Vector store architectures beyond “just a vector DB”

Vector store architectures beyond just a

vector DB. Vectors are only half the

system. Metadata, filtering, scale, and

ops decide which architecture actually

works in production. One, the three AWS

native paths and why they exist. You'll

see these three over and over in exam

questions. Manage knowledge base KB.

Two, open search vector plus neural

plugin. Three, Amazon Aurora with PG

Vector plus Amazon DynamoB for metadata.

Each exists because one size does not

fit all. Two, manage knowledge base, the

easy button. What it is, a fully managed

rag setup, ingestion, chunking,

embeddings, storage, retrieval, minimal

configuration, minimal control. When AWS

wants you to choose it, fastest time to

value, simple documents, no custom

ranking logic, low ops burden, when not

to use it. Exam, you need custom

metadata filters. You need hybrid search

tuning. You need audit debug visibility.

You need custom chunking or reranking.

Memory hook. Knowledge base equals

automatic gearbox. You don't tune it,

you just drive.

How to three? Open search vector plus

neural plugin. Enterprise search engine.

What it gives you? Vector search and

keyword search. Hybrid search. BM25 plus

vector. Metadata filtering at scale.

Ranking. Fine grain tuning. This is the

most exam heavy option. Typical

architecture. Embedding stored as

vectors. Text fields indexed.

Traditionally filters on metadata. Date,

ro, tenant, region. Why AWS loves it?

Scales well. Handles complex queries.

Designed for search workloads. Memory

hook. Open search equals search brain.

Vectors plus keywords filters tuning.

The four Aurora PG vector relational

first thinking. What it is? A relational

database with vector support. Vectors

are just another column. When it shines,

you already use Aurora. Strong

relational constraints. Transactions

matter. Moderate data size. When it

struggles, very large vector sets, heavy

similarity search traffic, advanced

search features, memory hook. Aurora PG

vectoral first, vector second.

Now five, Dynamo DB, not a vector DB,

but crucial. Dynamob is not used for

similarity search. It is used for

metadata, access control, tenant

isolation, fast key value lookups,

common pattern, vectors, open search or

Aurora, metadata, Dynamo DB, join at

query time, memory hook, DynamoB equals

identity and control plane. Number six,

AWS static 1 vector architecture

edition. Static vector store choice

index schema metadata model search

strategy vector hybrid SQL plus one

incoming query user question. The

architecture stays fixed. Queries vary.

That's static plus one. Seven. How AWS

expects you to choose exam decision

table. Choose manage KB when simplicity

control low ops default rag is enough.

Choose open search when hybrid search

needed complex filtering. Ranking large

scale multi-tenant systems. Choose

Aurora PG vector when strong relational

needs moderate scale SQL joins matter

existing Aurora footprint. Use DynamoB

when metadata lookup permissions fast

keybased access. Now 8 classic exam

traps. Watch for these. Use DynamoB for

vector similarity. Knowledge base for

complex filtering. Aurora PG vector for

massive search workloads. One vector

store solves everything. AWS wants

intentional trade-offs, not blanket

answers. One memory story. Don't forget

this. The library system. Knowledge base

equals selfch checkckout kiosk. Fast,

simple, limited options. Open search

professional librarian knows keywords,

topics, cross references. Aurora PG

Vector filing cabinet with SQL labels.

Structured, orderly, not built for

crowds. Dynamo DB ID card system. Who

can access what? No single tool runs the

whole library. Exam compression rules

memorized. Simple rag. Manage KB search

plus scale filters. Open search. SQL

relations. Aurora PG vector metadata and

access Dynamo DB. If an answer ignores

metadata or filtering, it's incomplete.

What AWS is really testing, they are

asking open quote. Do you understand

that retrieval is a system not a

database? Close quote. If your answer

shows separation of concerns,

scalability thinking, metadata

awareness, you're choosing like AWS

expects. Below are three concrete

production style examples showing when

and why you'd pick managed KB, Open

Search or Aurora Page Vector plus

Dynamob. This is exactly how AWS expects

you to reason in the exam. Real example

one, fast rag for internal documents.

Manage knowledge base scenario. A

company wants an internal chatbot over

PDFs, policies, HR docs, word files,

procedures, requirements. Working in

days, not weeks. No custom ranking. No

complex filters. Low ops. Architecture.

Upload docs to S3. Use bedrock knowledge

base. Ask questions. Get grounded

answers. Why this is correct? KB handles

chunking, embedding, storage, retrieval,

minimal configuration, no infer. Why not

open search here? Overkill. More ops. No

extra benefit. Exam takeaway.

Simple rag. Low control needs. Managed

knowledge base. Memory hook KB equals

plug-and-play real example 2 large

enterprise search with filters open

search scenario a legal research

platform with millions of documents

queries like open quote find cases about

construction defects from NC at 2018 for

tenant X close quote needs semantic

similarity keyword matching metadata

filters date jurisdiction tenant

reranking architecture documents

embeddings store vectors text In Open

Search, use vector search, BM25, keyword

search, metadata filters, optional

reranker, access control via tenant

metadata. Why this is correct? Open

search supports hybrid search filters

run at scale designed for search

workloads. Why not manage KB? Limited

tuning, limited debugging, poor fit for

complex filters.

Exam takeaway

scales hybrid search filters. Open

search

memory hook. Open search equals

enterprise search brain. Real example

three. Transactional app with

embeddings. Aurora PG vector Dynamob.

Scenario. A SAS app where each user has

notes and comments. Notes need

similarity search. Find related notes.

Strong consistency. SQL joins with users

projects permissions. Data volume is

moderate not millions of vectors.

Architecture notes table in Amazon

Aurora. Content embedding vector project

ID. Use PG vector for similarity search.

Store permissions and fast lookups in

Amazon Dynamo DB. User allowed project

IDs. Query flow one user query

embedding. Two SQL query vector

similarity where project ID in. Three

DynamoB checks access. Why this is

correct? SQL first design transactions

matter moderate scale existing Aurora

footprint. Why not open search heavier

ops less natural for transactional

joins? Exam takeaway: Relational data

plus vectors. Aurora PG vector plus

Dynamob for metadata. Memory hook.

Aurora PG vector equals SQL app with

vector spice.

How AWS expects you to choose. Real

decision logic requirement correct

choice. Fastest rag low ops manage KB

hybrid search filter scale open search

joins transactions Aurora PG vector

metadata permissions dynamob

statics one real world framing static

vector store choice index schema

metadata model user query the system is

fixed queries vary that's static one and

retrieval architecture

one final memory story lock it in the

company library knowledge base Selfch

checkckout kiosk. Fast, simple, no

customization. Open search expert

librarian knows keywords, topics,

filters. Aurora PG vector equals filing

cabinet with SQL labels. Structure

transactional. Dynamob ID badge system.

Who can see what AWS wants you to pick

the right librarian, not bring a

bulldozer?