LLM Fine-Tuning and MLOps Services for US & EU AI Teams

Three signals justify fine-tuning. First, latency or cost: a fine-tuned 7B model on vLLM costs roughly 1/40th of GPT-4o for the same task at p95 latency under 400 ms. Second, behaviour you cannot prompt your way into: domain-specific style, structured output adherence above 99%, or refusal patterns that frontier safety layers will not allow. Third, data leverage: you have 5,000+ high-quality labelled pairs that nobody else has. If the answer is mainly knowledge retrieval, do RAG first. If it is occasional formatting, prompt-engineer first. Fine-tuning is the right call when you need behavioural change at scale.

Both. OpenAI fine-tuning (GPT-4o, GPT-4.1, GPT-4o-mini), Anthropic via Bedrock custom models, Google Gemini tuning, and the full open-source stack: Llama 3.3, Qwen 2.5, Mistral, Phi-4, DeepSeek. The choice is engineering, not ideology. Closed models give you faster delivery and zero infra. Open models give you ownership, cheaper inference at scale, and on-premise deployment when compliance requires it. We run the same eval harness against both paths and present the cost-per-token, latency, and quality trade-off in writing before you commit.

Every engagement ships with a versioned eval suite: a golden set of 200 to 1,000 labelled examples curated with the client, automated LLM-as-judge with rubric scoring (Ragas, custom rubrics), task-specific metrics (BLEU, ROUGE, exact match, F1, structured-output adherence), and adversarial probes for hallucination, jailbreak, and PII leakage. Every training run posts to Weights & Biases with the full eval table. CI blocks any merge that regresses the golden set by more than the agreed threshold (typically 2%). The eval suite is yours, version-controlled in your repo, and runs against frontier baselines on every release.

Parameter-efficient methods first: LoRA and QLoRA cut GPU memory by 60 to 80 percent and let us run a Llama 3.3 70B SFT on a single A100 80GB node for under $350 in GPU time. Unsloth and Axolotl give us 2x training throughput vs vanilla Hugging Face TRL. We default to QLoRA 4-bit for first-pass experimentation, switch to full fine-tune only when ablations prove it moves the eval needle. Inference cost is controlled by INT8/INT4 quantization (AWQ, GPTQ), vLLM continuous batching, and speculative decoding. A typical client moves from roughly $20,000/month frontier API spend to about $3,900/month self-hosted inference. GPU compute runs on your own accounts, so you keep the cost lever.

Engagement starts with a mutual NDA and a GDPR-aligned DPA. Training data lives in your cloud account: we work via assumed IAM roles, never copy data to laptops, and the training cluster runs in your VPC (AWS SageMaker, GCP Vertex, Azure ML, or your Kubernetes). For regulated data we sign HIPAA BAAs and run on HIPAA-eligible regions only. PII redaction pipelines (Presidio, custom regex + NER) are part of the data curation step. We are GDPR-aligned, ISO 27001 ready, SOC 2 Type II in progress, HIPAA-capable, and CCPA-acknowledged.

Engagements are fixed-scope and lean, all-in and quoted in USD. An eval PoC runs from $2,900 (4–6 weeks); RAG over your knowledge base from $8,100 when retrieval beats tuning; an autonomous AI agent from $10,400; a fine-tuned model in production from $13,800. The exact number depends on base-model size and whether it is a closed API or self-hosted open weight, how much labelled data exists versus needs curating, the number of training methods in play, inference SLA and quantization targets, and compliance scope. You see the line-item budget at the end of discovery and sign off before any code is written. There is no recruitment markup and no tool surcharges, and GPU compute runs on your own accounts, so you keep the cost lever.

The practical floor for SFT on an instruction-tuned base model is around 500 high-quality examples — enough to move the eval needle reliably. For DPO or ORPO preference alignment, you need at least 1,000 preference pairs (chosen vs rejected). Quality matters far more than quantity: 500 carefully curated, gold-standard examples outperform 5,000 noisy ones. If you have fewer than 200 labelled examples, we start with data curation — synthetic generation with a frontier model, human review and de-duplication — before any training run. The golden set grows as we iterate.

We support the full open-source stack (Llama 3.3, Qwen 2.5, Mistral, Phi-4, DeepSeek, Falcon) and closed-API fine-tuning (GPT-4o, GPT-4.1, GPT-4o-mini, Anthropic via Bedrock, Google Gemini). If you already have a checkpoint — whether a community model, a prior vendor’s fine-tune, or your own internal model — we can evaluate it against your task and continue training from that checkpoint. We run the same eval harness against every candidate and present the cost-per-token, latency at p95, and quality trade-offs in a written memo before you commit to a base model.

Yes. On-premise and air-gapped deployments are a common requirement in regulated industries and government contexts. We deploy fine-tuned open-weight models on your own Kubernetes cluster or bare-metal GPU nodes, using vLLM or TGI for inference serving. Network egress from the training cluster to our infrastructure is zero — we work via assumed roles or bastion access inside your VPC. For air-gapped environments, we ship a reproducible training config, a containerised inference stack, and a documented runbook so your team can retrain and serve without external dependencies after handover.

An Eval PoC runs 4–6 weeks and delivers a go/no-go decision with a versioned eval harness. A full fine-tuned model in production (data curation, SFT, DPO ablations, vLLM inference, load testing, and handover) typically takes 10–14 weeks. RAG-over-knowledge-base engagements run 6–8 weeks. Autonomous agent builds depend heavily on the number of tools and the human-in-the-loop paths, but typically 8–12 weeks. All timelines assume your cloud account is ready and the first batch of training data is accessible in week one. We share a week-by-week milestone plan before signing.