Pre-training, fine-tuning, and evals platform
Build, evaluate, and deploy specialized custom models from a plain-text prompt in hours instead of months
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What is Pre-training, fine-tuning, and evals platform?
Oumi is an AI‑native custom model development platform that lets developers and organizations build, evaluate, and deploy specialized models from a plain‑text prompt in a matter of hours instead of months. It focuses on creating task‑specific models that consistently outperform generic frontier models on targeted use cases while running at a fraction of the cost and latency. The platform automates the key stages of the model lifecycle, including evaluation of current models, failure‑mode analysis, synthetic data generation, training, and production deployment, so teams can iterate rapidly without needing a full ML engineering stack.
Key features include automated evaluation of both frontier and fine‑tuned models against your specific task, automatic synthesis of high‑quality training data from identified failure modes, and one‑shot training pipelines that can produce competitive models in three iterations or less. It supports both text and multimodal open‑source models, runs on your own infrastructure or via managed providers, and is designed to be fully self‑hosted or cloud‑based depending on your needs. Oumi is aimed at developers, ML teams, and enterprises that want to own their models, avoid vendor lock‑in, and maintain control over data, weights, and deployment environments.
The platform is built around the idea that the ‘right’ model for a given task is usually smaller, cheaper, and more accurate than a massive frontier model, enabling up to 50% higher accuracy and 90% lower inference cost on domain‑specific workloads. It also emphasizes long‑term maintainability, so when new base models arrive you can quickly fine‑tune on top of them instead of rewriting your entire stack. This makes Oumi particularly attractive for regulated industries, financial institutions, and enterprises that must keep data on‑premise and want to future‑proof their AI investments.
Pre-training, fine-tuning, and evals platform pricing
Pricing model: Freemium
Oumi offers three main tiers: an open‑source, self‑hosted library that is free forever; a Pro Platform with a free tier and pay‑as‑you‑go pricing; and custom‑priced Enterprise plans. The self‑hosted open‑source tier includes pre‑training, fine‑tuning, evaluation, data synthesis, and curation that can run anywhere from a laptop to cloud infrastructure. The Pro Platform starts with free credits (up to about 50 dollars for corporate and 25 dollars for personal accounts) and then charges starting at 25 dollars per month with pay‑as‑you‑go usage afterward, giving access to automated evaluation, synthesis, training pipelines, deployment to chosen inference providers, and expert support. Enterprise plans are individually scoped and priced, including dedicated experts, domain‑specific model and agent tuning, and bespoke engagements for large organizations.
Pre-training, fine-tuning, and evals platform pros
- Builds production‑ready models from a prompt in hours
- Automates evaluation of frontier and fine‑tuned models
- Identifies and scores failure modes by category and severity
- Automatically generates targeted training data from failures
- Supports both text and multimodal open‑source models
- Trains task‑specific models that outperform generic frontier models
- Reduces inference cost by up to around 90% vs frontier models
- Cuts latency by using smaller, optimized custom models
- Fully self‑hostable with no vendor lock‑in on weights
- Runs on your own infrastructure or chosen inference provider
- Keeps all data and models under your control
- Automates the full model development lifecycle end‑to‑end
- Short feedback loop from prompt to deployed model
- Designed for enterprise‑grade security and compliance
- Supports continuous improvement with production feedback loops
Pre-training, fine-tuning, and evals platform cons
- Newer platform with less mature ecosystem than incumbents
- Learning curve for teams unfamiliar with finetuning workflows
- Cloud‑based Pro tier adds recurring monthly costs
- Self‑hosted setup requires existing ML infra or DevOps effort
- Enterprise pricing is opaque and custom‑scoped
- Limited documentation for some advanced features
- Heavily optimized for open‑source models, less for proprietary APIs
- Requires clear task definition and evaluation criteria to get best results
Frequently asked questions about Pre-training, fine-tuning, and evals platform
What is Oumi and what problem does it solve?
Oumi is an AI‑native custom model development platform that helps teams build, evaluate, and deploy specialized models from a plain‑text description of their task in hours instead of months. It solves the problem of relying on generic, expensive frontier models that are not optimized for specific workflows by enabling fully owned, task‑specific models that are more accurate, cheaper, and lower latency while avoiding vendor lock‑in.
How does Oumi improve model accuracy on my specific task?
Oumi first evaluates how your current models perform on your task, automatically identifies and scores failure modes, then uses those failure patterns to generate targeted training examples. It then trains or fine‑tunes a model on this curated data, which typically takes only a few iterations to surpass the accuracy of generic frontier models on your particular use case.
Can I run Oumi on my own infrastructure?
Yes, Oumi offers a self‑hosted open‑source library that can run on your own hardware, from a developer laptop to on‑premise clusters or private cloud environments. You retain full control over data, models, and deployment, and can integrate with your existing infrastructure and tooling without depending on Oumi’s hosted services.
What types of models and data does Oumi support?
Oumi supports both text and multimodal open‑source models, enabling pre‑training, supervised fine‑tuning, and evaluation at various scales. It handles a wide range of data types through its dataset framework, including chat‑style instructions, preference signals, and custom formats, and can work with both small custom datasets and large web‑scale corpora.
How long does it take to get a model into production with Oumi?
From a clear prompt describing your task, Oumi can typically move through evaluation, data synthesis, training, and deployment in a matter of hours rather than months. Many teams report going from proof of concept to a production‑ready, custom model within a few training iterations, often under two hours of active effort spread across the pipeline.
Is Oumi suitable for regulated industries such as banking?
Yes, Oumi is designed with regulated industries in mind, allowing institutions to keep proprietary data on‑premise while building and deploying custom models that match or exceed frontier model quality. Several case studies highlight use in large financial institutions that need strict data governance, cost control, and long‑term ownership of their AI systems.
What is the difference between the self‑hosted and Pro tiers?
The self‑hosted tier is an open‑source library free forever, giving you full code access and the ability to run everything on your own infrastructure. The Pro Platform adds managed pipelines, automated evaluation and synthesis workflows, hosted compute, deployment to your choice of inference provider, and expert support, with a free‑credit start and 25 dollars per month pay‑as‑you‑go pricing afterward.
How does Oumi help with data scarcity?
Oumi mitigates data scarcity by automatically turning identified failure modes into synthetic training examples tailored to your task. You review and approve these examples, so you get a high‑quality dataset without needing large annotation teams, weeks of manual labeling, or a data engineering backlog.
Can I use Oumi with existing Hugging Face or other open‑source models?
Yes, Oumi is designed to work with a variety of open‑source LLMs and multimodal models, including popular families like Qwen, DeepSeek‑R1, Llama, and others. You can plug in existing model checkpoints and use Oumi’s training, evaluation, and deployment tooling to fine‑tune and optimize them for your specific use case.
What support and SLAs are available for enterprise customers?
Enterprise customers receive dedicated Oumi experts embedded with their teams, customized engagements scoped to their goals, and tailored support for building and maintaining domain‑specific models and agents. Exact SLAs, response times, and service levels are negotiated individually as part of the custom enterprise agreement.