setup: on-premise GPU server, sovereign private cloud, or hybrid.
- Secure inference stack — production-grade serving with SSO, access control and audit logs, inside your network perimeter.
- RAG on your internal knowledge — the model answers from your documents and databases, with sources, while everything stays local.
- Agents on top — autonomous agent teams built on your private models, automating whole workflows end to end.
- Monitoring & model upgrades — usage dashboards, quality checks, and upgrades as stronger open models ship.
- Team training — so your teams actually adopt and operate the platform.
Why companies self-host
Data stays in-house
Prompts, documents and outputs never leave your servers. No third-party API, no data transfer — a hard requirement in legal, healthcare, finance and industry.
GDPR by design
Self-hosting means no transfer outside the EU and a full audit trail — we document the deployment so your DPO can sign off.
Predictable costs
No per-token bill that grows with adoption: a sized infrastructure, a fixed cost. Heavy usage is no longer punished.
No vendor lock-in
Open weights are yours to run for as long as you want. Swap models as better ones ship — without rewriting your stack.
How it works
Audit & sizing
We map your use cases, data constraints and infrastructure, then recommend models and hardware.
Pilot
A model deployed on your infrastructure, benchmarked against your real tasks — not a generic demo.
Production
Security hardening, SSO, monitoring, RAG and integrations — ready for the whole company.
Enablement
We train your teams and hand over the keys. You own the platform; we stay as backup.
Common use cases
Internal knowledge & RAG
Company-wide answers grounded in wikis, procedures and databases — nothing leaves the building.
Regulated document processing
Contracts, medical records and legal files processed by models you control.
High-volume agent workloads
Sustained agent traffic that would cost a fortune per token on APIs runs at a fixed infrastructure cost.
Hybrid architectures
Sensitive workloads self-hosted; specific tasks routed to external APIs under rules you define.
Pricing
Deployment projects are scoped per infrastructure and use case. Our audit gives you the actual break-even point between self-hosting and API costs for your volumes — before you invest.
For the economics, see our analysis on self-hosted LLM vs API costs.
Frequently Asked Questions
Are open-source models really as good as GPT or Claude?
For a growing share of tasks, yes. Since July 2026, Kimi K3 — an open-weight model from Moonshot AI — ranks above several leading closed models in independent blind testing, and DeepSeek, Llama and Qwen are close behind on many workloads. The honest answer depends on your use case, which is why our pilot benchmarks candidate models on your actual tasks before you commit.
What hardware do we need?
Less than you might think. Frontier-scale models like Kimi K3 need a multi-GPU server or a private cloud cluster, but most enterprise use cases run very well on mid-size open models that fit on a single GPU server. We size the infrastructure to the use case — not to the biggest model on the leaderboard.
Does self-hosting help with GDPR and the EU AI Act?
Significantly. With a self-hosted model there is no data transfer to a third-party provider or outside the EU: prompts, documents and outputs stay on infrastructure you control, with a full audit trail. We document the deployment so your DPO and security team can sign off.
Is self-hosted AI cheaper than API-based AI?
At low usage, an API is cheaper. At sustained, company-wide usage, self-hosting usually wins: you pay for a sized infrastructure instead of for every token. Our audit gives you the actual break-even point for your volumes before you invest.
Can we combine private models with API models?
Yes — hybrid is often the right architecture. Sensitive data and high-volume workloads run on your private models; specific tasks route to an external API under rules you define. We build the routing so the choice is a policy, not chance.
How long does a deployment take?
A pilot typically takes a few weeks. Moving to production depends on your security requirements and infrastructure, but think in weeks, not quarters — open-source tooling has matured enormously.