Cloud Migration
Moving AI workloads off hyperscalers: AWS exit paths, expired-credit playbooks and bring-your-own-cloud questions.
14 articles
Articles
1 September 2026
When AWS or GCP Credits Expire: AI Migration Playbook
When startup cloud credits expire, AI product companies face a sudden surge in infrastructure costs. Moving to open-weight models on serverless inference cuts per-token spend sharply and lets you pick models hosted in European data centres.
9 February 2026
High-Performance Alternatives to AWS SageMaker for AI Teams
AWS SageMaker AI combines compute with managed development, training and deployment features. A cheaper alternative depends on which of those features your team uses and the engineering work needed to replace them. Compare matched hardware, region, purchasing term and utilization, then include storage, transfers, migration and operations. Specialized GPU clouds can be an alternative for containerized training or inference, while teams that rely on SageMaker Pipelines, data tooling or managed endpoints may value the integrated service. Lyceum publishes this article and competes in this market.
23 February 2026
AWS Credits Expired: A Strategic Guide for AI Infrastructure
For many AI scaleups, the expiration of AWS Activate credits, or of Google for Startups and Microsoft for Startups credits, marks the end of the 'experimentation phase' and the beginning of the 'optimization phase.' During the credit period, efficiency is rarely a priority; engineers often overprovision A100s or H100s for simple tasks because the cost is abstracted away. However, once the first real invoice arrives, infrastructure shifts from a line item to a primary driver of Cost of Goods Sold (COGS). This transition, often called the cloud cliff, demands a rigorous technical audit of your stack. Moving forward requires more than just cost-cutting; it necessitates a sophisticated approach to GPU orchestration, hardware selection, and data residency to maintain competitive margins.
31 August 2026
EU Alternatives to AWS Bedrock and Azure OpenAI
AWS Bedrock and Azure OpenAI offer enterprise familiarity, but hidden egress fees and US CLOUD Act exposure drive up costs and compliance risks. EU-sovereign alternatives deliver strictly GDPR-compliant, OpenAI-compatible infrastructure without the hyperscaler tax.
11 May 2026
Egress Fees: The Hidden Cost of GPU Cloud Infrastructure
You provisioned an H100 cluster based on the hourly rate. Then the invoice arrived, and data transfer charges had overtaken your compute estimate. Here is how to model the true cost of AI infrastructure.
7 May 2026
Migrate ML Workloads from Legacy Clouds to an EU GPU Cloud
Hyperscaler credits expiring? Facing constrained GPU capacity and high egress fees? AI startups are moving to sovereign European infrastructure to regain control over costs and compliance.
4 May 2026
Hyperscaler GPU Alternatives in Europe: The Infrastructure Guide
Expiring cloud credits and chronically underused GPU capacity are breaking unit economics for AI startups. Engineering leaders are migrating to specialized European infrastructure to cut costs and guarantee GDPR compliance.
2 May 2026
Azure GPU Pricing Alternatives 2026
The initial wave of hyperscaler credits has dried up. Discover how AI startups are cutting compute costs while maintaining strict EU data sovereignty.
23 February 2026
Egress Fees GPU Cloud Comparison: The Hidden Cost of AI
For AI teams, the sticker price of a GPU hour is often a distraction from the true cost of operations. Egress fees can add thousands of dollars to a single month of moving massive datasets or model weights between providers, creating a financial moat that stifles multi-cloud flexibility.
23 February 2026
The Engineer's Guide to GPU Clouds with No Egress Fees
Egress fees are a quiet line item on an AI project's budget, and they create a financial barrier to data mobility. For ML teams moving terabytes of checkpoints and datasets, choosing a GPU cloud with no egress fees is a strategic necessity for maintaining cost-efficiency and operational flexibility.
23 February 2026
ML Training Without AWS: A Guide to Sovereign GPU Infrastructure
Hyperscalers often trap ML teams with high egress fees and complex orchestration that leads to chronically low GPU utilization. Transitioning to a sovereign GPU cloud allows for better resource efficiency, support for GDPR compliance, and a significant reduction in the total cost of compute.
23 February 2026
Switching from AWS to a European GPU Cloud: A Technical Guide
Many AI teams find themselves locked into AWS due to initial credits, only to face recurring egress fees and utilization waste later. Transitioning to a European GPU cloud like Lyceum offers higher utilization and European data centers in Paris and Finland, without the hyperscaler tax.
13 February 2026
Migrating from AWS to Dedicated GPUs: A Performance and Cost Guide
Legacy cloud providers often throttle high-performance workloads through hypervisor overhead and restrictive orchestration. For AI engineers, migrating to dedicated GPUs is no longer just a cost-saving measure; it is a technical necessity to unlock the full throughput of H100 and B200 clusters.
11 February 2026
Beyond the Big Three: Optimizing ML Training on Alternative Clouds
Legacy hyperscalers charge a premium for general-purpose infrastructure that often leaves GPUs idle and budgets drained. Moving to specialized ML infrastructure reduces egress fees and eliminates the DevOps tax while maximizing hardware efficiency for large-scale training runs.