Crusoe, a vertically integrated AI infrastructure company, announced Serverless Fine-Tuning and Self-Serve Deployments in Crusoe Intelligence Foundry, the managed AI platform for Crusoe Cloud.
Together, these capabilities give data scientists and ML engineers a complete path from proprietary data to production-ready models—on purpose-built AI infrastructure, without the overhead of managing it, according to Crusoe.
Fine-tuning is now a standard part of building with open-source AI models—and as open-weight models catch up to proprietary models, more teams are choosing to customize with their proprietary data and retain ownership of the fine-tuned weights.
Getting started is straightforward, but doing it repeatedly adds up. Idle clusters, hardware failures, and scattered tools slow teams down, and the people who should be improving the model end up troubleshooting infrastructure instead, the vendor said.
Crusoe Serverless Fine-Tuning eliminates infrastructure overhead so engineering teams can focus on model quality. Teams can launch a fine-tuning job in a few clicks—select a base model from a curated library of top-performing open-weight models, upload a custom data set, configure settings with pre-configured best practices, and submit; no dedicated reservation required.
Jobs run on Crusoe's distributed AI-optimized infrastructure with automated recovery and restart if hardware blips are detected, the company said.
"Open models have definitely crossed the quality threshold, while offering unique optimization opportunities with your data, and giving you full control of their lifecycle, " said Erwan Menard, senior vice president of product, Crusoe Cloud. "With Crusoe Serverless Fine-Tuning and Self-Serve Deployments your journey just got easier; fast iteration, predictable cost, and the guarantee that your data and weights stay yours. You shouldn't have to choose between a managed experience and ownership of your model."
Additionally, Crusoe Cloud is expanding the consumption options available for Crusoe Managed Inference and adding Self-Serve Deployments. While Serverless Inference APIs are ideal for early-stage experimentation, Self-Serve Deployments is designed for teams with production-ready workloads, the company said.
Billed by GPU per hour for predictable costs, users can select a base model in Intelligence Foundry, choose an inference profile optimized for throughput or responsiveness, and deploy to production on NVIDIA H100 or H200 GPUs.
Self-Serve Deployments expands the inference options available in Crusoe Intelligence Foundry. Customers can now choose Serverless Inference APIs for quick experimentation, Self-Serve Deployments for production-ready workloads optimized for throughput or responsiveness, or Tailored Deployments for dedicated, custom inference on any fine-tuned or proprietary model with SLA-backed performance.
Serverless Fine-Tuning and Self-Serve Deployments will be generally available soon in Crusoe Intelligence Foundry.
Pricing for Serverless Fine-Tuning is token-based, priced per one million tokens processed. Self-Serve Deployments are billed by GPU hour. For both Serverless Fine-Tuning and Self-Serve Deployments, teams can optionally contract for monthly or volume rates.
For more information about this news, visit www.crusoe.ai.