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Comparing Managed AI/ML Services

A practical, vendor-neutral look at what the major clouds offer.

Part 1

Every Brand Doing the Same Basic Job, Differently

why comparing managed AI/ML services across cloud providers matters, and why a vendor-neutral approach genuinely serves buyers best.

Part 2

Before Anyone Compared Brands at All

how organizations historically chose AI/ML infrastructure providers, and why that choice deserves more deliberate evaluation today.

Part 3

The Test Kitchen Every Brand Provides

comparing managed notebook and development environments — SageMaker Studio, Vertex AI Workbench, Azure ML Studio — across providers.

Part 4

Comparing How Each Brand's Oven Actually Cooks

comparing managed training infrastructure across providers — how each handles provisioning, scaling, and distributed training.

Part 5

The Set It and Forget It Mode, Compared

comparing AutoML offerings across providers, and evaluating when this automated approach genuinely fits a use case.

Part 6

How Each Brand Actually Delivers the Finished Dish

comparing managed model hosting and inference endpoint services across providers, and what genuinely differs in practice.

Part 7

The Pre-Made Ingredients Each Store Carries

comparing foundation model access across providers — Bedrock, Vertex AI Model Garden, and Azure OpenAI — and what genuinely differs.

Part 8

The Specialized Storage Appliance, Compared

comparing managed vector database offerings across providers, and what genuinely matters for retrieval-augmented AI applications.

Part 9

How Each Brand Organizes the Pantry

comparing managed feature store offerings across providers, and why consistent feature management genuinely matters.

Part 10

The Workflow System Bundled With Each Kitchen

comparing MLOps pipeline orchestration tools across providers, and what genuinely differs in how each coordinates the ML lifecycle.

Part 11

The Built-In Maintenance Tracker, Compared

comparing model monitoring and observability tooling across providers, and what genuinely matters for catching degrading model performance.

Part 12

The Safety Features Built Into Each Appliance

comparing responsible AI and fairness tooling across providers, and why this built-in capability genuinely matters.

Part 13

Reading the Price Tag Carefully

comparing pricing models for managed AI/ML services across providers, and why a surface-level price check can genuinely mislead.

Part 14

The Prep Service Each Store Offers

comparing managed data labeling services across providers, and why quality labeling infrastructure genuinely matters for supervised learning.

Part 15

The Compact Model for a Smaller Kitchen

comparing edge deployment options across providers for running AI models outside the cloud, closer to where data is actually generated.

Part 16

How Well the Appliance Fits Your Existing Kitchen

why ecosystem integration with a provider's broader data platform genuinely matters more than any single AI/ML service in isolation.

Part 17

The Brand-Specific Parts That Only Fit One Machine

how vendor lock-in specifically applies to AI/ML services, and what deliberate steps genuinely reduce this risk.

Part 18

Comparing the Warranty and Customer Support

why documentation quality and support responsiveness genuinely matter, and how they differ across providers in practice.

Part 19

Shopping for What You Actually Need, Not the Brand Name

a practical decision framework for choosing managed AI/ML services based on genuine organizational needs, not brand familiarity.

Part 20

The Fully Equipped Kitchen, Chosen Deliberately

reassembling every comparison covered across this series into the complete picture of how to genuinely evaluate managed AI/ML services.