Multi-Cloud
An architecture strategy that uses services from multiple cloud providers to avoid vendor lock-in, leverage best-of-breed capabilities, and improve resilience. Multi-cloud deployments distribute workloads across providers like AWS, Google Cloud, and Azure.
Multi-cloud strategies range from using different providers for different workloads to running the same application across multiple clouds simultaneously. Benefits include negotiating leverage with providers, accessing unique capabilities like Google's TPUs or AWS's ecosystem breadth, and avoiding dependence on a single provider's availability. The costs include increased operational complexity, the need for cloud-agnostic tooling, and potential data transfer charges.
For AI product teams, multi-cloud can provide access to the best AI-specific services from each provider: Google's Vertex AI for certain model types, AWS SageMaker for MLOps, and Azure's OpenAI integration for GPT models. However, the operational overhead of managing infrastructure across providers is substantial and should be justified by clear business or technical benefits. Growth teams should consider multi-cloud pragmatically: using the best analytics tools regardless of cloud provider while maintaining awareness of data transfer costs. Most teams benefit more from deep expertise in a single cloud provider than from spreading resources across multiple platforms, unless specific regulatory or business requirements mandate multi-cloud.
Related Terms
Content Delivery Network
A geographically distributed network of proxy servers that caches and delivers content from locations closest to end users. CDNs reduce latency, improve load times, and absorb traffic spikes by serving content from edge nodes rather than a single origin server.
Edge Computing
A distributed computing paradigm that processes data closer to the source of generation rather than in a centralized data center. Edge computing reduces latency, conserves bandwidth, and enables real-time processing for latency-sensitive applications.
Serverless Computing
A cloud execution model where the provider dynamically manages server allocation and scaling. Developers deploy functions or containers without provisioning infrastructure, paying only for actual compute time consumed rather than reserved capacity.
Function as a Service
A serverless computing category where developers deploy individual functions that execute in response to events. FaaS platforms like AWS Lambda, Google Cloud Functions, and Azure Functions handle all infrastructure management, scaling each function independently.
Platform as a Service
A cloud computing model that provides a complete development and deployment environment without managing underlying infrastructure. PaaS offerings like Heroku, Vercel, and Google App Engine handle servers, storage, networking, and runtime configuration.
Infrastructure as a Service
A cloud computing model that provides virtualized computing resources over the internet. IaaS offerings like AWS EC2, Google Compute Engine, and Azure Virtual Machines give teams full control over servers, storage, and networking without owning physical hardware.