Cloud Provider
Google Cloud
The data and AI cloud. Built by the people who invented Kubernetes.
Google Cloud is built on the same infrastructure that powers Search, YouTube, Gmail, and the world's most sophisticated AI systems. It excels at data engineering, machine learning, and containerised workloads — and its global private fibre network delivers performance that public internet simply cannot match.
150+
Cloud services
40+
Regions worldwide
11%
Global market share
Advantages
Best Kubernetes experience, period
Google invented Kubernetes and Borg before it. GKE (Google Kubernetes Engine) is the most mature managed Kubernetes service, with Autopilot mode for fully serverless, zero-ops clusters.
BigQuery changes the data game
BigQuery processes petabytes in seconds with no infrastructure to manage. For data teams, it is transformative — and often 60-70% cheaper than equivalent Redshift or Synapse setups.
AI and ML leadership
Vertex AI, TPUs (Tensor Processing Units), and native access to Gemini models give GCP a genuine edge for organisations doing serious machine learning at scale.
Sustained use discounts by default
Google automatically applies discounts of up to 30% for VMs that run most of the month — no reservations or upfront commitments needed. Committed use adds another 57% on top.
Superior private network
Google's global fibre network means inter-region and cross-continent traffic runs on private infrastructure — faster, more reliable, and more secure than traversing the public internet.
Disadvantages
Weaker enterprise sales and support
Google has historically been less aggressive in enterprise sales and account management than AWS or Microsoft. Getting escalated support requires significant spend.
Product discontinuation reputation
Google has killed many beloved products. Some enterprise architects are cautious about building on GCP services that might be sunset — though cloud infrastructure is more stable than consumer products.
Smaller service catalog
Fewer managed services than AWS. For some specialised needs (IoT, edge, niche ML services), you will find less coverage and may need to build more yourself.
Support quality below Enterprise tier
Below the Enterprise support tier (which starts at a high monthly minimum), response times can be frustratingly slow for production incidents.
Typical use cases
- 1Data analytics and petabyte-scale data warehousing with BigQuery
- 2Machine learning model training and serving, including LLMs
- 3Kubernetes-native platform engineering with GKE Autopilot
- 4Global consumer applications requiring low-latency worldwide delivery
- 5Multi-cloud strategies using GCP as the best-of-breed data and ML layer
Best for
Data-driven teams, AI and ML startups, and Kubernetes-first organisations who want the best container platform and data engineering ecosystem on the market.
How we work with GCP
We design, deploy, and operate GCP infrastructure for startups and scaling teams — from initial architecture to production Kubernetes clusters, cost optimisation, and 24/7 monitoring.
Book a free architecture reviewReady to build on GCP?
We handle architecture, Kubernetes, CI/CD, and operations so your team focuses on product.