Llama development services
We build self-hosted Llama inference for platform teams that need control over sensitive data and model versions. You get a tested serving stack, measured capacity and quality, and a deployment your team can operate.
01Capabilities
Llama inference under your control
Llama is a family of large language models (LLMs) from Meta. Our AI and machine learning development combines its downloadable weights with the controls needed to serve your task on your infrastructure.
- Boundary
A defined data boundary
We deploy on your hardware or in your own cloud account. We map data flows against your requirements, including for healthcare and fintech applications. That review covers prompts, retrieved material and logs, so the serving boundary accounts for the data around each request.
- Ownership
Task-specific fine-tuning
We fine-tune licensed weights on your material for the vocabulary and document formats the task requires. We evaluate the adapted model against an agreed test set, so the decision rests on task quality.
- Steadiness
Pinned versions and planned upgrades
We pin the selected checkpoint and define a controlled upgrade path. Where a model change requires workflow approval, your team has a specific version to test and approve before the replacement enters service.
- Volume
Measured serving capacity
We measure throughput and hardware utilisation against expected traffic. Batching and quantisation are tested alongside task quality, so the capacity plan and operating-cost comparison reflect the configuration you would deploy.
02Fit
Choosing the inference architecture for your workload
We compare self-hosted and hosted inference against the same task, data requirements and traffic. The recommendation includes who operates the platform and how each option meets the quality target.
| Your situation | What we recommend |
|---|---|
| Data must remain within a defined network boundary | Private inferenceWe assess a self-hosted Llama deployment against the written restriction and its data flows. The requirement determines the serving boundary; it does not select a model on its own. |
| An approved workflow where changing the model means approving it again | A pinned checkpointWe deploy a specified version with a controlled approval and upgrade path, so a change to the model can be tested before it enters your workflow. |
| Traffic is low, variable or still being measured | Hosted API integrationWe compare usage pricing and task quality with the cost of operating your own platform, using the OpenAI API or Claude where a hosted service fits your data requirements. |
| A private deployment needs a platform and an operating owner | Platform build and handoverWe scope the serving stack and operational responsibilities together. Your infrastructure owner joins the capacity and security decisions before deployment. |
| You want to ship the model inside software you sell | Checkpoint licence reviewWe document the exact Llama release and intended distribution for your legal review, and compare the terms with the selected Qwen checkpoint before implementation. |
| One narrow decision, made constantly, with your own examples of it | A task-specific modelWe evaluate a small classifier in PyTorch against the task and test data. Measured quality and serving requirements decide whether it fits better than a general LLM. |
Scope
We own the capacity plan and serving behaviour, including bursts and cold starts. Model precision is a measured quality decision, and the data boundary is part of the design. unicrew has built software since 2012, with 100+ senior in-house engineers across six countries. We are ISO 27001:2022 and ISO 9001:2015 certified, renewed through a multi-stage audit with Quay Audit UK.
03Delivery
Delivering Llama inference your team can run
We turn the deployment requirement into a tested serving plan and clear operating responsibilities.
- Document the deployment requirementWe record the regulation, contract clause or traffic requirement that shapes the architecture. Security and infrastructure owners confirm the allowed data flows, including whether a hosted option can meet the same boundary.
- Measure the workload and modelWe test expected concurrency and peak traffic at the required input lengths and response time. The chosen checkpoint and precision are evaluated on your task, so quantisation is accepted on measured quality.
- Build and test the serving pathWe implement batching and queue backpressure, then test scaling and cold starts. The handover documents load behaviour and operating responsibilities for the stack, packaged with Docker and scheduled with Kubernetes where those fit the deployment.
- Maintain a hosted quality baselineWe run the same test set against a hosted model on a schedule as part of QA and test automation. This keeps the quality comparison current and gives model or infrastructure changes a repeatable evaluation after release.
04Stack
Technology choices around Llama inference
These neighbouring technologies support model evaluation and the platform that serves the selected checkpoint.
05Questions
Planning a self-hosted Llama deployment
These answers cover the cost, licensing and operating decisions to settle before deployment.
Yes. We scope Llama development around capacity, model quality and the serving platform, with responsibilities agreed between our engineers and your infrastructure team.
We can own a scoped build or work within your delivery organisation. The platform scope includes the relevant cloud infrastructure work and an agreed operational owner.
The break-even point depends on measured utilisation, operating costs and the quality your task requires.
We compare expected traffic and context lengths against hosted usage pricing at the quality target. Include idle time, peak capacity and operational ownership in that comparison. Steady traffic alone does not establish a saving.
Llama 3.3 uses Meta's community licence, rather than an unrestricted open-source licence.
Check the exact release's terms, acceptable-use rules and notice obligations with your legal team. Llama 3.3 also has a monthly-active-user condition; other versions need their own review. Distribution inside your product needs the same version-specific check.
Hardware sizing comes from the model configuration and the workload it must serve.
- Checkpoint and serving precision.
- Input and context lengths.
- Concurrent requests and peak traffic.
- Required response time.
We measure memory use and throughput at that configuration, and evaluate quantisation against your task-quality target.
Start with a scoping call and a written description of the deployment requirement.
- Expected traffic and input lengths.
- Task examples and a quality target.
- Available infrastructure and its operational owner.
We return a written recommendation on hosting, capacity and operational responsibilities. Most engagements start within two to four weeks.
Llama work is quoted to the scope and engagement model.
- Time and materials is billed hourly and quoted per project.
- Fixed price is outcome based and quoted per project.
- Team extension is billed monthly per team member.
Plan your private Llama inference platform
Share the written data requirement and expected traffic. We will assess the hosting options, capacity and operating responsibilities with your quality target in view.