DynamoDB is the go-to for AWS-native workloads that need single-digit-millisecond latency at any scale, but its rigid key-value design and lack of native joins make it painful for anything beyond simple access patterns. MongoDB offers far more query flexibility and a richer document model for the same price class, while Firestore provides a better developer experience for smaller projects. DynamoDB wins only when you're all-in on AWS and can stomach the upfront schema design work.

→ Use it when you're building on AWS, need predictable performance at massive scale, and your access patterns are simple enough to model entirely with primary keys and secondary indexes.

Pros

+Serverless architecture with automatic scaling
+Single-digit millisecond latency for most operations
+Built-in backup and point-in-time recovery
+Seamless integration with other AWS services

Cons

-Pricing can be unpredictable with high throughput
-Limited query flexibility compared to relational databases

Compare:vs Firebase

Amazon DynamoDB

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AWS's NoSQL workhorse: scales like a dream, but you'll pay for every query and pray you never need a JOIN.

Pros

+Fully managed with automatic scaling and multi-AZ replication
+Single-digit millisecond latency for key-value operations
+Built-in security, backup, and in-memory caching with DynamoDB Accelerator (DAX)

Cons

-Pricing model can get expensive with high throughput or large datasets
-Limited query flexibility compared to relational databases (no JOINs, complex queries)

Compare:vs Firebase vs DynamoDB

MongoDB

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The database for when you want to store JSON and pretend it's a schema.

Why we picked it

MongoDB owns the document database category because it shipped the first usable JSON store and never stopped iterating. The aggregation pipeline, Atlas search, and change streams are features that competitors like Couchbase or Firestore still can't match in a single product. It's the default for a reason — you'll outgrow it only if you need ACID transactions across documents, at which point you should have used Postgres.

→ Use it when you need a flexible schema for rapid prototyping or JSON-heavy workloads, and you're willing to trade strict consistency for developer velocity and a mature ecosystem.

Pros

+Flexible schema allows rapid prototyping and iteration
+Native JSON-like document storage fits well with modern web apps
+Horizontal scaling with sharding is straightforward
+Aggregation pipeline is powerful for complex queries

Cons

-Lack of enforced schema can lead to messy data over time
-Joins are clunky compared to relational databases

Compare:vs Firebase vs DynamoDB vs Amazon DynamoDB

Elasticsearch

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The search engine that thinks it's a database. Great for logs, but good luck with transactions.

Why we picked it

Elasticsearch is a search engine first, a document database second. Its inverted index and near-real-time search are unmatched for log analytics and full-text search, but it lacks ACID transactions and has poor join support. MongoDB handles general-purpose document storage better; Elasticsearch wins only when search speed is the primary requirement.

→ Use it when your workload is primarily search or log analysis, and you can tolerate eventual consistency and no multi-document transactions.