Platforms & Cloud

The heavy lifting behind AI.

Data & AI platforms, landing zones, accelerators and cloud AI migrations. Compare the options, tick what describes your situation, and see which fits.

5data & AI platforms compared
4landing-zone patterns
7production-grade accelerators
5×5migration paths between AI providers

The platform is where your data, analytics and AI meet. The right choice depends less on features and more on your cloud, your team’s skills and your workloads. We’ve built on all of them and stay vendor-neutral.

Why you’d want one

  • One governed copy of data for BI, ML and GenAI
  • Security, lineage and access control in one place
  • Predictable cost instead of project-by-project infrastructure

Your situation (tick what applies)

Databricks

Choose it when
  • Large-scale data engineering, streaming and ML on one platform
  • Open formats (Delta / Iceberg) and runs on Azure, AWS and GCP
  • Model training, fine-tuning, serving and vector search built in; Unity Catalog governance
Watch out for
  • Needs strong engineering skills to run well
  • Compute costs need active FinOps

Snowflake

Choose it when
  • SQL-first teams who want analytics with very little infrastructure to manage
  • Secure data sharing with partners and a data marketplace
  • LLM functions and search callable directly from SQL (Cortex AI)
Watch out for
  • Complex custom ML and non-SQL workloads are less natural
  • Credit consumption must be governed

Microsoft Fabric

Choose it when
  • Organisations standardised on Microsoft 365, Power BI and Azure
  • One SaaS platform (OneLake) for engineering, warehousing and BI
  • Copilot experiences and tight integration with Azure AI Foundry
Watch out for
  • Younger platform; some features still maturing
  • Capacity-based pricing needs careful sizing

Google BigQuery + Vertex AI

Choose it when
  • Serverless analytics at massive scale with almost no ops
  • Native access to Gemini models, BigQuery ML and vector search
  • Strong for marketing, web and product analytics data
Watch out for
  • Best value when your workloads live on Google Cloud
  • Query-cost governance needed for self-service use

AWS native (S3, Glue, Redshift, SageMaker, Bedrock)

Choose it when
  • AWS-first organisations wanting full control of building blocks
  • Deep integration with AWS security, networking and existing workloads
  • SageMaker for ML and Bedrock for many foundation models
Watch out for
  • More assembly and integration work than an all-in-one platform
  • Needs a clear reference architecture to avoid sprawl

Using something else, or a mix of these? Tell us about your setup →

Related

Where platforms meet models.

Sovereign GPU Compute

Host, fine-tune and train your own models on dedicated in-region GPUs.

GPU Compute →

AI Gateways

One controlled entry point for every model call: the foundation for painless migrations.

AI Gateways →

Industry examples

See platform, landing-zone and migration use cases by industry.

Use cases →

Next step

Let's talk about your AI system.

A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.

Book a free call Find your starting point

Keep exploring

There’s more to see.

Up next · GPU Compute Your models. Your region. Your GPUs. Dedicated, in-region GPU capacity to host, fine-tune and train your own models. Research Smaller, faster, cheaper. On purpose. Distillation, small language models, speech recognition and inference optimisation. Use cases What this looks like in your industry 24 use cases across finance, healthcare, retail, manufacturing, legal and SaaS. How we work Start small. Prove value. Scale. Three engagement models, a measure-first approach and a 3-click starting-point finder. About Hands-on engineering, not slideware Who we are and the principles we work by.