Service

Private LLM deployment

Pain

Teams need broader reasoning than a compact SLM can offer, but public APIs conflict with data residency, procurement rules, or appetite for recurring token bills tied to external vendors.

Outcome

We help you stand up private LLM inference on approved hardware - sizing GPUs, choosing serving stacks, wiring auth and logging, and documenting data flows for your security and legal stakeholders.

Differentiator

We align with your existing platform standards (Kubernetes, VMs, air-gapped labs) and produce runbooks and review packs - not a one-size demo that ignores how your org actually operates.

When a larger private model fits

Private LLMs suit tasks that need wider general knowledge, longer context windows, or flexible instruction following - still under your network and logging policies when deployed on-premises or in your dedicated cloud. They trade higher compute cost per token for capability; routing and caching strategies (see hybrid routing) keep spend predictable.

Private LLM vs. Custom SLM vs. cloud API

Summary for stakeholder conversations; your procurement and security teams validate final positions.

Comparison of deployment approaches for language models
Aspect	Private LLM	Custom SLM	Cloud API
Primary fit	Broad reasoning, longer context, flexible instructions inside your boundary	High-volume, domain-specific patterns with smaller footprint	Fastest path to capability when data policy allows external calls
Typical infra	Strong GPUs, dedicated inference tier, higher idle cost	Smaller GPUs or CPU-friendly paths where model size allows	Vendor-managed; minimal local compute
Data boundary	Traffic stays in environments you control (subject to your config)	Same when deployed privately; training data stays under your policies	Data leaves your network per provider terms unless contractually restricted
Cost profile	CapEx / reserved GPU; predictable at steady load	Lower per-token compute; scales with narrow workloads	OpEx per token; spikes map directly to bills
Governance	Review packs for internal LLM use; logging and access control in your stack	Similar; often simpler blast radius due to smaller models	Depends on DPA, region, and subprocessors

Typical stack concerns

GPU planning: throughput vs. latency targets, batching, and headroom for peak loads.
Serving: vLLM, TGI, or your org’s standard inference layer; quantization where quality allows.
Auth and tenancy: who can call which model; quotas and audit trails.
Observability: request tracing, token and latency metrics, error budgets.
Updates: model versioning, rollback, and change windows aligned with your CAB process.

Architecture diagram

This page keeps a static architecture diagram with a long text description. Interactive pipeline and journey visuals ship under SLM-036 on the homepage, services overview, custom SLM, and hybrid routing.

Client applications call an API gateway for authentication, quotas, and logging; approved traffic reaches a GPU inference tier hosting the private model inside infrastructure you operate or contract. Exact topology follows your standards - this diagram is illustrative.

How this complements Custom SLM

Many programs pair both: SLMs handle high-volume, narrow tasks; a private LLM handles escalation and complex steps. We do not position private LLM as a replacement for every SLM - together they reduce reliance on public APIs while matching cost to task difficulty. Hybrid routing describes how traffic can move between SLM and LLM tiers under policy.

Request a PoC

From our insights

Frequently asked questions

Practical answers for technical buyers; validate resale, SLA, and capacity wording with legal and sales before public launch and paid campaigns.

Private LLM vs. Custom SLM - which first?

Start from workload: repetitive, narrow tasks favor an SLM first; if your blocker is broad reasoning under your boundary, a private LLM pilot may come first. Often both land in sequence with hybrid routing between them.

What hardware do we need?

It depends on model size, concurrency, and latency SLOs. We produce a sizing note with your targets - often A100/H100 class for larger models at production concurrency, with options to start smaller in a lab.

Do you resell GPUs or models?

No. We help you procure and configure against your approved vendors and licenses; we do not resell hardware or model weights.

Can this run air-gapped?

Yes in principle: images and weights are transferred through your process; updates are staged offline. Scope and timelines differ from connected deploys.

How do we evaluate quality privately?

Holdout sets, human review samples, and red-team prompts run inside your environment; metrics feed the same dashboards as production traffic.

How does this connect to hybrid routing?

Hybrid routing sends easy work to SLMs and escalates to your private LLM when policies trigger - see the hybrid routing service page for the routing narrative.

What does a PoC look like?

Single model, single use case, defined SLOs, read-only or synthetic data, and a 4–8 week window with a written handoff for scale-out or stop.

Private inference your security team can review