Why Slack Moved Its AI to Multiple Clouds

Slack has revealed its four-phase strategy for moving its AI platform from a single cloud to a multi-cloud architecture with AWS and Google.

Slack has publicly detailed how it evolved its artificial intelligence infrastructure, moving from a self-managed system on Amazon SageMaker to a more advanced multi-cloud platform. The company outlined a deliberate, four-phase journey to achieve this transition. The new architecture now relies on managed AI services from two of the biggest cloud providers: AWS Bedrock and Google Cloud's Vertex AI. This strategic shift allows Slack to leverage the unique strengths of each platform, creating a more robust and capable foundation for its AI-powered features.

This move provides a significant real-world case study for any company developing an AI strategy. For CTOs and engineering leaders, it signals a trend away from the complexity of managing AI models on a single cloud. Instead, the focus is shifting to using powerful, pre-built services like Bedrock and Vertex AI. A multi-cloud approach helps companies avoid vendor lock-in, access the best models from different providers, and improve overall system resilience. Developers and platform engineers can learn from Slack's phased rollout, which offers a practical blueprint for making a similar transition without disrupting users.

The decision by a major tech player like Slack validates the multi-cloud strategy for demanding AI workloads. As more businesses integrate AI into their core products, the choice of where to run models becomes a critical business decision. The calculation is no longer just about cost, but about which combination of cloud services provides the most flexibility, performance, and access to innovation. Companies planning their own AI roadmaps should consider if a similar strategy could help them balance the benefits of choice against the operational complexity of managing multiple providers.