LLM Development Services

Hosted API models from providers such as Anthropic, OpenAI and Google give the strongest general reasoning with no infrastructure to run, and they improve without any work on your side. The trade-offs are per-token pricing, dependence on a vendor's roadmap and retention policies, and data leaving your environment - though enterprise agreements and cloud-hosted versions of these models can keep traffic within your chosen cloud region.

Open-weight models such as Llama, Mistral, Qwen or Gemma can run on your own servers or private cloud, which gives full control over data, versions and latency, and can be cheaper at high, steady volume. In exchange you take on GPU capacity, serving, scaling and upgrades. Many systems mix both: an open-weight model for high-volume or sensitive steps, an API model for the hardest reasoning. We keep the application behind a thin model-access layer so switching models later is a configuration change, not a rewrite.

We work up a ladder. First, careful prompting with clear instructions, examples and structured output schemas - this solves more problems than people expect. If the model lacks your facts, add retrieval so it reads relevant passages from your documents at query time. If it has the facts but not the behaviour - a strict output format, a house style, a narrow classification task - consider fine-tuning.

Fine-tuning is also useful for distillation: training a smaller, cheaper model to match a larger one on a specific task once you have enough good examples. It is rarely the right way to teach a model new knowledge, because facts baked into weights go stale and cannot be cited.

Every LLM project starts with an evaluation set: real inputs with expected outputs or grading rules, covering common cases, edge cases and inputs the system should refuse. Scoring combines exact checks where possible, model-graded rubrics for open-ended answers, and human review of samples. The same suite decides which model to use, whether a prompt change helped, and whether a new model version is safe to adopt - so decisions rest on measurements rather than a few impressive demos.

Most LLM cost and delay comes from tokens and model size. Practical levers include routing simple requests to smaller models, trimming and caching long system prompts and shared context, retrieving fewer but better passages, streaming responses so users see output immediately, batching offline jobs, and caching answers to repeated questions. On self-hosted models, quantisation and efficient serving engines raise throughput on the same hardware. We measure cost and latency per request in production, so optimisation targets the calls that actually dominate the bill.

Options range from public APIs with zero-retention or enterprise terms, through models hosted inside your own cloud account, to self-hosted open-weight models in your VPC. The right choice depends on what data the prompts contain, where it must reside, and which regulations apply. Whatever the hosting, we redact or mask personal data where it is not needed, apply access controls to retrieved documents so users only see what they are entitled to, and log prompts and outputs according to your retention policy.