AWS vs. GCP for the AI-First Workload in 2025

The cloud-vendor selection question, for any team building seriously on top of a model, is now an AI-pricing question first and a general-compute question second. AWS's incumbent position — built over fifteen years of being the default — is being tested by a workload pattern in which inference and training spend can compound to half of total cloud cost within twelve months. The pricing dynamics on that workload meaningfully favor GCP at the time of this review.

We tested both at three production AI workloads through Q2 2025: an LLM-driven document processing pipeline, a recommendation system using fine-tuned open-source models, and a multimodal customer-support agent. We measured raw inference cost, training cost on identical model architectures, tooling productivity, and the procurement experience.

Where GCP wins

Cost. For the modal AI inference workload, GCP's pricing is 25–35% lower than AWS's equivalent SKUs for equivalent throughput. The gap is largest on TPU-optimized workloads and narrowest on standard GPU inference. Even on standard GPU inference, GCP's pricing is 12–20% lower in our measurements.

Tooling is the second GCP win. Vertex AI's integrated experience — model registry, evaluation pipelines, deployment, observability — is the cleanest in the category and is meaningfully ahead of SageMaker for the team building from scratch. The gap is smaller than it was 24 months ago but it remains real.

AI-pricing differences compound to half of total cloud cost within twelve months for the modal AI-first workload. The pricing dynamics now meaningfully favor GCP.

The third GCP advantage is BigQuery's integration with Vertex. Teams that store training data in BigQuery — increasingly the default for analytics-shaped data — get a meaningful productivity benefit from the integration. Equivalent setups on AWS require more glue code.

Where AWS still wins

Procurement. AWS's enterprise sales motion is materially more sophisticated than GCP's. Buyers who need committed-use discounts negotiated at scale, multi-year discount structures, marketplace billing for procurement-mandated vendor management, and the depth of AWS's partner network find the procurement experience meaningfully better at AWS. We have watched two of our test customers' procurement teams refuse to engage with GCP at the executive level because the GCP relationship-management was visibly thinner.

The second AWS win is the existing-footprint case. For organizations whose data and compute already lives in AWS, the marginal cost of adding AI workloads to AWS is meaningfully lower than the integration cost of adding GCP. This is not a structural product advantage; it is a switching-cost advantage that AWS earns by being the incumbent.

The third AWS strength is breadth. Bedrock's model catalog has grown materially in the last 18 months and is now competitive with Vertex's. Anthropic's flagship models are available on AWS. The recent open-source models (Llama derivatives, Mistral, the Chinese open models that matter) ship to Bedrock quickly. AWS is no longer the model-poor cloud.

On Azure

We did not include Azure in this comparison because Azure's positioning has become heavily dependent on the Microsoft-OpenAI partnership. For organizations whose AI strategy is built around OpenAI's flagship models, Azure remains the right answer. For organizations whose strategy is model-agnostic — which describes most AI-first workloads we now see — Azure is a credible third choice but rarely the leading choice on either cost or tooling.

The procurement reality

Multi-cloud AI architectures sound clean in slide decks and operate messily in practice. The team that runs inference on GCP and training on AWS is doing twice the data-engineering work of the single-cloud team and is paying for the additional complexity in operational overhead. Multi-cloud as a religion is bad architecture.

Multi-cloud as a pricing-leverage strategy is more defensible. A buyer who can credibly threaten to move 30% of their AI workload to a competing cloud will get materially better committed-use pricing. This is a real strategy and the operational cost of running a small footprint in a second cloud is sometimes paid back through pricing leverage on the primary cloud's renewals.

The verdict

GCP for the AI-first workload that does not already live somewhere. AWS for the AI-extending workload that already lives in AWS and where switching costs are not justifiable. Both at once is fine if you have the operational appetite to manage the complexity. The default answer used to be AWS without thinking. The default answer is now GCP-or-AWS-with-thinking, which is itself an interesting category change.