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Slack Outlines Four-Phase Journey to a Multi-Cloud AI Serving Platform

Slack's multi-cloud AI infrastructure: how enterprises are moving beyond single-vendor lock-in.

Reporting by InfoQ AI/ML 1 min brief
Paper-cut illustration of an amber microchip with circuit paths extending into a row of data-center cabinets.
The infrastructure powering AI.AI illustration by KeyNews
The KeyNews take

Why it matters

Slack's shift from Amazon SageMaker to a multi-cloud AI serving platform (AWS Bedrock + Google Cloud Vertex AI) signals a broader enterprise trend: avoiding vendor lock-in while scaling inference across competing cloud providers. Critical for infrastructure leaders planning resilient, cost-optimized AI deployments.

The key facts

9 to know

  1. Slack evolved through four distinct infrastructure phases

  2. Migration from self-managed Amazon SageMaker to multi-cloud architecture

  3. Now spans AWS Bedrock and Google Cloud Vertex AI

  4. Multi-cloud strategy reduces vendor dependency and optimizes cost/performance tradeoffs

  5. Four-phase infrastructure evolution outlined

  6. Migration from self-managed Amazon SageMaker to multi-cloud

  7. AWS Bedrock and Google Cloud Vertex AI integrated

  8. Multi-cloud architecture reduces vendor lock-in risk

  9. Enterprise-scale AI serving platform deployment

Go to the source

InfoQ AI/MLinfoq.com

Publisher excerpt: Slack has outlined how its AI serving infrastructure evolved through four distinct phases, moving from a self-managed Amazon SageMaker deployment to a multi-cloud architecture spanning AWS Bedrock and Google Cloud Vertex AI. By Matt Foster
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