Compare GPU cloud providers against the workload, location, network route, and data volume you actually need—not a single headline SLA or bandwidth figure. Check whether the exact GPU configuration is covered and obtainable, separate GPU fabric from VM egress, calculate transfer and connectivity charges for each destination, then validate candidates with the same workload benchmark.
Start with a matched comparison
A useful comparison holds the assumptions constant. For each candidate, specify the GPU model and count, region and zones, storage location, network route, expected outbound volume and destinations, and whether the capacity is on demand, reserved, or interruptible. Record the documentation date and contract terms alongside each result; cloud availability, pricing, and product details can change.
Do not collapse three different questions into one: whether the service has an availability commitment, whether the required GPU capacity can be provisioned when needed, and whether the network can move your workload’s traffic at the needed rate. An SLA percentage is not a capacity guarantee unless the applicable contract explicitly says it is.
| Comparison axis | Record for every candidate | Why it matters |
|---|---|---|
| GPU capacity | Exact SKU, model, count, memory, region, zones, and reservation or queue terms | Coverage and access can vary by accelerator and location. |
| Availability | SLA scope and target, measurement method, exclusions, capacity commitment, claim requirements, and remedy | A headline percentage may not apply to the GPU deployment you plan to run. |
| GPU networking | Within-node interconnect, inter-node fabric, and topology | Distributed training can be constrained by communication between GPUs or hosts. |
| Egress limits | Per-VM maximum, per-flow ceiling, aggregate quota, route, and destination | The effective rate depends on more than a machine’s published maximum. |
| Transfer cost | Outbound volume by destination, included amounts, rate tiers, and billing unit | Different paths can have different transfer charges. |
| Connectivity cost | Ports, attachments, private interconnect, fabric, cross-connect, and facility charges | A private path may add fixed costs even if its transfer rate is lower. |
| Validation | Benchmark, traffic shape, destination, region, software, and measurement window | Like-for-like tests make documentation-based candidates more comparable. |
Check whether the exact GPU deployment is covered
Read the SLA for the specific service and accelerator configuration, not only the provider’s general compute SLA. Capture the model’s general-availability status, the zones where it is offered, how uptime is measured, scheduled-maintenance and other exclusions, and what evidence and claim deadline apply. Note the service credit or other remedy; an SLA remedy is not necessarily compensation for business impact.
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Google Cloud illustrates why SKU-level checks matter: its Compute Engine SLA covers an attached GPU instance only when the GPU model is generally available. In a region with multiple zones, the model must also be available in more than one zone. Those conditions are described in Google Cloud’s About GPU instances documentation, consulted October 7, 2026. A single-zone GPU deployment should not be assumed to qualify just because the underlying VM family has an SLA.
For every provider, separately verify the capacity arrangement. Check whether the required count can be reserved, whether a reservation has a minimum term or other conditions, and what happens if capacity is unavailable at the time you need it. The cited provider documentation does not establish equivalent capacity guarantees across providers, so compare the actual offer and contract rather than inferring one from an availability target.
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Compare networking by layer, not by one bandwidth number
“Network bandwidth” can refer to different parts of the data path. Record these separately, and keep each published figure tied to its machine type, NIC configuration, software, and destination assumptions:
- Within-node GPU interconnect: the link between GPUs in one physical server. This matters for multi-GPU jobs on a node.
- Inter-node fabric: host-to-host networking for jobs distributed across servers. Record the topology and any configuration requirements.
- VM egress: the outbound limit for an instance, plus any per-flow ceiling that applies to the route.
- Aggregate limits: project, region, or other shared quotas that can constrain multiple instances together.
- Data paths: the connection to object or block storage, the public internet, or a private interconnect. These are not interchangeable routes.
Google Cloud’s GPU machine documentation gives configuration-specific maximum network bandwidth for A3 H100 instances: 25 Gbps for a3-highgpu-1g and 1,000 Gbps for a3-highgpu-8g. These are published maxima in Google Cloud documentation consulted October 7, 2026—not independently measured application throughput or a cross-provider benchmark. Google also cautions that actual egress depends on destination and other factors. See GPU machine types.
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Google Cloud’s Compute Engine network bandwidth documentation describes instance and project-level limits, as well as per-flow limits for some outbound paths. It also states: “Bandwidth from the internet is not covered by any SLA and is subject to network conditions.” Treat a machine’s maximum as a ceiling for a specified configuration, not a promise that one connection or an internet destination will sustain that rate.
Validate the traffic pattern you will run
Benchmark representative training or inference traffic rather than relying on a generic speed test. Use the intended packet sizes, parallelism, number of workers, destination, storage path, and software configuration. Where the documented design has per-flow limits, include the multiple-flow behavior your application would actually use. Record throughput, latency, packet loss or retries where applicable, and the test date and configuration. Report these as your team’s measurements, not as provider guarantees.
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Calculate egress for the route and destination
Estimate outbound bytes separately for each destination and transfer path, then apply the current billing rules for the exact service. Distinguish internet egress, transfer within one provider or region, cross-region movement, private interconnect transfer, and traffic through a third-party fabric. Check directionality, billing units, included quotas, tiers, and product exclusions. Add fixed connectivity costs—such as ports, attachments, cross-connects, and colocation or fabric charges—where they apply.
CoreWeave’s pricing page, consulted October 7, 2026, lists egress and input/output operations as free in the displayed pricing sections and lists data transfer within CoreWeave as free. It separately lists public IP and Direct Connect charges, so those displayed transfer terms do not establish that every network-related cost is zero. Check the applicable service and current conditions on CoreWeave Cloud Pricing.
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For Google Cloud, architecture guidance says transfer over Partner or Dedicated Interconnect is charged at a lower rate than internet traffic, while the interconnect can add monthly port or attachment charges. Third-party facilities and equipment can add costs as well. The same guidance says redundant Dedicated Interconnect topologies have monthly SLAs that vary by topology, while a single connection has no SLA. These are connectivity-path considerations, not a GPU compute SLA; see Patterns for connecting other cloud service providers with Google Cloud.
Build a workload-specific transfer estimate
- List destinations: identify where training data, checkpoints, model artifacts, logs, and inference outputs will go.
- Assign volume and path: estimate outbound bytes for each destination and state whether the route is internet, same-provider, cross-region, private interconnect, or third-party fabric.
- Apply the matching billing terms: use the current service-specific rate, billing unit, tier, and any included amount for that path.
- Add fixed charges: include recurring or usage-based port, attachment, cross-connect, colocation, or fabric fees that are part of the design.
- Check the bill against a test: run a representative data export, measure the bytes transferred, and compare the resulting billed transfer with your estimate.
Use provider examples without treating them as a ranking
The cited official pages provide useful examples, but they do not form a normalized, cross-provider comparison of SLA scope, capacity, network performance, and egress costs for matched GPUs and geographies.
| Provider and source | What the cited page establishes | What it does not establish for a matched comparison |
|---|---|---|
| Google Cloud Compute Engine GPU machine types, network bandwidth, and GPU instance coverage |
Configuration-specific GPU machine bandwidth maxima, network egress limits, and GPU availability conditions relevant to SLA eligibility. | Equivalent terms for another provider, actual application throughput, or a capacity guarantee beyond the applicable contract. |
| CoreWeave Cloud pricing page |
Displayed transfer and network line items, including free egress and intra-CoreWeave transfer in the sections consulted October 7, 2026. | That every transfer path or network-related cost is free, or that the terms apply identically to every service. |
| Lambda On-Demand Cloud overview |
GPU-backed virtual machines, listed GPU families including B200, GH200, and H100, and SXM’s improved bandwidth between GPUs within a physical server. | A comparable SLA or egress price based on that overview alone. |
Make the decision with a controlled benchmark
After checking the contract and published limits, test candidates using the same GPU count and model, region, storage assumptions, network route, software, traffic shape, and measurement window. Include at least one representative training or inference workload and one data-export scenario. Track time to provision as well as throughput, latency, packet loss or retries where relevant, and total billed transfer. Date and document the configuration so later changes in service terms or workload assumptions do not silently invalidate the comparison.
Choose based on the workload’s actual bottleneck and destination. A candidate with a suitable SLA but no dependable access to the required capacity may not meet a launch schedule; a high network maximum may not help if the application’s path or flow pattern cannot use it; and a low per-byte transfer charge may not offset fixed connectivity costs at your volume. The right choice is the one whose documented terms and measured behavior fit the same deployment assumptions.
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