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

2026-06-25 · Source: InfoQ · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Software Development & Engineering · Depth: Intermediate, quick

Summary

Slack has detailed its four-phase evolution of its AI serving infrastructure, transitioning from a self-managed Amazon SageMaker deployment to a multi-cloud architecture incorporating AWS Bedrock and Google Cloud Vertex AI. Initially, SageMaker required manual capacity forecasting and GPU resource planning within an escrow VPC. The company then migrated to Amazon Bedrock, eliminating infrastructure management overhead and gaining faster access to Anthropic models. To manage 10x traffic variability, Slack combined Bedrock's Provisioned Throughput and On-Demand offerings. Recognizing single-provider dependency as a limitation, Slack implemented a multi-cloud strategy with Google Cloud Vertex AI, building a provider-agnostic serving layer for secretless authentication, API normalization, unified observability, and intelligent routing. This final configuration improved quality on complex reasoning workloads by approximately 10% and reduced latency for short prompts by around 67%.

Key takeaway

For MLOps Engineers designing AI serving platforms, Slack's journey demonstrates the value of a multi-cloud strategy. You should prioritize building a provider-agnostic abstraction layer to improve resilience and access diverse foundation models. This approach can reduce single-provider dependency risks and enhance performance, as seen with Slack's 10% quality and 67% latency gains. Consider implementing intelligent routing and unified observability for robust cross-cloud operations.

Key insights

Multi-cloud AI serving platforms enhance resilience, performance, and model access by abstracting provider dependencies.

Principles

• Single-provider AI platforms limit resilience and model access.
• Abstraction layers enable consistent multi-cloud AI operations.
• Hybrid capacity models manage fluctuating AI workloads effectively.

Method

Slack's method involved migrating from self-managed SageMaker to AWS Bedrock, then integrating Google Cloud Vertex AI via a provider-agnostic serving layer with intelligent routing and unified observability.

In practice

• Combine Provisioned Throughput and On-Demand for AI scaling.
• Implement API normalization for multi-cloud consistency.
• Use continuous endpoint evaluation for traffic redirection.

Topics

• Multi-cloud AI
• AI Serving Platforms
• AWS Bedrock
• Google Cloud Vertex AI
• LLM Inference
• Cloud Resilience

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by InfoQ.