Vertex AI Vector Search 2.0 went GA in March 2026. For ISVs building on Google Cloud, it collapses embedding pipelines, indexing, feature stores, and hybrid search into one managed service, so you can ship AI features instead of building infrastructure.
Google Distributed Cloud Air-Gapped puts Gemini LLMs inside fully air-gapped defense and sovereign environments – the first hyperscaler to pull this off. Here’s why it matters and who should care.
Prompt injection is the SQL injection of the AI era. Model Armor is the first cloud-native solution that protects any LLM, on any cloud, without locking you into a single vendor.
Standard load balancers treat LLM inference like any other HTTP traffic. That is expensive and slow. GKE Inference Gateway knows the difference.
AlloyDB collapses three separate database systems into one managed PostgreSQL instance. The benchmarks are embarrassing for Aurora.
Most enterprise AI projects don’t fail because the technology doesn’t work. They fail because no one built the infrastructure to let it work at scale.
BigQuery can now run AI models directly inside SQL. The implications for how you’ve been architecting your data stack are a little uncomfortable.
Your AI reads text just fine. It’s the contracts, recordings, and images it can’t touch that are going to cost you.
Google Research just published a way to cut AI serving costs by 50% with zero accuracy loss. The interesting part is what happens to the ISVs who figure this out first.