The Engineer's Guide to GPU Clouds with No Egress Fees

Egress fees are the tolls cloud providers charge for moving data out of their network to the internet or another cloud region. While inbound data transfer (ingress) is almost universally free, outbound transfer is metered and billed, typically on a tiered basis. In a standard web application, egress might be negligible, but in machine learning, the volumes are massive. A single training run for a Large Language Model (LLM) can generate terabytes of telemetry, logs, and model checkpoints.

For instance, saving a 70B parameter model in FP16 format requires approximately 140GB of storage. If an engineer checkpoints the model every few hours and syncs those weights to a local server or a different storage provider, the egress costs can quickly rival the compute costs. Hyperscalers like AWS and Google Cloud typically charge between $0.05 and $0.09 per GB for data transfer to the internet. At these rates, moving a 10TB dataset out of the cloud would cost roughly $800 to $900, a significant 'exit tax' that discourages teams from using specialized hardware elsewhere.

Lyceum Technologies addresses this by offering a zero-egress model. By removing the financial friction of data movement, ML teams can treat their data as a fluid asset, moving it between Berlin-based training clusters and Zurich-based inference nodes without fear of bill shock. This approach is particularly critical for teams that have outgrown their initial hyperscaler credits and are looking for a sustainable, long-term infrastructure partner.

Data gravity is the concept that as datasets grow, they become harder and more expensive to move, attracting applications and services into their orbit. In AI development, data gravity is amplified by egress fees. When your 500TB training corpus resides in an S3 bucket, the cost of moving that data to a more cost-effective GPU provider becomes a barrier to optimization. This forces teams to stay with their current provider even if better hardware or lower compute rates are available elsewhere.

This lock-in effect is a deliberate architectural choice by many legacy providers. By making it free to ingest data but expensive to extract it, they ensure that the entire ML lifecycle—from preprocessing to training and inference—stays within their ecosystem. For ML engineers, this means being stuck with 40% average GPU utilization because the overhead of migrating to a more efficient orchestration platform like Lyceum is deemed too high due to egress costs.

Breaking data gravity requires a provider that treats bandwidth as a utility rather than a profit center. When egress fees are eliminated, the 'weight' of the data vanishes. Engineers can use Lyceum's one-click PyTorch deployment to spin up a cluster in Zurich, pull data from a legacy bucket, and then export the final weights to a private data center without incurring a single cent in transfer fees. This flexibility is the cornerstone of a truly sovereign and agile AI strategy.

From a technical perspective, cloud providers do incur costs for maintaining high-bandwidth network backbones and paying for transit to Tier 1 internet service providers. However, the rates charged to customers often represent a significant markup over the actual cost of bandwidth. Industry analysts frequently point out that egress fees serve a dual purpose: recovering infrastructure costs and acting as a strategic moat against competition.

In early 2024, major players like Google Cloud, AWS, and Azure announced they would waive egress fees for customers who choose to leave their platforms entirely. While this was framed as a move toward 'customer choice,' it was largely a response to the European Data Act, which aims to reduce switching costs. However, these waivers come with strict caveats: you must typically terminate your account, apply for credits in advance, and complete the migration within a 60-day window. This does not help teams who want to run a multi-cloud architecture or frequently move data between different specialized services.

In contrast, a GPU cloud with no egress fees by design, such as Lyceum, does not require you to 'leave' to get the benefit. The zero-fee policy applies to every byte, every day. This supports the modern reality of AI engineering, where data is rarely static. Whether you are streaming training logs to Weights & Biases or pushing checkpoints to a model registry, the network should be an enabler, not a bottleneck or a billing trap.

When evaluating GPU providers, it is essential to look beyond the hourly instance price. The Total Cost of Compute (TCC) includes the instance rate, storage, and networking. A provider might offer a lower hourly rate for an A100 but make up the difference through aggressive egress billing. Specialized GPU clouds have emerged to provide a more transparent alternative to the complex pricing models of hyperscalers.

Specialized providers often use a 'flat' networking model. Because their infrastructure is purpose-built for high-throughput AI workloads, they optimize their peering arrangements to minimize transit costs. Lyceum, for example, operates out of Berlin and Zurich, leveraging high-speed European interconnects to provide zero-egress connectivity. This is particularly advantageous for European enterprises that must comply with GDPR and ensure their data residency remains within the EU.

The following table illustrates the typical differences in data transfer policies between legacy hyperscalers and specialized sovereign providers:

In deep learning, checkpointing is the process of saving the state of a model (weights, optimizer state, and metadata) during training. This is vital for recovering from hardware failures or preemptions. For large-scale training runs, checkpoints are frequent and massive. If you are training a Llama-3-70B model, a single checkpoint can exceed 150GB. If your training script saves a checkpoint every 500 steps, you could easily generate several terabytes of data per day.

If these checkpoints are stored in a separate region or exported for local evaluation, the egress fees become a dominant part of the operational budget. Engineers often find themselves in a trade-off: save checkpoints less frequently to save money, or save them frequently to ensure resilience but pay a heavy network tax. This is a false choice that hinders engineering best practices.

By using a GPU cloud with no egress fees, teams can implement aggressive checkpointing strategies without financial penalty. Lyceum's platform, which features auto-detect memory bottlenecks and precise predictions of memory footprints, allows engineers to optimize their training loops for performance rather than cost-avoidance. You can use standard PyTorch utilities like torch.save(model.state_dict(), 'checkpoint.pt') and sync the resulting files to any destination globally, knowing that the cost of that transfer is zero.

For European companies, the movement of data is not just a financial issue; it is a legal one. Under GDPR and the rulings of the European Court of Justice (such as Schrems II), transferring personal data or sensitive intellectual property to non-EU jurisdictions involves significant compliance overhead. Many US-based cloud providers, even those with European regions, are subject to the US CLOUD Act, which can conflict with EU data protection standards.

A GPU cloud with no egress fees that is also EU-sovereign provides a unique advantage. Lyceum's infrastructure in Berlin and Zurich ensures that data never leaves the EU unless explicitly directed by the user. Because there are no egress fees, there is no financial incentive for the provider to keep your data 'trapped' in a specific region. This aligns with the 'GDPR by design' philosophy, where the user has total control over their data's lifecycle and location.

Furthermore, the lack of egress fees simplifies the auditing process. When data movement is free, you don't need complex billing alerts to track where data is going for compliance reasons. You can focus on the technical implementation of secure data pipelines, using Lyceum's CLI tool and VS Code extension to manage workloads across sovereign nodes with the same ease as a local machine.