Beyond SQL and MongoDB: Choosing the Right Database in the AI Era

The Database Landscape in 2025

For most of the 2010s, the database conversation was simple: use PostgreSQL or MySQL for relational data, MongoDB for documents, Redis for caching. That was essentially the entire decision tree for the vast majority of applications.

The AI era has shattered that simplicity. Building AI-powered applications exposes you to a dramatically expanded database landscape — one where the right choice depends on your workload characteristics, your team's infrastructure expertise, your cloud provider, and whether your data is structured, document-like, time-series, or embedding-based.

This guide maps the modern database landscape and helps you understand when to reach for each option.

The Foundation: Why Different Databases Exist

All databases trade off between different properties. The CAP theorem tells us a distributed database can only guarantee two of three: Consistency, Availability, and Partition tolerance. But beyond CAP, databases also make choices about:

• Data model — Relational (rows/tables), document (JSON), key-value, wide-column, graph
• Query model — SQL, aggregation pipelines, key lookups, graph traversal
• Write optimization vs. read optimization — Log-structured merge trees vs. B-trees
• Scale model — Vertical scaling, read replicas, horizontal sharding, serverless

No single database is optimal for all these tradeoffs. The reason the landscape is so fragmented is that different workloads have fundamentally different needs.

Redis: The Indispensable Cache Layer

Every non-trivial production application at scale uses Redis. It is not optional infrastructure — it is necessary infrastructure.

Redis stores everything in RAM. This makes it orders of magnitude faster than any disk-based database. The canonical use cases:

Session storage — Web sessions need to be read on every authenticated request, must be shared across multiple application servers, and should expire automatically. Redis handles all three natively.

Rate limiting — Count requests per user per time window with atomic INCR operations. No race conditions, no locked rows.

Leaderboards — Redis Sorted Sets maintain a scored ranking in O(log n) time. Real-time gaming leaderboards, sales rankings, feed scoring — all trivial with ZADD and ZREVRANGE.

Job queues — Libraries like BullMQ use Redis as a persistent job queue. Push background tasks into a Redis list; workers pop and process them.

The AI connection: Redis has become critical for AI applications specifically. LLM API calls cost money and take time. Caching responses to identical prompts in Redis serves them in sub-milliseconds at zero API cost. At scale, semantic caching — using Redis Stack's vector capabilities to match *similar* prompts, not just identical ones — reduces costs further. AI application developers who don't build a caching layer are burning money.

Cassandra: When Writes Are Your Bottleneck

Apache Cassandra is the answer to a specific question: what do you use when you need to write millions of rows per second, across multiple regions, with no single point of failure?

The architecture answer is a masterless, peer-to-peer cluster where every node is equal. There is no primary. Data is replicated across multiple nodes based on a consistent hash of the partition key. Write to any node; the cluster handles replication. Lose several nodes; the cluster continues operating.

The write throughput story is real: a modest Cassandra cluster can sustain millions of writes per second. PostgreSQL on a single server caps out far below that, even with optimized write paths.

Where Cassandra shines:

• IoT sensor data — thousands of devices writing millions of readings per day
• User activity logs — click streams, page views, search queries
• Time-series metrics — infrastructure monitoring, financial tick data
• Multi-datacenter applications — Cassandra's NetworkTopologyStrategy replicates across data centers natively

The tradeoff you accept: Cassandra requires a complete mindset shift in data modeling. JOINs don't exist. You model your tables around your queries. The same data often lives in multiple tables, each optimized for a specific access pattern. For teams coming from relational databases, this is a significant investment.

AI use case: Cassandra is the online (low-latency serving) layer of many enterprise feature stores. Machine learning models need pre-computed features served with low latency at prediction time. Cassandra's sub-millisecond reads by partition key, combined with its write throughput for feature updates, makes it the go-to for this pattern.

Amazon DynamoDB: Serverless Scale on AWS

DynamoDB is Amazon's answer to the database question for teams fully committed to AWS. It is fully managed, serverless, and designed to handle arbitrary scale with single-digit millisecond latency.

You don't provision servers. You don't tune configurations. You don't manage replication. AWS handles all of it. In On-Demand mode, DynamoDB scales instantly from zero traffic to millions of requests per second without any pre-provisioning.

The single-table design pattern is DynamoDB's signature modeling approach. Because DynamoDB charges per request, making multiple requests to fetch related data is expensive. Instead, you store all entity types in a single table using a composite key structure that lets you fetch a user and all their orders in a single query.

The AI connection: DynamoDB Streams + Lambda is a powerful pattern for AI pipelines. When a document is inserted into DynamoDB, a Lambda function triggers automatically, generates an embedding via your AI provider, and stores it in a vector database. Your application writes to DynamoDB; AI enrichment happens asynchronously in the background.

Who should use DynamoDB: Teams deeply invested in the AWS ecosystem. If your application already runs on Lambda, ECS, or EC2, and you want a database with zero operational overhead, DynamoDB is compelling. If you're multi-cloud or cloud-agnostic, the AWS lock-in is a real concern.

FerretDB: Open Source MongoDB Without the License Drama

MongoDB changed its license from AGPL to SSPL in 2018. SSPL is source-available, not open-source — it requires companies offering MongoDB as a managed service to open-source their entire stack. Many open-source projects couldn't use it.

FerretDB is the community's response: a proxy that implements MongoDB's wire protocol, storing data in PostgreSQL under the hood. Your application uses the standard MongoDB driver. From the application's perspective, it's connecting to MongoDB. The data physically lives in PostgreSQL.

The hidden superpower: because FerretDB stores documents as JSONB in PostgreSQL, you can run analytics directly on your document data using SQL. Your MongoDB-compatible application coexists with SQL queries in psql. Data warehousing becomes simpler.

Best for: Teams that chose MongoDB for its document model but want a truly open-source stack, don't want vendor lock-in, or already operate PostgreSQL infrastructure and don't want to add another database system.

Couchbase: Enterprise NoSQL with SQL Familiarity

Couchbase combines in-memory performance (every document is cached in RAM) with a SQL-like query language (N1QL) and a flexible JSON document model. N1QL is its killer feature for teams coming from relational databases — it looks and behaves like SQL, querying JSON documents with SELECT, JOIN, GROUP BY, and UNNEST.

Couchbase Capella (the managed cloud service) adds vector search, making it a strong choice for teams building RAG applications who want their document store and vector search in the same system.

Mobile sync: Couchbase Lite + Sync Gateway provides a battle-tested offline-first mobile solution with enterprise support.

CouchDB: When Offline-First Is Non-Negotiable

Apache CouchDB has one scenario where it is clearly the best choice: offline-first, sync-heavy applications. CouchDB's revision-based conflict detection and peer-to-peer replication protocol handle the messiness of syncing data modified in multiple places.

PouchDB — a JavaScript database that runs in the browser — implements the same protocol. The result: a web application that works completely offline. When the user regains connectivity, PouchDB syncs with CouchDB automatically.

Field data collection, rural healthcare applications, logistics tools — any application that must function without reliable connectivity — this is CouchDB's domain.

PostgreSQL with JSONB: The Hybrid Option

Before reaching for a dedicated document database, consider that PostgreSQL's JSONB type is surprisingly capable. If your data is *mostly* relational with *some* variable JSON fields, a single PostgreSQL deployment with GIN-indexed JSONB columns may be all you need. FerretDB is actually built on this insight.

Choosing the Right Database: A Decision Framework

Rather than prescribing a single answer, here's how to think through the decision:

| Need | Database |

|------|----------|

| Sub-millisecond caching, sessions, rate limiting | Redis |

| Millions of writes/second, time-series, multi-region | Cassandra |

| Zero ops overhead on AWS, unpredictable scale | DynamoDB |

| MongoDB API with open-source licensing | FerretDB |

| SQL-like queries on JSON, enterprise support | Couchbase |

| Offline-first sync with browser clients | CouchDB + PouchDB |

| Relational data with some flexible JSON fields | PostgreSQL JSONB |

| Complex queries, ACID, joins, new project | PostgreSQL |

The Multi-Database Reality

Most production AI applications use several databases simultaneously:

• PostgreSQL for users, billing, and relational data
• Redis for sessions, caching, and rate limiting
• A vector database for semantic search
• DynamoDB or Cassandra for high-throughput event logging

This is normal. Each database does what it's best at. The art is in drawing clear boundaries between them. Understanding the landscape — knowing when to reach for Redis versus Cassandra versus a document database — is one of the highest-leverage skills an architect can develop.

Explore our tutorials on Redis, Cassandra, DynamoDB, CouchDB, Couchbase, and FerretDB to go deeper on any of these systems.