Source URL: https://www.hopsworks.ai/post/migrating-from-aws-to-a-european-cloud-how-we-cut-costs-by-62
Source: Hacker News
Title: Migrating from AWS to a European Cloud – How We Cut Costs by 62%
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text provides a detailed overview of Hopsworks, an open platform for developing and operating AI systems, emphasizing its integration with Kubernetes and its cost advantages over AWS. It discusses the migration from AWS to OVHCloud, highlighting the strategic decision-making associated with infrastructure, cloud costs, and performance optimization.
Detailed Description:
The content focuses on Hopsworks, which stands out as a robust platform for AI system development with significant capabilities and cost advantages. Here are the major points of interest:
– **Hopsworks Capabilities**:
– Designed for scalability on Kubernetes clusters, usable across different environments, including public clouds and air-gapped data centers.
– Acts as an alternative to leading MLOps platforms like AWS Sagemaker and GCP Vertex, boasting higher performance in real-time AI and enhanced Python integration.
– **Core Features**:
– **Lakehouse Layer**: Utilizes Delta Lake and other technologies for efficient storage and retrieval of historical feature data crucial for AI model training.
– **Real-Time Database**: Introduction of RonDB to support low-latency AI workloads with specialized query capabilities.
– **Model Registry**: Facilitates ML model management while supporting deployments with KServe/vLLM, thus aiding ease of integration in AI pipelines.
– **Cost Management**:
– Highlights how Hopsworks serverless provides a freemium model, offering significant storage benefits at no cost while highlighting potential egress cost risks when operating on AWS.
– The move to OVHCloud allowed better control over costs, particularly in terms of egress and overall service affordability.
– **Migration Experience**:
– Smooth transition from AWS to OVH, involving the backup and migration of data with minimal downtime.
– Special emphasis on avoiding cloud-specific services, thereby improving the stack’s portability and resilience.
– Observability and metrics managed in-house utilizing OpenSearch and Prometheus/Graphana, further enhancing operational insight.
– **Strategic Considerations in Cloud Services**:
– Evaluated managed Kubernetes services from both AWS and OVH, weighing pros and cons in terms of maturity, pricing, and service features.
– Emphasis on avoiding egress costs which are notably lower with OVH compared to AWS.
– The advantages of OVH include lower costs for S3-compatible object storage and better pricing paradigms for container registries.
– **Partnerships and Future Collaboration**:
– Collaboration with OVH to provide a sovereign AI platform caters to geographical compliance and data governance requirements.
In conclusion, this text is pivotal for AI, cloud, and infrastructure professionals as it delineates a strategic migration between cloud services focusing on cost-effectiveness and operational resilience, alongside advanced features that facilitate the development of AI systems at scale.