LLM fine-tuning services: senior AI pods that train and ship

Ryz provides LLM fine-tuning through dedicated AI pod teams of senior engineers who work in your cloud and repos, alongside your team. The pod first tests whether prompting or retrieval already solves the problem, then builds the training dataset, tunes a hosted or open-weight model, and ships it only if it beats the baseline on your evals. Every engineer is from the top 1% of the tens of thousands we interview, on US business hours.

What we build

Fine-tuning changes how a model behaves: format, tone, classification boundaries, domain shorthand, or the ability of a small model to do one job a large model does at higher cost. It is a poor way to teach a model facts that change, which is a retrieval problem. Typical deliverables:

• A go or no-go baseline. A labeled eval set and scores for prompting, few-shot and RAG on a frontier model, so the fine-tune has a number to beat.
• Training datasets. Instruction and response pairs built from your tickets, documents and expert corrections, with deduplication, PII scrubbing, train/test splits that do not leak and a written data card.
• Supervised fine-tunes on hosted models. Jobs on OpenAI's fine-tuning API, Azure OpenAI or Amazon Bedrock custom models, for the base models each provider supports.
• Parameter-efficient tunes of open-weight models. LoRA and QLoRA adapters on Llama, Mistral or Qwen family models with Hugging Face TRL and PEFT, when you need the weights in your own account.
• Preference tuning. DPO-style training on ranked pairs when the goal is "answer the way our reviewers prefer" rather than one right answer.
• Distillation. Using a large model's outputs, reviewed by your experts, to train a smaller model that handles high-volume traffic at lower cost and latency.
• Serving and routing. Deploying the tuned model behind vLLM, SageMaker or the provider's endpoint, with routing that sends hard cases back to a larger model.
• Retraining pipelines. Versioned datasets, reproducible training runs and a schedule for refreshing the model when your data or the base model changes.

How an engagement works

1. Talk. We look at the task, the failure you are trying to fix, available examples, data restrictions and latency and cost targets.
2. Match. We propose a pod scoped to your stack, typically a tech lead, an ML engineer with training experience and a backend or data engineer, with names and a price.
3. Join. The pod works in your repos, CI and standups, with weekly demos that show eval scores, not anecdotes.
4. Grow. You add people, expand to more tasks, or take the pipeline over with documentation and the eval suite.

Week 1 is data access and the baseline: collecting examples, writing the scoring rubric with your experts and measuring prompting and RAG. Month 1 usually brings a cleaned dataset, a first training run and a side-by-side comparison against the baseline, including cost per call. Month 3 is production: the tuned model serving real traffic behind a feature flag, monitoring for drift and a documented retraining process. If the baseline already wins, we tell you, and the pod moves to the approach that does.

The stack our teams work in

How we make sure a fine-tune is worth it

A fine-tuned model can look better in a demo and be worse in production. These are the failure modes our pods guard against:

• Fine-tuning to add knowledge. Facts trained into weights go stale and are hard to cite. If the problem is missing information, we build retrieval instead. See RAG vs fine-tuning.
• Test set contamination. Near-duplicates of eval examples in the training data inflate scores. We deduplicate across splits with fuzzy matching and hold out examples by customer, time period or document, not at random.
• Overfitting and catastrophic forgetting. A model tuned hard on one format can lose general reasoning or safety behavior. We track validation loss, keep learning rates and epochs conservative and run a regression suite of general and safety prompts alongside the task evals.
• Label noise. If two experts disagree on 20% of labels, the model learns the disagreement. We measure inter-annotator agreement on a sample and fix the rubric before scaling labeling.
• Sensitive data in weights. Models can memorize training text. We scrub PII and secrets before training and test for memorization of canary strings.
• Base model churn. Hosted base models get deprecated. We keep datasets and training configs versioned so a tune can be rebuilt on a newer base, and re-run the evals when it is.
• Hidden cost. Training is cheap next to serving dedicated GPUs around the clock. We compare total cost per thousand calls for the tuned model against a frontier model with a good prompt.

Our pods have shipped AI systems to production for enterprises, including an AI driver-support agent that covers about 218,000 driver calls a year in three languages. See the case studies for details on that and other work.

Team shapes and cost

Typical Ryz cost is $7,000 to $15,000 per engineer per month. Mid-level engineers run $7,000 to $10,000, seniors $10,000 to $15,000 and leads $15,000+, quoted per team. GPU and API costs are billed by your cloud provider and are separate.

• Feasibility pod: tech lead + 1 senior ML engineer. $15,000+ plus $10,000 to $15,000 is about $25,000 to $30,000+ per month. Good for building the eval set and deciding whether to tune.
• Training pod: tech lead + 2 senior ML engineers + 1 data engineer. $15,000+ plus three seniors at $30,000 to $45,000 is about $45,000 to $60,000+ per month. Good for dataset work, training and serving one or two tuned models.
• Production pod: about 7 senior engineers including a tech lead, ML, data and backend engineers. 7 × $10,000 to $15,000 is about $70,000 to $105,000 per month, plus the lead premium. Good for several tuned models, routing and retraining pipelines.

Project cost is team size × duration × monthly rate. A training pod at about $50,000 per month for three months comes to roughly $150,000. Quotes are scoped per team, and you get a plan, a price and the names of the people before you start.

Dedicated team or staff augmentation?

Choose an AI pod team when you want a group that owns the outcome: dataset, training, evals and serving, shipped to production. Choose staff augmentation when your ML team already has the pipeline and needs more hands. Senior fine-tuning engineers or machine learning engineers work on your team, under your leads.

When Ryz isn't the right fit

If you want a proprietary model platform or a vendor that sells a pre-trained industry model, a platform vendor fits better. If you need a quick hourly gig to run one training job, try a freelance marketplace. If you need coverage on European or Asian hours, a global network is a better match.

Related

• AI development services
• RAG development services
• LLM development services
• MLOps services
• Hire LLM evaluation engineers
• OpenAI vs Anthropic for enterprise

FAQ

How much does LLM fine-tuning cost?

Team cost is typically $7,000 to $15,000 per engineer per month, mostly senior. A training pod of a lead, two ML engineers and a data engineer is about $45,000 to $60,000+ per month. Compute and API usage are separate and for LoRA-scale work they are usually a fraction of team cost. You get a scoped plan, price and names before you start.

How fast can a fine-tuning project start?

After the scoping call we propose a team. Most of the timeline depends on scope and your onboarding, especially how fast the pod can access examples and experts who can label them.

How much training data do we need?

It depends on the task. Narrow format or classification tasks can improve with hundreds to a few thousand high-quality examples. Quality and consistency matter more than volume, so we start by labeling a small set carefully and measuring.

Should we fine-tune or use RAG?

Use RAG when the model needs facts that change or must be cited. Fine-tune when the model knows enough but behaves wrong: format, tone, labels or cost. Many production systems use both.

Can we fine-tune Claude?

Anthropic does not offer a general fine-tuning API on its own platform. Fine-tuning for some Claude models has been available through Amazon Bedrock in certain regions. We check current availability for your account before planning around it.

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