LLM development services from senior AI pod teams

Ryz Labs delivers LLM development through dedicated pods of senior engineers who build applications and pipelines on large language models: model selection, prompt and context design, evaluation suites, routing, fine-tuning when it pays off, and self-hosted open-weight models when data or cost requires it. The pod works in your cloud and repos on US business hours, and our engineers are the top 1% of the tens of thousands we have interviewed.

What we build

LLM development covers the layer below a single feature: the choices about which model runs, where, with what context, and how you know it is working. Typical work:

• LLM application backends. Services that assemble context, call models with structured outputs and tools, stream responses and record every call for review.
• Model selection studies. Candidate models from Anthropic, OpenAI and open-weight families compared on your eval set for quality, latency and cost per task.
• Model routing. A small, fast model handles easy requests and escalates hard ones to a larger model, based on measured confidence or task type.
• Self-hosted open-weight models. Llama, Mistral or Qwen family models served with vLLM or Text Generation Inference on GPU instances in your VPC, for data residency or high-volume cost control.
• Fine-tuning and distillation. LoRA or provider fine-tuning to teach a format, tone or narrow task, or to move a workload from a large model to a cheaper one. Details on our LLM fine-tuning page.
• Evaluation suites. Task-specific test sets, reference answers, rubric graders checked against human labels, and regression runs in CI.
• Guardrails. Input and output checks for PII, prompt injection, off-topic requests and policy violations, placed where they cost the least latency.

How an engagement works

1. Talk. We cover the task, data sensitivity, expected volume, latency budget and any constraints on which providers or regions you can use.
2. Match. We propose a pod, often LLM engineers, a backend engineer, an ML or MLOps engineer for hosting, and a tech lead, with names and a price.
3. Join. The pod works in your repos and cloud, attends standups and shares eval results every week, so model decisions are visible to your team.
4. Grow. Reuse the eval harness and serving stack for the next use case, or hand everything over to your platform team.

In week 1, the team builds the first eval set from real inputs and runs two or three candidate models against it to set a baseline. By month 1, there is typically a working service on the chosen model with traces, cost per request and scores tracked in CI. By month 3, typical work is optimization: routing, caching, fine-tuning or self-hosting if the numbers justify it, plus production rollout. Your scope and onboarding decide the pace.

The stack our teams work in

How we keep LLM quality measurable

The biggest risk in LLM development is not a bad model, it is not knowing whether a change made things better or worse. What a senior team builds in:

• Vibes-based iteration. Tweaking prompts by reading a few outputs creates regressions you never see. Every prompt, model or context change runs against the eval set before merge.
• Unreliable LLM judges. Model-graded evals are useful but biased toward longer and more confident answers. We calibrate graders against human labels and use exact checks wherever the task allows.
• Context overload. Bigger context windows tempt teams to send everything. Irrelevant context lowers accuracy and raises cost, so we measure retrieval quality and trim context deliberately.
• Self-hosting math done wrong. GPUs cost money whether or not they are busy. We compare per-token API pricing with GPU cost at your real utilization before recommending self-hosting.
• Model deprecations. Providers retire model versions. Versions are pinned, and the eval suite makes migration a measured change instead of a surprise.
• Prompt injection and data leakage. Untrusted input is separated from instructions, outputs are filtered for sensitive data, and tool permissions are scoped.
• Latency tails. Averages hide the slow requests users notice. We track p95 and p99 latency, stream tokens and set timeouts with fallbacks.

Our pods have run LLM systems at production volume, including an AI voice platform that has placed more than 1M outbound calls. See the case studies.

Team shapes and cost

Ryz engineers typically cost $7,000 to $15,000 per engineer per month. Mid-level (comparable to Amazon L5) is $7,000 to $10,000, senior (comparable to Amazon L6) is $10,000 to $15,000, and leads are $15,000+.

• LLM pilot pod: a tech lead plus 2 senior LLM engineers. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
• Production pod with hosting: about 7 senior engineers, including a tech lead, an ML engineer for serving and fine-tuning, and backend engineers. About $75,000 to $105,000+ per month.
• Added specialist: 1 senior LLM engineer on your team at $10,000 to $15,000 per month.

Project cost is team size × duration × monthly rate. Before anything starts, you get a scoped plan, a price and the names of the people.

Dedicated team or staff augmentation?

Choose an AI pod team when you need an LLM system designed, measured and operated by one accountable team. Choose staff augmentation when you have the architecture and need depth: hire LLM engineers, fine-tuning engineers or LLM evaluation engineers who work on your team.

When Ryz isn't the right fit

Pretraining a foundation model from scratch is a research-lab project, not what our pods do. If you want a proprietary LLM platform to license, talk to platform vendors. If you need engineers in European or Asian time zones, a global network fits better.

Related

• RAG development
• LLM fine-tuning
• OpenAI vs Anthropic for enterprise
• RAG vs fine-tuning
• AI development services
• Hire Anthropic Claude developers

FAQ

Should we use a hosted model or host our own?

Most teams should start hosted, through a provider API or Bedrock or Azure OpenAI, because it is faster and you pay only for use. Self-hosting an open-weight model makes sense for strict data residency, very high steady volume or a narrow task a smaller model handles well. We run the numbers on your workload.

Do you build custom LLMs?

We adapt existing models through prompting, retrieval and fine-tuning, and we host open-weight models. We do not pretrain foundation models from scratch, which very few companies need.

How much does LLM development cost?

Ryz engineers typically cost $7,000 to $15,000 per engineer per month, with leads from $15,000. A three-person pilot pod is about $35,000 to $45,000+ per month, and total cost is team size × duration × monthly rate. Model usage and GPU costs are separate and run on your own cloud or provider accounts.

How soon can an LLM team start?

After the scoping call we propose a team with names. Most of the timeline depends on scope and on how quickly your team can grant access to repos, cloud accounts and sample data.

How do you choose between Claude, GPT and open-weight models?

By testing them on your eval set for quality, latency and cost, then picking per task. Many systems use more than one model, with code kept portable so you can switch later.

Questions we didn't answer? Email info@ryzlabs.com.