If per-token bills climb every quarter, security review keeps blocking your pilot, and model updates arrive without your approval, it’s time to bring the model in-house. A private LLM deployment fixes all three.
Your data, workloads, and budget get mapped to the right deployment model before any hardware is bought.
Our engineers deploy and serve open-weight models in your data center or cloud account, ready for internal applications and for agents built through our enterprise AI agent deployment services.
Workflows move off the public API with quality measured at every step.
High-volume document jobs run on your own capacity, free of rate limits.
One private model serves every business unit, each inside its own data boundary.
Self-hosting moves model security to your side, and our security engineers harden the full stack before go-live.
After go-live, we keep your private model fast, available, and current under your change control.
Our architects review your data classes, current API usage, and infrastructure. The readout names the workloads to move first and the route each data class may take.
Capacity sizing, the deployment model, and a cost comparison against today’s API spend fit into one document for budget and security sign-off.
Delivery begins with the workload that carries the highest cost or compliance risk. At handover, code, configuration, and runbooks pass to your engineers.
The engagement closes with the first model upgrade run through your evaluation gate. Your team sees the process work before running it alone.
Up to 50% faster delivery, up to 20% fewer bugs. Our engineers use AI assistance across discovery, architecture, build, and QA. A senior engineer reviews every release before it ships.
ISO 27001:2022 certified. Our own delivery runs under audited security controls, HIPAA-ready and GDPR covered.
92% senior engineers. The people who size your GPUs and design your access model have shipped production systems in regulated environments.
10+ years of data engineering. Pipelines, quality checks, and retrieval indexes come from a team that has built them for over a decade.
96% employee retention. The engineers who deploy your model stay to run its next upgrade.
99% on-time delivery. Fixed-scope milestones keep your rollout on the dates you planned.
An API invoice finance can’t forecast. A security review that won’t clear. A model update your validation team never approved.
On the hardest open-ended reasoning, top proprietary models still tend to lead. Most enterprise workloads are narrower: extracting fields, classifying documents, summarizing cases, answering questions over your own archive. On tasks like these the gap is often small, and retrieval or fine-tuning can close it.
General benchmarks won’t tell you where your gap sits. Your own data will:
Our engineers run this comparison as the first step of every migration. Where a gap remains, fine-tuning through our LLM development services closes it before cutover.
The two cost models work differently.
Steady, predictable volume favors private capacity. Batch jobs that fill idle hours overnight improve the math further. Low or spiky volume often stays cheaper on an API.
To check your own position, take three months of API invoices and split the steady daily baseline from the peaks. The baseline is what private capacity would carry, and the peaks can stay on the API. Our architects build this comparison into every architecture proposal.
Four factors set the hardware bill:
A smaller model tuned for a narrow job often runs on a fraction of the hardware a general-purpose model needs. Our engineers size capacity from load tests on your real traffic patterns before any purchase order goes out.
Self-hosting changes who carries the risk. The vendor no longer sees your data, but your team now owns:
Prompt injection through retrieved documents works the same way on a private model as on a public one. Treat the deployment as a new production system with its own threat model, access reviews, and penetration tests. Our security engineers harden and test the stack before go-live. If your wider estate needs the same attention, our cybersecurity services team can extend the scope.
Reviewers usually ask for:
Share this list with your reviewers early and ask what their process adds. Approval then runs in parallel with the build, and the review doesn’t stall at the end. If your control framework needs work first, our IT compliance specialists can start there.
Yes, and for many companies a hybrid setup is the right end state. Routing rules send restricted data classes to the private model and everything else to the API. The private model also serves as a fallback when the API goes down. Our LLM orchestration services build that routing layer and the rules behind it.
Licenses differ across model families. Some use permissive terms such as Apache 2.0. Others ship under community licenses with acceptable-use rules or thresholds tied to the number of users. A model that wins the technical evaluation and then fails legal review costs weeks, so send candidate licenses to your legal team before testing begins. License review sits inside our model selection step for that reason.
Your team can run it from the runbooks delivered at handover. Our engineers can also stay on through LLMOps services to cover monitoring, capacity changes, and upgrades. Many teams pick a split: the client operates day to day, and we handle upgrades. To map the right split for your team, contact us and our architects will walk through it with you.
AI
AI
AI
