What AWS Actually Is for AI Workloads
Amazon Web Services is the incumbent cloud platform, launched in 2006 and now offering over 200 distinct services. For AI workloads, the primary managed services are Amazon SageMaker (ML training and deployment), Amazon Bedrock (managed access to foundation models including Claude, Llama, and Titan), Amazon Kendra (enterprise search), Amazon Rekognition (vision AI), and Amazon Comprehend (NLP). Bedrock is particularly relevant for teams building LLM-powered applications without wanting to manage model infrastructure - it provides API access to major models with AWS-native security controls. AWS also provides the broadest range of compute options for custom AI workloads. GPU instances ranging from the older P3 family through to P4d and P5 instances with A100 and H100 GPUs are available across multiple regions. Spot instance pricing can reduce GPU compute costs by 60-90 percent for workloads that tolerate interruption, which matters significantly for training runs. The Inferentia and Trainium chips are AWS-proprietary silicon designed for lower-cost inference and training respectively, offering cost advantages for teams that can optimise for them. For UK and EU customers, AWS offers data residency in the London (eu-west-2) and Frankfurt (eu-central-1) regions with explicit GDPR data processing agreements available through the AWS Data Processing Addendum. The ICO and most enterprise legal teams are familiar with AWS's compliance documentation, which reduces procurement friction for UK-based products.
What Google Cloud Platform Actually Is for AI Workloads
Google Cloud Platform is the cloud infrastructure arm of Google, and for AI workloads specifically it has a distinctive positioning: it is the same infrastructure that runs Google Search, Gmail, YouTube recommendations, and Google's internal AI research. Vertex AI is the unified ML platform that covers training, deployment, experiment tracking, and model serving. It integrates tightly with BigQuery ML for analytics-scale machine learning and with Google's own foundation models via the Gemini API. GCP's TPU (Tensor Processing Unit) hardware is a genuine differentiator for teams training large models. TPUs are Google-proprietary AI accelerators that outperform standard NVIDIA GPUs for specific training workloads, particularly transformer architectures. Access to TPU pods - large clusters of TPUs for distributed training - gives GCP a technical capability that AWS does not directly match. For most application-layer AI products this is not relevant, but for companies doing serious model development it matters. For vector search, which underpins most RAG architectures, GCP offers Vertex AI Vector Search (formerly Matching Engine), a managed approximate nearest neighbour service that can handle billions of vectors with low latency. This is production-grade infrastructure that competes directly with dedicated vector databases like Pinecone. GCP also offers AlloyDB for PostgreSQL, which includes vector search capabilities similar to pgvector but on a fully managed, horizontally scalable PostgreSQL-compatible database. GDPR compliance is covered through the Google Cloud Data Processing Addendum and GCP operates data centres in London and multiple EU regions.
Managed AI Services and Foundation Model Access
AWS Bedrock and GCP Vertex AI are the two main managed foundation model services, and they take meaningfully different approaches. Bedrock is a model marketplace: you select from a curated list of third-party models (Anthropic Claude, Meta Llama, Mistral, Cohere, Amazon Titan) and access them via a unified API without managing the underlying infrastructure. Billing is per-token, and the integration with IAM, VPC, and CloudTrail gives enterprise security teams the audit trails they need. For UK fintech or healthtech products that need documented AI governance, Bedrock's integration with AWS native security tooling is a practical advantage. Vertex AI takes a more integrated approach. Google's own Gemini models are first-class citizens on the platform, but Vertex also supports open-source model deployment via Model Garden, which lets you serve Llama, Mistral, or custom fine-tuned models on managed infrastructure. The Vertex AI Evaluation Service lets you run systematic prompt and model evaluations, which matters for products where output quality needs to be tracked and reported. For teams doing any kind of systematic AI evaluation - required under the EU AI Act for high-risk AI systems - Vertex's evaluation tooling is more mature. In practice, neither platform locks you out of the major commercial model APIs. You can call the Anthropic or OpenAI APIs directly from either platform's compute, bypassing the managed model services entirely. Most early-stage teams do exactly this, which means the managed model service comparison matters more as you scale and need enterprise security controls around model access.
Startup Credits, Cost, and Pricing Structure
Both AWS and GCP run startup credit programmes that can meaningfully reduce early-stage infrastructure costs. AWS Activate provides up to 100,000 USD in credits for qualifying startups, with additional credits available through partner networks and accelerators. GCP for Startups offers up to 200,000 USD in credits for eligible companies, with a higher baseline and a longer usage window in some tiers. If you are early-stage and cost-sensitive, checking your eligibility for both programmes before committing to a platform is worth doing. Beyond credits, the pricing structures differ in ways that matter at scale. GCP's per-second billing on compute instances was an industry innovation and is now standard across both platforms. GCP's networking egress costs have historically been lower than AWS's in some configurations, and BigQuery's serverless query model can be cheaper than equivalent AWS Athena or Redshift workloads for analytics at moderate scale. AWS is known for having more complex pricing with more services, more pricing dimensions, and more reserved instance optimisation opportunity - which is both a flexibility advantage and an operational complexity cost. For AI-specific costs, both platforms price LLM inference on their managed services at competitive per-token rates that are broadly comparable to direct API pricing. GPU instance costs are similar across both platforms for equivalent hardware, though GCP's TPU pricing can undercut GPU costs for specific training workloads. The honest answer is that neither platform is definitively cheaper across all workloads - the right choice depends on your specific services mix and usage pattern.
Ecosystem, Tooling, and Integration Depth
AWS wins on breadth of third-party integrations and the depth of its partner ecosystem. The majority of enterprise software products, security tools, compliance platforms, and SaaS services have native AWS integrations before they add GCP support. For a product that needs to connect with enterprise customers' existing AWS infrastructure - common in B2B SaaS where enterprise buyers are AWS-native - this has practical procurement implications. Being able to deploy in a customer's own AWS account via AWS Marketplace is a genuine commercial advantage in certain enterprise segments. GCP has a tighter integration with Google Workspace, Google Analytics 4, and the broader Google advertising and analytics ecosystem. For products where user data flows from Google Analytics into ML pipelines in BigQuery, the integration is seamless and well-documented. The Google Cloud developer experience, particularly for data engineering workloads, has improved substantially since the early days and now competes seriously with AWS in this dimension. For AI-specific tooling at the framework level, most Python AI libraries were developed and tested primarily on standard Linux compute that runs equally well on both platforms. The Hugging Face ecosystem, PyTorch, TensorFlow, and JAX all work on both platforms' GPU instances. GCP's TPU support for JAX is particularly strong and is the preferred training environment for many Google-originating research frameworks. This matters primarily for teams doing custom model development, not for teams building applications on top of existing foundation models.
UK and EU Regulatory Compliance
For UK and EU products, regulatory compliance is not an afterthought - it is often a procurement requirement. Both AWS and GCP have comprehensive GDPR compliance documentation and offer data processing agreements that satisfy ICO requirements. Both provide the ability to restrict data processing to specific EU or UK regions. The practical differences lie in how mature the compliance documentation is, how familiar enterprise legal teams are with each platform's DPA, and what additional certifications are available. AWS has ISO 27001, SOC 2, Cyber Essentials Plus, and a wide range of sector-specific certifications including NHS Digital's Data Security and Protection Toolkit alignment documentation. For healthtech products being evaluated for NHS procurement, AWS's existing relationships with NHS Digital and its G-Cloud registration are practically useful. GCP has equivalent certifications and is also on G-Cloud, but AWS has longer-standing relationships with UK public sector buyers in many categories. The EU AI Act introduces a new compliance layer that matters for high-risk AI systems - those used in employment, credit scoring, education assessment, critical infrastructure, or law enforcement contexts. Both platforms provide the audit logging and model versioning infrastructure needed to demonstrate conformity, but neither automates the risk assessment and conformity documentation process that high-risk AI systems require. That work sits with the product team regardless of which cloud you choose. For FCA-regulated fintech products, both platforms have regulatory sandbox documentation and offer the technical controls needed to satisfy PRA and FCA requirements for AI governance.
When AWS Is the Right Choice
AWS is the right choice when your team or your customers already have significant AWS investment and expertise. If your enterprise buyers run on AWS and want to deploy your product in their own VPC, AWS is the natural fit. If you need the broadest possible range of managed services with the most mature documentation, and if your technical team has strong AWS experience, the path of least resistance is to stay in the AWS ecosystem. AWS is also the better choice if your product needs services that GCP does not yet match - for example, Amazon Connect for contact centre AI, specific compliance certifications that GCP has not yet obtained in your sector, or tight integration with AWS IoT for edge AI workloads. The depth of the AWS service catalogue means that unusual or complex requirements are more likely to have a native managed solution. For UK startups going through AWS Activate, the credit programme is generous and the support team has genuine startup experience. The AWS Startup Loft community in London provides access to technical support that is worth using if you are eligible. If you are undecided and your team has no strong preference, AWS's larger UK footprint and wider enterprise customer familiarity gives it a slight edge as a default choice for B2B AI products targeting UK enterprise buyers.
Verdict
Neither AWS nor GCP is universally better for AI workloads. The honest recommendation depends on your specific situation. GCP wins on tightest Vertex AI integration, BigQuery ML for analytics workloads, and TPU access for teams doing custom model training. AWS wins on breadth, enterprise ecosystem integration, UK public sector relationships, and the widest range of managed services beyond AI. For most early-stage AI SaaS products built with foundation model APIs rather than custom trained models, the difference is smaller than the comparison makes it sound. You will call the Anthropic or OpenAI API from either platform's compute, store data in a managed Postgres or vector database, and deploy a Node or Python service behind a load balancer. The cloud platform matters more as you scale and start using platform-native services for security, analytics, and model governance. At SpeedMVPs, we typically deploy on Vercel plus Railway or AWS depending on the product's needs and the client's existing infrastructure. We are platform-agnostic and will recommend what actually suits your product rather than defaulting to a single provider. Our AI MVPs are delivered in 2-3 weeks from 8,000 GBP with full code ownership transferred, and we include infrastructure setup in the engagement. Get a free consultation at speedmvps.co.uk