DevOps & AI Infrastructure

Private AI Infrastructure

Host your product's AI models and agents where you control the data. We deploy them in your infrastructure or an agreed isolated environment, then evaluate, size, secure, monitor and update them.

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Where your product's AI actually runs

Many AI features send prompts, documents and outputs to a third-party model API. Some products need that processing inside a boundary they control, because of data policy, customer contracts or the need to pin model versions. We host models and agents for your product in your cloud account, on-premises or in an agreed isolated environment, and operate them over time. This is about your finished product. The AI tools we use while building your software are a separate decision.

AI-assisted, expert-led AI infrastructure

How AI assists

  • Scoring candidate models against your evaluation set, with automated and model-graded checks that engineers spot-check.
  • Drafting serving, autoscaling and infrastructure configuration for engineer review.
  • Analyzing load tests and usage data to suggest compute sizing, including whether GPUs are needed at all.
  • Reviewing model and agent logs to surface failures, unusual tool calls and drops in answer quality.

What our experts own

  • Model choice, weighing answer quality, licensing, size and running cost against your use case.
  • Compute sizing: GPUs only when the measured workload needs them.
  • Network boundaries, access control, and what is logged, retained or allowed to leave the environment.
  • Release approval for model, prompt and agent-permission changes, after regression evaluation.

What stays inside your boundary

Illustrative boundary for an internal document assistant; the real lines are agreed with you before anything is deployed.

  • Inside your boundary

    • Prompts, uploaded documents and model outputs
    • Model weights and the servers that run inference
    • Search indexes and embeddings built from your files
    • Logs and evaluation data, kept only as your policy allows
    • Agent tool calls into internal systems, each with scoped permissions

  • Crosses only with approval

    • New model versions, brought in after license and evaluation checks
    • Aggregate usage and latency metrics for an external dashboard
    • Redacted examples shared with a software vendor to resolve an issue
    • Fallback calls to an external model API, if you allow them

  • Stays outside

    • Third-party model APIs and the providers that run them
    • Hosted analytics or error tracking that would record prompt text
    • Vendor telemetry from serving software, switched off where possible

What we set up and run

Hosting, evaluation and operations for your AI

  • Model selection and evaluation

    Open-weight models such as Llama, Mistral or Qwen compared on your own examples for answer quality, speed, licensing and running cost.

  • Serving and scaling

    Inference servers such as vLLM behind an internal API, with request queuing, batching and autoscaling sized to real demand.

  • Right-sized compute

    CPU, GPU or mixed capacity sized from load tests. Smaller or quantized models often do the job; GPUs are added only when the workload needs them.

  • Access and network boundaries

    Private networking, authenticated endpoints, role-based access and controlled outbound connections, so prompts and outputs stay inside the agreed boundary.

  • Logging and monitoring

    Latency, errors, throughput, resource use and answer-quality signals tracked, with logging designed around what may be stored.

  • Model, prompt and agent updates

    Updates released only after regression evaluation, plus deployed-agent operations: tool permissions, approval points, run logs and human takeover.

Who needs privately hosted AI

Your product's AI can no longer send prompts and documents to an outside model API, and nobody on the team has run models in production before.

  • Software vendors whose enterprise buyers won't allow their data to reach outside model APIs
  • Regulated teams that must keep documents and model logs on their own infrastructure
  • Teams building internal assistants over confidential files, such as contracts or HR records

Typical private AI requests

Typical scenarios we scope, not client case studies.

  • Moving a working feature off a model API

    A document summarizer uses a commercial API, and a large customer's contract now forbids it. We would test open-weight models on its real inputs, host one that meets your quality bar in your cloud account and switch over behind the same internal interface.

  • Servers with no internet connection

    A deployment site allows no outbound internet, so models must run on local servers. We would package models, serving software and dependencies for offline installation, and agree how each new version is checked before it is carried in.

  • Debugging without storing prompts

    Policy says prompts may contain personal data and must not be stored, yet failures still need investigating. We would log metadata and quality signals by default, and capture redacted samples only when you approve it, for a limited period.

From requirements to running models

  1. 01

    Define the boundary

    We agree use cases, which data may be processed, where the boundary sits, expected load and the quality bar your evaluation set will test.

  2. 02

    Evaluate and size

    Candidate models are tested on your examples, then compute is sized from load tests. We recommend GPUs only if the results show they're needed.

  3. 03

    Deploy and harden

    Serving, access control, network rules, logging and monitoring are deployed as code, then load- and failure-tested before real traffic.

  4. 04

    Operate and improve

    Monitoring, capacity and access reviews, and model or prompt updates released only after regression evaluation passes.

Two ways to work with AI tools

AI assists with infrastructure code and diagnostics. Choose where it may process your configuration and logs.

Not sure? We'll recommend one during scoping. Compare the packages in detail

What private AI hosting excludes

  • Designing and building the AI feature or agent itself is LLM Integration or Automation Agents; this service hosts, secures and operates the models behind it.
  • Training a custom model is Machine Learning Models, and fine-tuning a language model is scoped under LLM Integration; we host and operate the result.
  • Running the rest of your application, such as web servers, databases and ordinary releases, is Managed DevOps & Operations; this service covers the models and agents.
  • We check model licenses against your intended use, but legal sign-off on licenses and data agreements stays with your own counsel.

How engineering, QA and operations connect

  • AI engineering in your product

    Our AI engineers connect hosted models to your product: retrieval, tool calls, agent workflows and the screens where people review, correct or take over.

  • Evaluation as QA

    QA keeps evaluation datasets and regression checks for answer quality, tool permissions and failure handling, and runs them before every model or prompt change.

  • Design for AI behavior

    Designers shape how AI output appears: loading and fallback states, sources, and the controls people use to correct or override a result.

  • Operations after launch

    Hosting a model is different from running an ordinary app. We keep capacity, access, updates and evaluations under review, with support hours agreed in a support plan.

FAQ

Frequently asked questions

How is this different from the Private / Local AI Engineering package?

Private AI Infrastructure is where your finished product's AI runs: the models and agents your users or staff work with. Private / Local AI Engineering is a development package: it keeps the AI tools we use while building your software on models inside an agreed boundary. The other package, Claude Code / OpenAI Codex Engineering, uses commercial coding agents. Either package can be combined with this service.

Do we need GPUs to run private models?

Not always. Smaller or quantized models can run on CPUs for tasks like classification, extraction or low-volume internal tools. Larger models, long inputs and many simultaneous users usually need GPUs. We size compute from load tests on your real use cases and recommend the smallest setup that meets your quality and speed needs.

Which models can you host?

Open-weight language models such as Llama, Mistral, Qwen or Gemma, embedding and reranking models for search, and task-specific models your team has trained. We compare candidates on your own examples and check each license for your intended use before recommending one.

Is self-hosting cheaper than a model API?

Sometimes. At low or uneven volume, a hosted API under suitable terms is often cheaper and simpler. Private hosting makes sense when data must stay inside your boundary, when you need control over model versions, or when steady volume makes dedicated capacity economical. We compare both against your expected usage before you commit.

Related reading

Keep your product's AI inside your boundary

Tell us what your AI features do and where data must stay. We'll recommend models, hosting and an operating plan to match.

Start a ProjectPlan your private AI setup