Picking a database used to be a religious argument, relational versus document, SQL versus NoSQL, fought in forum threads that never resolved anything. In 2026 the honest answer is duller and more useful: most real applications end up running two or three databases at once, each doing the specific job it’s actually good at, and the argument has mostly moved from “which one” to “which one for this particular workload.”

Here’s where each option below actually earns its place, and where teams commonly pick the wrong one out of habit rather than fit.

Quick comparison

DatabaseTypeBest forPricing
PostgreSQLRelationalComplex queries, data integrityFree, open source
MySQLRelationalWeb apps, WordPress, wide hosting supportFree, open source
MongoDBDocumentFlexible, evolving schemasFree tier; Atlas paid from ~$9/mo
RedisIn-memoryCaching, sessions, real-time featuresFree, open source; managed tiers vary
SupabaseHosted PostgreSQLFirebase alternative, fast setupFree tier; paid from $25/mo

1. PostgreSQL

PostgreSQL has spent three decades earning a reputation as the database engineers reach for when correctness matters more than convenience. Full ACID compliance, genuinely advanced indexing, and native JSONB support mean it handles both rigid relational data and semi-structured documents without forcing a choice between the two, a flexibility that’s pulled it ahead of MySQL for a lot of new projects in recent years.

Pros: Enterprise-grade reliability that’s genuinely free, no license fee at any scale. Advanced features, window functions, full-text search, custom extensions, that used to require an expensive commercial database. Extension ecosystem, including PostGIS for geospatial data and pgvector for AI embeddings, covers use cases most relational databases can’t touch natively.

Cons: Configuration and tuning for genuinely high-throughput workloads takes real expertise, the defaults are conservative, not optimized out of the box. Slightly steeper learning curve than MySQL for a first-time database admin.

Best for: Applications where data integrity and complex querying matter more than raw simplicity, financial systems, analytics platforms, anything with genuinely relational data.

2. MySQL

MySQL remains the database most web developers meet first, largely because it’s the database WordPress, and a huge share of the web’s content management systems, run on by default. That installed base means hosting support is universal and cheap, and the operational knowledge required to run it is widely available.

Pros: The most widely supported database across shared and managed hosting, genuinely no hosting provider lacks MySQL support. Performance is solid for typical web application read patterns. Massive documentation and community knowledge built up over decades.

Cons: Some advanced features PostgreSQL handles natively require workarounds or third-party extensions in MySQL. Historically weaker on strict data-integrity enforcement, though MariaDB and recent MySQL versions have closed much of that gap.

Best for: Web applications, particularly anything running on WordPress or a similar CMS, where wide hosting compatibility matters more than advanced relational features.

3. MongoDB

MongoDB made its name by rejecting the rigid schema relational databases require, storing data as flexible, JSON-like documents that can evolve as an application’s needs change without a formal migration every time a field gets added. Atlas, its managed cloud service, has become the default way most teams actually run it in production.

Pros: Schema flexibility genuinely speeds up early-stage development, when a data model is still changing week to week. Horizontal scaling is more straightforward than sharding a relational database. Atlas removes most operational burden for teams that don’t want to run database infrastructure themselves.

Cons: That same schema flexibility becomes a real liability at scale without disciplined data-modeling practices, “flexible” easily becomes “inconsistent” across a large, long-lived application. Complex relational queries, joins across collections, are noticeably more awkward than in a relational database built for exactly that.

Best for: Applications with evolving, document-shaped data, content management, catalogs, user-generated content, where the schema genuinely isn’t stable yet.

4. Redis

Redis isn’t really competing with the databases above, it’s usually running alongside one of them, an in-memory data store built for speed rather than long-term storage. Caching, session management, real-time leaderboards, and pub/sub messaging are all jobs Redis does dramatically faster than a disk-based database ever could.

Pros: Genuinely blazing performance for the workloads it’s built for, in-memory access is orders of magnitude faster than disk-based reads. Versatile beyond simple caching: queues, rate limiting, real-time features all run cleanly on Redis. Simple to add alongside an existing primary database rather than replacing anything.

Cons: Data persistence is a real design decision, not automatic, an unconfigured Redis instance loses everything on restart. Memory costs scale directly with dataset size, so it’s genuinely expensive to use as a primary data store rather than a cache layer.

Best for: Caching, session storage, and real-time features layered on top of a primary database, not as a standalone system of record.

5. Supabase

Supabase took PostgreSQL and wrapped it in the developer experience that made Firebase popular, real-time subscriptions, built-in authentication, and file storage, while keeping the underlying database open source and genuinely portable rather than locking you into a proprietary format.

Pros: Dramatically faster time to a working backend than configuring PostgreSQL and auth separately, with real-time infrastructure built in rather than bolted on by hand. Built on standard PostgreSQL underneath, so you’re not locked into a proprietary query language the way Firebase requires. Generous free tier for early-stage projects and side projects.

Cons: Still a younger platform than Firebase with a smaller ecosystem of third-party tutorials and integrations. Costs scale meaningfully at real production traffic, worth modeling before committing a growing application to it.

Best for: Teams that want PostgreSQL’s power with Firebase’s development speed, particularly for projects that might need to migrate off a proprietary backend later.

Why most real applications run more than one database

The old debate assumed a single database had to handle everything, transactional data, caching, search, real-time updates, and that assumption is mostly gone from how production systems actually get built in 2026. A typical modern application runs PostgreSQL or MySQL as the system of record, Redis in front of it for caching and session data, and sometimes a dedicated search engine or vector database for anything approaching AI or full-text search at scale.

This isn’t over-engineering, it’s matching each tool to the job it’s actually good at. Forcing a relational database to handle millisecond-latency caching wastes its strengths; forcing Redis to be a permanent system of record ignores its actual design intent. The real skill in modern database architecture is knowing which workload belongs where, not finding one database that does everything adequately.

Scaling patterns that actually work, versus the ones that sound good in a meeting

“We’ll just shard it” is the most common database scaling plan that never survives contact with reality. Sharding, splitting a database across multiple servers by some partitioning key, genuinely solves scale problems, but it also introduces real complexity: cross-shard queries become expensive or impossible, and rebalancing shards as data grows unevenly is a genuinely hard operational problem most teams underestimate until they’re living inside it.

The scaling path that works for the overwhelming majority of applications is boring and sequential, not a single dramatic architecture decision made on day one. Start with vertical scaling, a bigger server, more RAM, faster disks, which solves most performance problems for longer than teams expect. Add read replicas next, routing read-heavy traffic like dashboards and reports away from the primary database that handles writes. Only reach for sharding once a single primary database genuinely can’t handle write volume even on the largest reasonable server, a threshold most applications never actually hit.

PostgreSQL and MySQL both support read replicas natively and well. MongoDB’s sharding is more mature than either relational option’s, a genuine advantage if horizontal scale is a known, near-term requirement rather than a hypothetical one being planned for prematurely.

Backup strategy: the thing everyone means to configure properly

A database without a tested backup and restore process isn’t really backed up, it just has a backup file sitting somewhere nobody has verified actually works. The distinction matters enormously the one time it counts, and “the one time it counts” always arrives without warning.

Automated daily backups are table stakes, and every managed option on this list, Atlas for MongoDB, Supabase, most managed PostgreSQL and MySQL hosts, includes them by default. The step teams actually skip is testing a real restore, not just confirming a backup file exists, but actually restoring it to a separate environment and verifying the data comes back intact and current. Schedule that test quarterly at minimum. A backup nobody has ever restored from is a hypothesis, not a safety net.

Point-in-time recovery, the ability to restore a database to its exact state at any specific moment rather than just the last nightly snapshot, matters more than most teams realize until a bad deploy corrupts data at 2pm and the last clean backup is from midnight. PostgreSQL supports this natively through write-ahead log archiving; confirm it’s actually configured, not just theoretically available, before assuming it’ll be there when needed.

Vector databases: the workload nobody was planning for two years ago

AI applications introduced a genuinely new database requirement that older systems weren’t built for: storing and searching high-dimensional vector embeddings for semantic search and retrieval-augmented generation. PostgreSQL’s pgvector extension has become a surprisingly popular answer, letting teams add vector search to an existing Postgres database rather than standing up an entirely separate specialized system.

Purpose-built vector databases exist for teams with genuinely heavy AI workloads where pgvector’s performance ceiling becomes a real constraint. For most applications adding AI features to an existing product, though, extending a database you already run and already understand operationally beats introducing a fourth or fifth system, one more thing to maintain and monitor, one more backup routine to remember.

Indexes: the single highest-leverage database change most teams skip

A slow query almost always traces back to a missing index, not a database engine that’s fundamentally too weak for the job. An index tells the database how to find rows without scanning the entire table, and the difference between a query with the right index and one without can be the gap between ten milliseconds and ten seconds on a table with real volume.

The practical rule: any column that appears in a WHERE clause, an ORDER BY, or a JOIN condition on a query that runs frequently deserves an index. That sounds obvious stated plainly, and yet a huge share of production slowdowns trace back to exactly this being skipped, usually because the query worked fine in development against a test table with two hundred rows and nobody re-checked it once the table held two million.

Over-indexing carries its own real cost, every index speeds up reads but slows down writes, since the database has to update every index on every insert or update. The right number of indexes is “exactly the ones your actual query patterns need,” discoverable by watching a database’s slow-query log rather than guessing, and pruning indexes nothing actually queries against periodically as an application evolves.

WordPress-specific database considerations

WordPress runs on MySQL or MariaDB by default, and for most sites the database itself is rarely the actual bottleneck, poor caching, unoptimized queries from a plugin, or under-provisioned hosting usually cause the slowdowns people blame on “the database.” Quality managed hosting like Kinsta includes database optimization and object caching, addressing the actual common culprits, slow queries and repeated database hits for the same data, rather than requiring a database migration most WordPress sites never actually need.

Object caching specifically, storing frequently requested database results in memory via Redis or a similar layer, produces a bigger real-world speed improvement for most WordPress sites than any database engine swap would. Before assuming a site needs a different database entirely, confirm object caching is actually configured and working, since it’s the fix that resolves the underlying problem most people are actually experiencing.

A specific WordPress pattern worth checking directly: plugins that run a separate database query per item in a loop, fetching each post’s metadata one row at a time inside a foreach rather than batching the request into one query up front. This N+1 query pattern is the single most common cause of a WordPress site that feels sluggish specifically on pages with lots of items, a directory, a large archive, a product grid, and it’s a code problem, not a database engine limitation. No database swap fixes a plugin issuing five hundred queries where one would do.

For sites at genuine scale, tens of thousands of posts or a large multisite network, a managed host that includes query monitoring tools pays for itself quickly, since it surfaces exactly which plugin or theme function is issuing the slow or repeated queries rather than leaving an admin to guess.

Choosing without the religious argument

Start from the shape of the data, not from what a previous project used out of habit. Genuinely relational data with clear structure and integrity requirements points to PostgreSQL or MySQL. Rapidly evolving, document-shaped data points to MongoDB. Anything needing millisecond response times for repeated reads needs a cache layer like Redis regardless of what the primary database is. And a team wanting to move fast without managing infrastructure gets real value from Supabase’s managed approach on top of standard, portable PostgreSQL.

Bring order to every dataset by pairing database choice with the tools around it. After tuning your database engine, our guide to IT asset management suites covers lifecycle tracking for the broader infrastructure a database lives inside. And for the version control and deployment side of managing schema changes over time, source code management platforms rounds out the workflow.

FAQ

What is the best database for a new web application in 2026?

PostgreSQL is the strongest general-purpose default for most new applications, combining relational integrity with enough flexibility, JSONB support, extensions, to cover a wide range of needs without switching databases later. MySQL remains the pragmatic choice specifically for WordPress and similar CMS-based projects where hosting compatibility matters most.

Should I use MongoDB or PostgreSQL for a new project?

Choose MongoDB when your data model is genuinely still evolving and forcing a rigid schema would slow early development down. Choose PostgreSQL when data integrity, complex relationships between records, and consistent querying matter more than schema flexibility. Many teams start with MongoDB for speed and later regret the lack of relational integrity once the application matures, worth weighing honestly before committing.

Do I need Redis if I already have a primary database?

For any application with repeated reads of the same data, session management, or real-time features, yes, Redis as a caching layer produces a real, measurable performance improvement most primary databases can’t match on their own. It’s not a replacement for your primary database, it’s a speed layer that sits in front of it.

Is Supabase a good replacement for Firebase?

Yes, for teams that want Firebase’s fast development experience without Firebase’s proprietary lock-in. Since Supabase runs on standard PostgreSQL underneath, your data and schema remain portable to any other Postgres-compatible host if you ever need to migrate, which Firebase’s proprietary format doesn’t offer.

How do vector databases fit into a typical application stack?

Most applications adding AI-powered search or recommendation features don’t need a dedicated vector database from day one. PostgreSQL’s pgvector extension handles vector storage and similarity search well enough for moderate scale, letting a team add AI features to an existing database rather than introducing and maintaining an entirely separate system before it’s actually necessary.