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

Understanding Egress Fees in AI Infrastructure

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. Read on 3 August 2026, AWS lists $0.09 per GB for the first 10 TB per month of data transfer out to the internet from EU (Frankfurt), Microsoft Azure lists $0.087 per GB for the equivalent first tier from West Europe, and Google Cloud Premium Tier lists $0.12 per GiB for the first 1 TiB to European destinations and $0.11 per GiB up to 10 TiB. At the AWS first-tier rate, moving a 10 TB dataset out of the cloud costs about $920, a significant 'exit tax' that discourages teams from using specialized hardware elsewhere.

Lyceum addresses this by offering S3-compatible storage with no ingress or egress charge. By removing the financial friction of data movement, ML teams can treat their data as a fluid asset, moving it between European data centers in Spain, Paris and the Nordics 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.

The Impact of Data Gravity on Model Training

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 low 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 spin up a cluster in Lyceum's European data centers, 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.

Why Hyperscalers Charge Egress Fees

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. 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. The waivers still carry conditions. AWS's own announcement of 5 March 2024, updated 30 September 2025 and read on 3 August 2026, asks you to apply through AWS Support, allows 90 days to complete the move, and warns that AWS applies additional scrutiny if the same account applies more than once. It also states that you do not have to close your account. None of that helps a team that wants 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.

Comparing GPU Clouds with No Egress Fees

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, runs workloads in European data centers in Spain, Paris and the Nordics, with GPU compute billed per second and no base fee, and does not charge for ingress or egress on its S3-compatible storage. 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:

Compare GPU compute costs alongside egress savings. Use the GPU Pricing Calculator to compare costs across RunPod, Lambda, AWS, GCP, CoreWeave, and Lyceum.

Checkpointing and the Cost of Resilience

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.

EU Sovereignty and Data Transfer Compliance

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 and European processing provides a real advantage. GPU VM and training workloads run in European data centers in Spain, Paris and the Nordics, with no training on customer data and no data retained after processing. Not every product is European by default: a few serverless inference models are hosted globally, so check the region for the specific model before you route regulated data to it. 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, billing alerts may be unnecessary for cost control, but you still need to track where personal 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.

Technical Implementation: Bypassing the Hyperscaler Tax

Transitioning to a zero-egress GPU cloud involves more than just changing a URL in your training script. It requires an orchestration layer that understands the relationship between compute, memory, and networking. Lyceum provides this through a workload-aware scheduler that automatically selects the optimal hardware based on your specific job requirements, whether you are cost-optimized or performance-constrained.

Consider a typical workflow using the Lyceum CLI. An engineer can submit a training job with a single command, naming the entry point script and the machine type. The platform then handles the provisioning and networking, ensuring that the data transfer between the storage layer and the GPU nodes is optimized for the highest possible throughput without hidden costs. The way those charges accumulate is the subject of our piece on egress fees as a hidden cost of GPU cloud.

# Deploying a PyTorch job on Lyceum
 lyceum python run train.py --machine gpu.h100

Because Lyceum predicts runtime and memory utilization before the job runs, engineers can avoid the common problem of overprovisioning. In a traditional cloud environment, you might overprovision a VM just to get higher network throughput (as bandwidth is often tied to instance size). With Lyceum, network performance is decoupled from instance size, allowing you to choose the exact GPU you need without sacrificing data transfer speeds.

Total Cost of Compute (TCC) vs. Hourly Rates

The industry is moving away from simple hourly rates toward a more holistic metric: Total Cost of Compute (TCC). TCC accounts for the fact that a 'cheaper' GPU might take longer to finish a job or incur higher auxiliary costs like egress and storage. For example, an older V100 might be cheap per hour, but its lower Tensor Core throughput and slower interconnects could make the total job cost higher than using a modern H100.

Lyceum's scheduling product predicts memory use and runtime within a node and selects the GPU that fits, which lowers cost per job under standard per-second billing. Prediction is scoped to a single node, so a run spanning several nodes still needs its sizing done by hand. When you combine that scheduling efficiency with storage that carries no egress charge, the savings compound. You are no longer paying for idle VRAM, and you are no longer paying to move the results of your computation.

This shift in perspective is essential for AI team leads and CTOs who are responsible for scaling infrastructure. By choosing a partner that aligns its incentives with your efficiency, you can reinvest the budget typically lost to hidden fees back into your research and development. In the competitive landscape of AI, the ability to iterate faster and cheaper is the ultimate differentiator.

Sources

[1] Amazon Web Services, AWS Data Transfer price list, EU (Frankfurt), published 20 July 2026, read 3 August 2026; [2] Google Cloud, VPC network pricing, Premium Tier internet data transfer out, read 3 August 2026; [3] Microsoft Azure, Bandwidth pricing, West Europe, read 3 August 2026; [4] AWS News Blog, Free data transfer out to internet when moving out of AWS, 5 March 2024, updated 30 September 2025, read 3 August 2026