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Some data cannot leave your infrastructure. PHI under HIPAA. PII under GDPR. Proprietary IP your legal team will not let touch a third-party API. Classified or CUI data in regulated environments. For those cases, sending queries to OpenAI or Anthropic is not an option, regardless of what their DPA says.
Private LLM deployment runs the model on hardware you control: on-premise GPU servers, an AWS VPC with no data-sharing agreements, Azure private endpoints, or fully air-gapped environments. No BAA negotiations. No per-token data exposure risk. No rate limits from a shared API.
We select the model, configure the inference stack, run load testing against your latency targets, and hand off a deployment your team can operate. The 90-day optimization period is included, because real usage patterns always reveal tuning opportunities that load testing alone doesn't.
Tell us what you're building.
Four deployment patterns covering the range from on-premise GPU hardware to air-gapped environments.
High-throughput inference using vLLM on your physical servers. We handle model quantization, tensor parallelism across multiple GPUs, batching configuration, and performance profiling to hit your latency targets.
Isolated cloud deployments inside your VPC with no data-sharing agreements, no model training on your data, and network policies that prevent data egress. The cloud provider processes compute, your data never leaves your network boundary.
Ollama-based deployments for teams with CPU-only servers, smaller GPU hardware, or developer laptop use cases. Right-sized for internal tools, prototypes, or workloads that don't need high-throughput inference.
Fully disconnected deployments for environments with no internet access: classified networks, isolated OT systems, or facilities that prohibit external API calls. We handle the offline model delivery and deployment process.
We benchmark before we deploy. We load-test before we hand off. We stay available for 90 days after launch because real traffic reveals what synthetic load testing misses.
01
We document your data classification requirements, latency targets, concurrency requirements, and existing hardware or cloud infrastructure. Then we recommend the model and inference stack, not the other way around.
02
We benchmark candidate models on your actual prompts and measure output quality, latency at your target concurrency, and hardware utilization. You see the benchmark results before we commit to a model.
03
We deploy the inference stack, tune quantization and batching for your hardware, and run load testing to verify your latency and throughput targets are met. We do not hand off a deployment that has not been load-tested.
04
We integrate the inference endpoint with your application, set up monitoring, and provide 90 days of optimization support: model updates, configuration tuning, and performance improvements as your usage patterns become clear.
We select based on your quality requirements and hardware constraints, not marketing rankings.
Private deployment is the right call for specific reasons, and a costly mistake for the wrong ones. We would rather run the numbers with you than sell you a GPU cluster you do not need. Four facts shape the decision.
Utilization decides the cost. A GPU you own costs the same whether it runs at 80 percent or 10 percent. Self-hosting wins on cost only when sustained volume keeps the hardware busy. Bursty, intermittent workloads leave expensive silicon idle, and the per-request cost balloons. We model your real utilization curve, because a cluster at 15 percent is more expensive per call than the API it replaced.
The quality gap is real and measurable. The best open-weight models are strong, but on complex reasoning they still trail the frontier API models. Whether that gap matters depends entirely on your task. We benchmark candidate open-weight models on your actual prompts and show you the numbers, so the trade between control and capability is a decision you make with evidence rather than a hope.
Serving is an ongoing job, not a one-time setup. A private deployment is infrastructure your team now owns: patching, scaling, monitoring, and the occasional 2 a.m. incident. We build it to run cleanly and document it thoroughly, but we are honest that it adds operational surface area. For some teams that is worth the control; for others a managed option is the better trade, and we say which we think you are.
When it clearly wins. Three cases make private deployment the obvious answer regardless of the cost math: data that genuinely cannot touch a third party, a hard latency or air-gap requirement no shared API can meet, and sustained volume high enough that token pricing exceeds total cost of ownership. When one of those holds, the question is not whether but how, and that is the work we do.
Private deployment is significantly more expensive and complex to maintain than cloud APIs. Here is when you should use the API instead.
Teams whose data is fine with cloud APIs
If your data doesn't have hard regulatory or contractual constraints on third-party processing, OpenAI's enterprise tier or Anthropic's API with zero data retention is faster, cheaper, and better-quality than a private deployment. Use cloud APIs unless you have a specific reason not to.
Teams without GPU infrastructure or cloud budget
A minimum viable private deployment requires either a dedicated GPU server (starting around $10,000–$30,000 for hardware) or a reserved cloud GPU instance ($2,000–$8,000+ per month depending on size). If that budget doesn't exist, private deployment is the wrong solution.
Tell us your data classification requirements, existing hardware or cloud environment, latency targets, and the application you want to power. We'll reply with a deployment recommendation and rough scope within one business day.