A customer is clear about their needs. It triggers a call to an endpoint. It seeks the answer.

You're building a SaaS application that needs to support multiple databases. Or maybe you're migrating from MySQL to PostgreSQL. Or you have different clients using different database engines.

Whatever the reason, you've likely encountered this nightmare:

When migrating or running SAP S/4HANA on AWS, many organizations fixate on EC2 instance prices and assume that choosing the cheapest instance types will yield the biggest savings. In reality, cloud TCO is heavily impacted by landscape design choices, how many environments you run, how they’re sized, how data is managed and what auxiliary services you use. Cutting cloud costs isn’t just about shrinking VM sizes it’s about architecting an efficient SAP landscape. As one SAP FinOps guide notes, focusing only on instance sizing addresses symptoms, not causes. True cost optimization asks Is the SAP landscape design efficient? Are you running unnecessary SAP instances, and can workloads consolidate onto fewer systems?. In other words, a thoughtful landscape architecture often yields larger savings than a simple per-server cost reduction.

Understanding an SAP S/4HANA Landscape on AWS

A typical S/4HANA landscape consists of multiple tiers and environments. You might have separate DEV, QA, Staging and Production systems each a full SAP stack with its own HANA database and application servers. On AWS, that could translate to dozens of EC2 instances, along with associated storage and network infrastructure. Each additional environment or system copy multiplies costs for compute, Amazon EBS storage, Amazon EFS shared file systems, backup retention, and so on. Landscape design decisions such as how many parallel systems to run or whether every environment needs high availability can quickly outweigh the cost of an individual EC2 instance.

"Do the simplest thing that could possibly work."

— Kent Beck, creator of Extreme Programming and pioneer of Test-Driven Development.

Amazon S3 Vectors changes the equation. It's native vector storage built into S3 — no clusters, no provisioning, no idle compute. You store vectors like you store objects, query them with sub-100ms latency, and pay per use. It went GA in December 2025 and now supports 2 billion vectors per index across 31+ AWS regions.

AWS has increasingly nudged Lambda Node.js workloads toward modern asynchronous patterns, including guidance that async/await handlers are recommended and that callback-based handler signatures are only supported up to Node.js, with Node.js requiring asynchronous work to use async handlers. This constraint is a design opportunity: Once handler execution is centered on a returned value and on predictable, composable functions, cross-cutting behavior can be expressed as functional wrappers and pipelines rather than as framework-specific magic.

It’s vector database season. Conferences are full of RAG pipeline talks. Pinecone raised over $100 million; Milvus, Weaviate, and Qdrant are all well-funded and competing to disrupt how we store and search embeddings.

Let’s be blunt: The vector databases are poorly implemented, and most of the companies implementing them are doing so to solve a problem that doesn’t exist.

This is what I learned about multi-cloud strategy, not the vendor pitch but the messy reality of keeping production alive across multi-cloud boundaries.

The Problem: "Anonymous" Queries

When your database starts lagging (slow queries), you check the processlist in MySQL only to find a vague line:

Here's the short answer: They're not competing — they're complementary. Each one solves a different problem at a different layer of the agent architecture. Think of them like TCP, HTTP, and HTML — different protocols at different layers that work together to make the web function.