Foundation models that are actually yours.
We design and train custom LLMs from the ground up, built on your data, tuned to your domain, and deployed on infrastructure you control. For organizations where off the shelf isn't enough, and sovereignty isn't optional.
Every layer
of the stack.
Training a real model takes more than a fine tuning script. We own dataset curation, infrastructure, training runs, and evaluation, and hand off a model your team can operate with confidence.
Dataset Curation
Sourcing, cleaning, deduplication, and quality filtering at scale, including synthetic data generation when the corpus is thin.
Pretraining at Scale
Distributed training across H100 / A100 clusters with mixed precision, gradient checkpointing, and efficient parallelism strategies.
Domain Adaptation
Continued pretraining on industry specific corpora, legal, medical, financial, scientific, without losing general capability.
Eval Harness Design
Custom benchmarks that test what actually matters for your use case, not just public leaderboards.
Distillation & Compression
Larger teacher models distilled into faster, cheaper students, with measured trade offs on capability.
Sovereign Deployment
Full ownership of weights, training data, and serving infrastructure, including air gapped on premise options.
Reasons a custom model is worth six months.
Almost every AI need is served by an existing model plus good engineering, and our own FAQ says so. The buyers on this page are the exceptions, and each of them has a reason no API or fine-tune can answer.
National and Sovereign Language Models
The most consequential custom training happening right now: nations building models for languages the foundation labs treat as an afterthought. We train models where the tokenizer, the corpus, and the evaluation are designed for your language and your institutions from the first token, built on our multilingual and low resource language practice and deployed on sovereign infrastructure where the weights answer to your jurisdiction alone.
Regulated Enterprises With Institutional Corpora
Decades of legal opinions, clinical records, underwriting decisions, or research archives encode knowledge no public model was ever shown. When the corpus is genuinely massive and genuinely proprietary, continued pretraining or a from-scratch build produces a model that reasons in your institution's terms rather than paraphrasing the public internet, and the data never crosses a third party's boundary on the way.
Companies Building a Model as the Product
If the model is what you sell, renting the intelligence layer means your margins, your roadmap, and your differentiation belong to someone else's API pricing. We train models our clients commercialize: architecture, tokenizer, and training data chosen for the product's economics, with IP ownership of every weight and no upstream license that can change under your product.
When Inference Cost Is the Business Case
At sufficient scale, the cheapest model is a small one that does exactly your task. We train and distill compact models that replace large generic ones on high volume workloads, and the arithmetic is often decisive: a domain-trained small model matching a generic model ten times its size, at a fraction of the serving cost. The same discipline continues after launch through our AI cost optimization practice, because a model trained for cheap inference deserves a serving stack tuned the same way.
Defense and No-Third-Party-Weights Mandates
Some environments cannot accept weights of unknown provenance, whatever the license says. For defense, intelligence, and critical infrastructure clients, we train from initialization with a documented data lineage for every token in the corpus, delivered and operated inside the perimeter, so the provenance question has a complete answer.
The Middle Path Most Buyers Have Never Priced
Between fine-tuning and from-scratch sits continued pretraining: taking strong open weights and pretraining them further on your domain corpus. Published research from NVIDIA's chip design models puts the cost at under 1.5 percent of training from scratch, while moving domain capability in ways fine-tuning alone cannot. A large share of the buyers who arrive at this page wanting a custom model actually want this, and part of our scoping is telling you which one you are.
Almost Nobody Should Pretrain From Scratch. We Say That While Selling It.
The jump from fine-tuning to pretraining is measured in orders of magnitude, thousands of GPU-hours versus millions, which is why almost no one trains from scratch, and why our first FAQ points most visitors to fine-tuning. From-scratch training is the right call for a short list of reasons: sovereignty over provenance, a language or domain the foundation models genuinely fail, or model-as-product economics. If your reason is not on that list, we will tell you in the scoping call and route you to the cheaper path, because a six month engagement built on the wrong premise helps neither of us.
No GPU Spins Before the Eval Exists.
The most expensive failure in custom training is not a bad run, it is a good run against an undefined target: six figures of compute producing a model nobody agreed how to judge. Our first stage builds the evaluation harness before any training starts, with intermediate checkpoints evaluated mid-run and the authority to stop early if the signals are off. Compute is rented. Judgment is what you are actually hiring.
From corpus to production model.
Custom training is a six month commitment, not a six week sprint. We sequence it carefully so you see capability gains before the final run.
Scoping & Eval Design
We define the model's job, the success metrics, and the eval harness, before any GPU spins up.
Data Pipeline
Sourcing, cleaning, tokenization, and quality filtering, building the corpus that defines what the model knows.
Training Runs
Pretraining on your cluster or ours, with intermediate evals and the ability to stop early if signals are off.
Alignment & Handoff
Instruction tuning, safety alignment, and operational handover with monitoring and retraining playbooks.
Training economics, the 1.5 percent secret, and the size question.
Custom training quotes range across two orders of magnitude because the word "custom" hides three very different projects. Here are the real numbers for each, and the decision rules that keep the budget pointed at the right one.
Three tiers, honestly priced. Training a 7B parameter model from scratch runs $50,000 to $500,000 all in, and a 70B from-scratch build runs $1.2 million to $6 million, with frontier scale models costing hundreds of millions and belonging to a different conversation entirely. The number buyers miss: compute is the floor, not the ceiling. Engineering, data acquisition, and annotation routinely exceed the GPU bill by two to three times, which is why a quote built only on GPU-hours is a quote that will double. Our proposals price all three lines, compute, data, and engineering, against the eval harness the model must pass.
The middle path this industry underexplains, possibly because it is less expensive to sell. Continued pretraining takes strong open weights and keeps pretraining them on your domain corpus, legal, medical, financial, or a whole language, so the model absorbs your domain the way it absorbed the internet. NVIDIA's published chip-design research put the cost at under 1.5 percent of pretraining from scratch, tens of thousands of GPU-hours instead of millions, while producing domain capability that instruction tuning alone cannot reach. For most enterprises with a serious corpus, this is the correct answer, and identifying whether you are that buyer is the first thing our scoping does.
Because data preparation is typically 30 to 50 percent of the total cost and close to 100 percent of the outcome. Sourcing, cleaning, deduplication, quality filtering, and tokenizer design decide what the model can know before a single training step runs, and a mediocre corpus trained on a perfect cluster produces a well-engineered disappointment. This is also where thin domains get solved: synthetic data generation, carefully validated, fills corpus gaps that no amount of extra compute can. When a training project fails, the autopsy almost always reads data, which is why our pipeline stage runs before, and often costs more than, the training itself.
Probably smaller, and the evidence is consistent: domain-adapted 7B to 13B models routinely outperform generic models several times their size on the specific task they were trained for, at a quarter of the serving hardware. Every parameter you train, you also pay to serve, forever, so the sizing rule is the smallest model that clears your evaluation, not the largest one the budget allows. We test candidate sizes against your actual eval harness before committing the main run, because the difference between a 13B and a 70B is not just the training invoice, it is every inference bill for the life of the system.
The failure mode is rarely a crashed run, it is a completed one that answers the wrong question: no agreed evaluation, a corpus assembled optimistically, or a size chosen by ambition rather than testing. The protections are procedural and non negotiable in our process: the eval harness is designed and signed off before scoping ends, the corpus is profiled and sampled before the pipeline is built, intermediate checkpoints are evaluated mid-run with authority to stop early, and capability gains are demonstrated on smaller runs before the expensive one. Custom training is a six month commitment, and every gate exists so that no six figure decision is made on faith.
Yes, and it is some of the most meaningful work in this field. The foundation labs optimize for the languages of their revenue, which leaves most of the world's languages with tokenizers that fragment their script and corpora scraped without curation. We have shipped models across more than twenty languages by treating the language as the project: tokenizer design for the script's actual structure, corpus building with native speaker curation where public data is thin, synthetic augmentation validated by humans, and evaluation written in and for the language rather than translated from English benchmarks. Paired with sovereign deployment, the result is a national capability, not a licensed dependency.
Bring a sample of your corpus and the job description for the model. Scoping will tell you whether the honest answer is fine-tuning, continued pretraining, or a from-scratch build, with the price of each, and two of those three answers cost far less than the one you probably arrived expecting.
Questions about
Custom LLM Training
When you need true sovereignty over weights and data, when your domain or language is poorly represented in foundation models, or when your scale makes inference cost the bottleneck. Otherwise, fine tuning usually wins on cost and time.
From 1B to 70B parameters routinely. Beyond that, training cost climbs steeply, we'll walk you through the trade offs vs. distillation or fine tuning a frontier model.
Your cloud, your on premise cluster, or compute we provision. For sovereignty sensitive projects we deploy entirely in your jurisdiction with no data leaving your perimeter.
Six to nine months end to end for most engagements. The training run itself is weeks; the data work, eval design, and alignment phases are where the time actually goes.
Yes. Weights, training data, eval harness, and operational tooling all transfer to you. We're a development partner, not a hosted API vendor.
Stop experimenting.
Start deploying AI that works.
Book a free discovery call. We'll review your data, scope the right model size, and tell you honestly whether custom training is the right move.
info@croncore.com