Wave of light particles flowing through faint circuit traces on a dark background

Services

LLM Fine Tuning

Custom model behavior for enterprise teams that need consistent output, smaller prompts, and lower inference cost.

LLM fine tuning continues training a pretrained language model on your own examples so it adopts your output formats, tone, and task behavior by default instead of relying on prompt-by-prompt instruction. AlphaCorp AI is an AI engineering studio that scopes, trains, evaluates, and deploys fine-tuned models for enterprises in healthcare, financial services, SaaS, and logistics, using parameter-efficient methods (LoRA, QLoRA, DPO) on open-weight models and supervised tuning on Vertex AI and Amazon Bedrock. With OpenAI winding down its self-serve supervised fine-tuning platform, we build tuned models on weights and platforms you actually get to keep.

Track record

Creators of RustyRAG

Realtime RAG, built in Rust · Sub-200ms end-to-end

10+ years delivering AI solutionsIgnas Vaitukaitis · Founder & CEO

Read RustyRAG’s source before you sign.

Shipped for

VersarWashington, DC
GynisusNew York
CampusReelNew York
LuniqGermany
HospitalityFlowSingapore

The numbers behind LLM fine tuning in 2026

The case for fine-tuning is measured, and so is the urgency. Training compute stopped being the bottleneck three years ago. Data quality is the bottleneck now.

74%of organizations named inaccuracy their top AI risk, up 14 points in a yearStanford HAI, 2026 ↗

Jan 2027OpenAI stops accepting new supervised fine-tuning jobs from any customerOpenAI deprecations ↗

99.3%of ChatGPT's Vicuna-benchmark score, from a QLoRA 65B tune on one 48GB GPUQLoRA, NeurIPS 2023 ↗

Overview

What AlphaCorp AI's LLM fine tuning service covers

We deliver the full fine-tuning stack, from data curation through adapter serving. Six capabilities, each shipped to production.

Supervised fine-tuning on open-weight models

We tune Llama and other open models with torchtune, Hugging Face PEFT, Axolotl, and Unsloth. You own the weights, the adapters, and the dataset.

LoRA and QLoRA adapter training

Base weights stay frozen while small low-rank matrices learn your task, cutting trainable parameters by orders of magnitude. QLoRA's 4-bit quantization fits a 65B model on one 48GB GPU.

Preference tuning with DPO

Direct Preference Optimization turns your ranked outputs into a supervised loss with no separate reward model, teaching the model which of two answers your reviewers actually prefer.

Reinforcement fine-tuning for graded domains

Where outcomes are checkable (codes, citations, calculations), a programmable grader scores sampled answers and the model learns the strategy rather than mimicking static examples.

Managed-platform tuning

Supervised fine-tuning of Gemini on Vertex AI, and Claude on Amazon Bedrock, for teams that need tuning inside their existing cloud tenancy.

Distillation into smaller models

Frontier-model outputs become training data for a cheaper, faster model behind the AI agents and products that cannot afford frontier latency.

03Stack

The Stack We Ship On

We pick the best tool for each job, not the trendiest. This is what runs behind the agents, retrieval pipelines and automation we put into production.

Languages

PythonRustTypeScript

Foundation Models

AnthropicOpenAIGeminiLlamaMistralHugging Face

Fast Inference

GroqCerebrasOpenRouterReplicateOllamavLLM

Agents & Orchestration

LangGraphLangChainLlamaIndexCrewAIn8n

Vector & Memory

MilvusPineconepgvectorChromaWeaviateRedis

Voice, Image & Fine-Tuning

ElevenLabsLiveKitVapiComfyUIPyTorch / LoRAModal

Cloud & Delivery

AWSAzureGoogle CloudDockerKubernetesVercel

Evals & Observability

LangSmithLangfuseWeights & BiasesGrafana

Process

How an AlphaCorp AI LLM fine tuning engagement runs

Every engagement runs the same five stages, and the first one can end the project.

Fit check against RAG and prompting

We confirm fine-tuning is the right tool. Changing factual knowledge points to retrieval-augmented generation; instruction-following gaps often close with prompt engineering at a fraction of the cost.

Evaluation before training

We build the held-out eval set and safety regression suite first, so every later improvement claim is a number instead of an impression.

Data curation over data volume

LIMA (2023) aligned a 65B model with 1,000 curated examples. We cut noisy and contradictory examples, and we screen out genuinely new facts, which the model learns slowly and badly.

Adapter training with epochs costed up front

The training bill that blindsides teams is the epoch multiplier: a 100K-token dataset at three epochs costs three times the quoted per-token price. We put that math in the plan before the first run.

Safety regression, deployment, monitoring

Guardrail evals rerun as a release gate, adapters ship into your serving stack, and monitoring watches for drift.

Benefits

Why invest in LLM fine tuning

LLM fine tuning pays for itself when a smaller tuned model replaces a bigger, slower, pricier one. The evidence is specific.

Small models beating big ones on your task

OpenAI's 2022 InstructGPT study found human raters preferred a fine-tuned 1.3B model over the 175B base model 71% of the time. AWS's 2024 Bedrock benchmark showed fine-tuned Claude 3 Haiku beating un-tuned Claude 3.5 Sonnet by 9.9% on the target task.

Formats that hold without a 2,000-token system prompt

Tuned behavior lives in the weights, so schemas and tone survive prompt edits, model handoffs, and tired engineers.

Lower inference cost and latency

Distilling frontier behavior into a smaller model cuts the per-request bill and response time at the same stroke.

Reasoning gains on gradable work

OpenAI's vendor-reported reinforcement fine-tuning cases include a 39% accuracy improvement on tax analysis and a 20% F1 lift on legal citation extraction. Vendor-reported, worth validating on your own evals, and exactly what stage two of our process exists for.

Why AlphaCorp AI

Why enterprises choose AlphaCorp AI for LLM fine tuning

We turn down fine-tuning projects. That is the differentiator buyers remember, because a studio that recommends retrieval when retrieval wins is one you can trust with training runs.

We say no when RAG wins. Google-affiliated 2024 research found that fine-tuning on new facts trains the model to answer whether or not it knows, pushing hallucination up. Retrieval carries facts. Tuning shapes behavior. Most of our production systems use both.

Portability by design. OpenAI's 2026 phase-out stranded teams whose only customization lived on one vendor's platform. Adapters trained on open weights, or on Vertex AI and Bedrock inside your own tenancy, survive vendor pivots.

Tradeoffs stated before you spend. Baked-in behavior needs retraining to change, and 2024 to 2025 findings show LoRA still trails full fine-tuning on hard targets like code generation. When that gap matters to your task, we price full fine-tuning honestly or steer you elsewhere.

The people you talk to are the people who build. Our remote-first team works US Eastern hours in English, Portuguese, and Spanish, with no handoff between the pitch and the training run.

What LLM fine tuning costs on each platform

Platform compute pricing is public and dated. As of August 2026, OpenAI's remaining supervised fine-tuning runs about $3 per million training tokens on GPT-4o mini up to about $25 per million on GPT-4o, multiplied by epochs, with tuned-model inference at roughly 1.5x the base rate; that product stops accepting new jobs after January 6, 2027. Reinforcement fine-tuning on o4-mini bills about $100 per hour of core training compute at standard inference rates. Vertex AI charges dataset tokens multiplied by epochs and serves tuned Gemini models at the same per-token rate as the stable base model.

Engagement pricing is scoped, since dataset condition drives most of the effort. A short working session with our engineers produces a fixed quote.

How AlphaCorp AI handles data and safety in LLM fine tuning

Your data trains in your tenancy. We run training inside your cloud project (Vertex AI, Bedrock) or on GPUs you control for open-weight work, and the datasets, adapters, and eval suites remain your property at exit.

Safety is a release gate. Qi et al. (2023) showed that fine-tuning on as few as 10 to 100 adversarial examples can strip a model's safety alignment, and that even benign training data degrades guardrails by accident. So we rerun safety and refusal evals after every training run and block deployment on regressions. We also align data reviews with NIST's Generative AI Profile (AI 600-1), which recommends training-data IP and copyright review for any model being fine-tuned.

Bring us the task, the data you have, and the model bill you would like to shrink. Within one scoping call you will know whether fine-tuning is the right tool, what it will cost, and what the eval that proves it will measure.

FAQ

LLM fine tuning FAQs

What is LLM fine tuning and when do you actually need it?

LLM fine tuning continues training a pretrained model on your own examples so it adopts your formats, tone, and task structure by default. You need it when the model's behavior must change: output schemas, domain style, distilled capability in a smaller model. When the problem is missing or changing facts, retrieval is usually the better first move.

How much does LLM fine tuning cost?

Platform compute runs per token multiplied by epochs (roughly $3/M to $25/M training tokens on OpenAI's remaining models, dataset tokens times epochs on Vertex AI), and about $100 per compute hour for reinforcement fine-tuning, per August 2026 vendor pricing. The engagement fee depends on how much curation your data needs, so we price it after a scoping session rather than off a rate card.

How long does an LLM fine tuning project take?

Training itself is fast: the 2023 QLoRA paper tuned a 65B model in 24 hours on a single GPU. The calendar is set by data curation and evaluation, which is why our fit-check and eval stages come first and why scope decides the timeline in the quote.

Should we fine-tune or build RAG?

Build RAG when the problem is knowledge; fine-tune when the problem is behavior. Fine-tuning cannot cheaply track changing facts, and retrieval cannot fix tone or output structure. Enterprise practice in 2026 combines both, and most AlphaCorp AI production systems do too.

Is LLM fine tuning dead now that OpenAI is retiring it?

No. OpenAI is retiring one self-serve product, with new supervised jobs ending January 6, 2027, while pushing reinforcement fine-tuning instead. Gemini tuning on Vertex AI, Claude tuning on Bedrock, and the whole open-weight Llama ecosystem with torchtune and Hugging Face PEFT remain fully active.

Will fine-tuning make our model less safe?

It can, even with entirely benign data, which 2023 research on Llama-2 and GPT-3.5-Turbo demonstrated with as few as 10 to 100 examples. That is why AlphaCorp AI treats post-training safety evals as a hard release gate instead of a checkbox.

What happens after the fine-tuned model ships?

You keep everything: adapters, datasets, eval harness, training recipes. We wire monitoring for drift and quality, define retraining triggers, and can run ongoing MLOps support or hand the stack to your team with documentation. Nothing about the setup requires us to stay.

Updated August 31, 2026