If you're looking for a Bitdeer alternative, there are a few options worth checking out. Lambda is a flexible cloud computing foundation that lets you provision on-demand and reserved NVIDIA GPU instances for AI training and inferencing. It supports a range of GPUs, including the NVIDIA H100, and offers features like scalable file systems, one-click Jupyter access and pay-by-the-second pricing that make it good for quickly provisioning and managing GPU instances.
Another good option is RunPod, a globally distributed GPU cloud that lets you run any GPU workload with immediate spinning up of GPU pods. It supports a range of GPUs and offers serverless ML inference, autoscaling and job queuing. RunPod offers a CLI tool for easy provisioning and deployment, 99.99% uptime and real-time logs and analytics, and flexible pricing depending on the GPU instance type and usage.
Anyscale is also worth a look, particularly if you want a service that can run on the cloud and on your own premises. It offers workload scheduling, heterogeneous node control and cost savings of up to 50% on spot instances. Anyscale integrates with popular IDEs and offers native support for a wide range of AI models, making it a good choice for AI application development and deployment.
If you want a serverless environment, Cerebrium offers a pay-per-use model for training and deploying AI models. It promises ease of use with features like GPU variety, infrastructure as code and real-time logging and monitoring. Cerebrium's tiered plans and variable usage costs mean it can be a good option for engineers who want to scale their AI projects without breaking the bank.