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GPU cloud bills are determined by the cost of a completed training run or inference result, not by the advertised GPU rate alone. Internet and cross-region egress can add a separate per-GiB charge, while a Spot interruption can discard work, trigger a restart, and leave disks or checkpoints billable. The practical metric is cost per completed job: GPU runtime plus recovery work, data transfer, storage retention, and the operational time needed to finish successfully.
Spot capacity can be worthwhile for fault-tolerant workloads with frequent checkpoints and local data. It is a poor fit for a job that cannot resume cleanly, must export large datasets repeatedly, or has a deadline that makes an interruption expensive.
What you are actually paying for
A typical AI pipeline has several separately metered components:
- GPU and machine runtime while the job is running.
- Outbound network transfer when data leaves a cloud, region, or provider.
- Persistent-disk storage for datasets, checkpoints, and stopped or preempted instances.
- Rerun time and repeated reads or transfers after an interruption.
- Recovery and scheduler overhead, including time spent finding replacement capacity.
These meters interact. A training job may read a dataset from object storage, write checkpoints, and export artifacts. If those locations are in different regions or clouds, the outbound side of the transfer can be charged independently of GPU seconds. If Spot capacity is reclaimed before the next checkpoint, the replacement job may reread data and repeat transfers. A low hourly rate therefore does not establish a low total cost.
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Use this model for each run:
completed-job cost = GPU and VM runtime + expected restart work + egress + persistent-disk retention + recovery overhead
No provider-independent interruption percentage or universal share of AI spending caused by egress is established. Measure those values from your own job and billing logs rather than applying a generic rate.
How egress fees are charged
Internet egress is a separate meter
Google Cloud’s network pricing table lists internet data transfer out by destination and monthly volume. For North America and Europe, the listed Premium Tier rate is $0.12 per GiB for the first 1,024 GiB; higher-volume tiers are lower, and other destinations have different rates. The figure is a current pricing-page value accessed in 2026, not a worldwide or all-tier price.
Amazon Web Services documents a 100 GB monthly internet data-transfer-out allowance on its current EC2 pricing page, aggregated across eligible services. Other transfer paths are charged separately, so the allowance should not be treated as a blanket waiver for cross-region, cross-provider, or every service’s traffic.
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What counts as expensive traffic
Classify every large data movement by its destination:
- Same location: Keeping GPUs, object storage, and checkpoints together can avoid transfer paths that would otherwise incur outbound charges.
- Another region: The source region’s outbound meter and the destination service’s pricing rules may both matter.
- Another cloud or an external customer: Public-internet or inter-provider egress can be charged independently of compute.
- Repeated restart traffic: A preempted job that rereads a remote dataset or uploads a replacement checkpoint can multiply the same transfer.
Cloud price sheets use different units. Google Cloud’s cited rate is per GiB, while the AWS allowance is stated in GB. Preserve the unit when estimating costs; do not silently convert one figure into the other or imply that the allowances are directly interchangeable.
A simple egress estimate
Record outbound bytes by destination for one completed run. For example, if a job sends 800 GiB to North America or Europe on Google Cloud’s Premium Tier, that volume falls within the cited first-1,024-GiB tier and the listed rate would imply $96 before any other charges. If the run sends more than 1,024 GiB, apply the published lower rate for the additional tier rather than extending $0.12/GiB to all bytes. The destination, tier, and month must remain attached to every estimate.
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Discounts remove a continuity guarantee
Spot capacity uses spare infrastructure. Google Cloud says Spot VMs provide the same machine types, options, and performance as regular instances while reducing Compute Engine costs by up to 91%. That is a maximum current pricing-page claim, not a guaranteed discount for every GPU, region, or day. Google also states that Spot prices are variable and can change up to once every day.
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GPU cost is additive and regional
Google Cloud states that each GPU adds to the instance cost in addition to the machine-type cost. GPU prices are region-specific, and Spot GPUs use Spot prices. A comparison that lists only a GPU hourly figure can therefore understate the bill by omitting the attached machine, region, boot disk, and other resources.
What an interruption does to a job
An interruption is not merely a pause in the GPU meter. The instance may stop, terminate, or hibernate, depending on the provider and configuration. The process can lose in-memory state, a data-loader position, optimizer state, and any work created since the last durable checkpoint. The replacement job then needs to locate capacity, attach storage, restore state, and resume.
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How egress and interruptions compound each other
Suppose a training job keeps its dataset in one location and its GPUs in another. Every run already pays for the required outbound transfer. If a Spot VM disappears after several hours, the restart may reread the same shards and upload a new checkpoint. A short checkpoint interval reduces lost compute but can increase checkpoint traffic and storage writes; a long interval reduces checkpoint overhead but increases the amount of work at risk.
For each run, capture:
- GPU hours and attached machine hours.
- Data read and written, separated by destination and GiB or GB unit.
- Checkpoint count, size, and location.
- Persistent-disk hours before and after an interruption.
- Interruption events, lost training time, replacement capacity wait, and rerun duration.
Then compare a successful on-demand run with a Spot run using the same workload. The useful result is not the nominal Spot discount; it is the resulting cost and elapsed time per completed model or inference batch.
Cost comparison framework
Use the following axes when deciding whether a workload belongs on Spot capacity.
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|---|---|
| Compute price and Spot discount | Sets the baseline GPU charge, but changes with region and supply. |
| Interruption exposure | Determines probability of lost progress and scheduler work. No universal provider-independent rate is established. |
| Checkpoint interval and size | Controls recovery time and repeated storage or network transfer. |
| Egress destination and GiB | Rates vary by destination, tier, and provider; internet, cross-region, and other paths are not interchangeable. |
| Persistent-disk retention | Disks may continue charging after a preempted or stopped VM no longer incurs VM-hour charges. |
| Region and data locality | Co-locating data and GPUs can reduce cross-region and internet transfer. |
| Cost per completed result | Converts GPU time, retries, transfer, storage, and recovery into the metric decision-makers need. |
When Spot GPUs are a good fit
Usually suitable
- Training can resume from a durable checkpoint without manual reconstruction.
- The model and optimizer state are checkpointed frequently enough that lost work is acceptable.
- Datasets and checkpoints are close to the GPU region.
- The scheduler can requeue jobs across several capacity pools or zones.
- The deadline allows replacement-capacity delays.
- The workload can tolerate variable completion time in exchange for lower compute cost.
Usually risky
- A run keeps important state only in memory or on an ephemeral local disk.
- Each restart must transfer a large dataset across a region or provider boundary.
- Checkpoints are so large or infrequent that recovery traffic dominates savings.
- A fixed delivery window makes a capacity wait more expensive than the compute discount.
- Persistent disks are left attached indefinitely after failed attempts.
Make training resilient before moving it to Spot
- Checkpoint to durable storage. Save model weights, optimizer state, scheduler state, data-loader position, and the software or configuration needed to resume. Store checkpoints where replacement instances can reach them without a large cross-region transfer.
- Choose an interval from the loss budget. Decide how much GPU work can be lost in one interruption. Shorter intervals reduce recomputation but increase checkpoint writes, storage, and possibly egress.
- Handle interruption notices. On receiving a provider interruption signal, stop accepting new work, flush the current checkpoint when time permits, and record the event with the job ID and capacity pool.
- Requeue across capacity pools or zones. A replacement request limited to one pool can wait unnecessarily. Keep the model artifact and checkpoint path available to every permitted placement.
- Make startup idempotent. A retry should not duplicate output files, corrupt a checkpoint, or rerun completed shards. Use unique run IDs and atomic checkpoint publication.
- Clean up deliberately. Decide whether a stopped or preempted instance’s disks should be retained for recovery. Delete obsolete disks and checkpoints according to a documented retention period so storage charges do not accumulate.
- Record the completed result. Mark a run successful only when the model, export, or inference batch is verified. Failed attempts should remain visible for cost attribution.
Monitoring and attribution
Join compute, network, storage, and scheduler data by a common project, job, and run identifier. At minimum, create a dashboard or export with these fields:
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- GPU model, region, capacity type, and runtime.
- Machine-type and GPU charges separated where the provider exposes them.
- Outbound GiB or GB by destination, service, and region.
- Checkpoint bytes, disk-hours, and retention age.
- Interruption count, interruption reason when supplied, lost minutes, and restart count.
- Cost and wall-clock time for each completed result.
AWS says its Spot data feed describes Spot usage and pricing and typically arrives hourly when usage occurs. Use that feed with provider billing exports to reconcile interruptions and actual prices. For Google Cloud, preserve the region and Spot-price context from the applicable billing records; the advertised “up to 91%” figure is not a substitute for the rate applied to your instance.
A practical decision procedure
- Measure one baseline run. Capture the on-demand or otherwise stable run’s GPU hours, data movement, checkpoint volume, disk retention, elapsed time, and final bill.
- Map data locality. List where the dataset, checkpoints, GPUs, and exported result live. Identify every boundary crossed by outbound traffic.
- Model recovery. Use your checkpoint interval and observed startup time to estimate lost GPU work and replacement wait. Do not insert a generic interruption percentage; use historical events from the same pools when available.
- Test a limited Spot workload. Run a resumable job with explicit interruption logging. Compare completed results, not just the first attempt’s hourly charge.
- Set a go/no-go rule. Adopt Spot when the measured cost per completed result and acceptable completion-time risk beat the stable option. Keep critical or deadline-bound stages on capacity with stronger continuity when they do not.
Common mistakes and fixes
Comparing hourly prices only
Problem: A quoted Spot discount ignores machine charges, egress, disk retention, and retries.
Fix: Reconcile the full completed-job cost, including every failed attempt.
Putting GPUs far from the data
Problem: Each restart repeats a remote read, and every checkpoint export can become outbound traffic.
Fix: Co-locate the working dataset and checkpoint store with the GPU region when architecture and governance permit.
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Problem: Compute billing may stop while persistent disks continue to accrue storage charges.
Fix: Apply explicit disk-retention and cleanup policies, and include disk-hours in the run ledger.
Checkpointing without optimizer or data state
Problem: Restoring only model weights can change training behavior or force a partial restart.
Fix: Save the optimizer, scheduler, random-state requirements, and data position needed for a faithful resume.
Using a universal interruption assumption
Problem: There is no single interruption rate that applies to every provider, region, GPU, and time period.
Fix: Track interruptions for the exact capacity pools and workload, and report the observation window with the result.
Quick Recap
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