We agree which project data is in scope, who may access it and which network connections the work needs. That boundary shapes the model, document index, application, logs and backups.
Private physical computers for the work
Clutch has private physical computers available for projects that need local computing. We agree whether a machine or isolated environment is reserved for the project, what hardware is available, and what throughput and response time the work needs. Model size, quantization, concurrency and context length all affect memory, speed and quality; a local model is not a drop-in guarantee of cloud-scale capacity.
Keep inference and project knowledge local
Where the chosen hardware and model allow it, prompts and model inference run on the private computer. Project documents can be parsed, embedded and indexed inside the same agreed boundary, then retrieved for a response with source references. Access controls define who can add, search or remove material. We test retrieval on representative questions, because a confident answer is only useful when the right source was available and selected.
Make the data boundary testable
A fully local setup can keep project data 100% inside the agreed boundary if external model calls, telemetry and cloud sync are disabled. We gate outbound traffic and verify network behavior; this confirms the data path, not universal security.
- User
- Permissions
- Local retrieval
- Local model
- Review
Each stage stays inside the agreed project boundary unless a specific external path is approved and verified.
Choose the model with samples, license and hardware in mind
Open-weight candidates such as Qwen and DeepSeek may fit some private workloads, subject to the exact release license, hardware, latency and quality requirements. The Qwen quickstart describes local inference and deployment options; the DeepSeek-V3 repository publishes its model and local-running information. Those references do not establish that every model fits a given machine. We check the selected weights and license, estimate resource needs, and benchmark the client's own examples before committing to an architecture.
We can shape the workflow in ways analogous to those used with a hosted assistant such as Claude Opus 4.8: retrieve context, call approved tools, check intermediate work and ask a person to review. That is a workflow comparison, not evidence of equal model quality. The chosen local model must pass the agreed sample tasks, and a human remains responsible for consequential output.
- Map List the data, users, actions and external connections in scope.
- Benchmark Test candidate models and retrieval on approved client samples.
- Configure Set permissions, local indexes, logs, backups and gated network routes.
- Verify Inspect the release configuration and test that traffic follows the documented boundary.
Operate it as a real system
A local model still needs updates, access reviews, disk capacity, backup checks and a plan for hardware failure. We configure application and retrieval logs inside the agreed boundary, choose what those logs contain and who can read them, and test restoration from backup. Model updates are evaluated against the acceptance sample before they replace a working version. These operating tasks make the setup understandable after the initial build.
We document the local data boundary and verify the configured network paths. Device security and day-to-day operations still matter.
Integrations without a fixed catalog
External integrations are optional. We can connect Slack, Telegram, WhatsApp, email, Jira and your own systems through approved connectors, without a fixed catalog. Sensitive documents, model processing and local search stay in the private environment. You choose which outputs may leave it; external channels receive only those approved outputs. For a fully local setup, communication and integrations remain inside the agreed private network.
For other product and workflow options, return to the AI Factory Agency. To discuss a specific data boundary or workload, contact Clutch Developer.
Private AI computing in 2026: what stays on your machines
Private AI is an architecture choice about where information is processed and stored. Clutch has dedicated physical computers available for scoped deployments. A fully local design can keep inference, retrieval, application logs, and backups inside an agreed private boundary, provided those components are configured to avoid external APIs and telemetry.
Map the whole data path
- Inputs: the documents, records, prompts, users, and access permissions the workflow needs.
- Processing: the model runtime, search index, application, and any queues or temporary files.
- Operations: administrator access, monitoring, updates, network routes, retention, and backup locations.
A local model alone does not make a system private. Check whether the user interface, error reporting, analytics, model downloads, remote support, or backup job sends information elsewhere. Define who can administer the machines, how patches are installed, how long prompts and answers are retained, and how data is restored or deleted. If an external service is required, document exactly what leaves the boundary and why.
- User input
- Local retrieval
- Local inference
- Private logs and backup
- Approved response
This boundary applies only when every connected component is configured and operated within it.
Choose a model by testing the workflow
Open-weight options such as Qwen and DeepSeek can be evaluated for local inference, subject to each model's license, hardware needs, and chosen deployment method. The Qwen quickstart documents local inference paths; the DeepSeek-V3 repository describes local running options and its model license. These references do not prove that a model fits your data or workload.
If your team would otherwise use Claude Opus 4.8 for a workflow, compare candidates on your own representative prompts, documents, languages, and failure cases. Check answer quality, latency, throughput, operating effort, and cost on the actual hardware. This is a suitability test for a particular task; it is not a claim of equal performance or that Opus runs locally.
A hypothetical boundary
Imagine a design firm asking an assistant to search confidential project notes. A scoped setup might place the notes index, inference service, audit log, and encrypted backups on Clutch's dedicated physical computers, with named administrators and documented retention. A diagram of the intended boundary, network review, restore test, and access audit would be needed before calling it operational.
No deployment deserves a universal 100% security promise. Misconfigured access, compromised devices, weak credentials, vulnerable software, insiders, and mistakes in backup handling remain risks. Discuss a private computing scope alongside the wider AI services, then contact Clutch. Custom solutions start at €10,000 or scoped rental at €1,000 per month; taxes may apply, and each quote defines capacity, setup, hardware, third-party costs, and responsibilities.
FAQ
What does a private AI setup cost?
The starting prices are €10,000 for a private setup project and €1,000/month for monthly capacity rental. Your quote depends on scope, capacity, setup, hardware, external provider costs and applicable taxes.
Which projects are a good fit for local AI?
It can fit work that needs local inference or controlled document retrieval and has a defined user group, task and acceptance sample. Hardware limits, response time and maintenance must also fit. We benchmark first; some workloads are better served by a managed model or a hybrid design.
Does private computing guarantee complete privacy or security?
No setup offers a universal guarantee. A fully local data path can keep data inside the agreed boundary when external calls, telemetry and cloud sync are disabled, outbound access is gated and the configuration is verified.
How much work or how many users can it support?
Capacity depends on model size, available hardware, concurrent requests, document volume and acceptable latency. We test a representative load and agree a supported capacity and upgrade path rather than infer it from a model name.