Postgres vs MySQL: which to pick in 2026

Choosing between Postgres and MySQL in 2026 comes down to one question: how weird does your data model get? If your app is mostly users, orders, and standard CRUD with read-heavy traffic, MySQL is faster to start, simpler to operate, and still powers a huge slice of the web. If you need JSONB, full-text search, geospatial queries, complex joins, custom types, or extensions like pgvector for AI workloads, Postgres wins, and it isn't close.

Both are battle-tested, free, and good enough that the wrong choice will not kill your startup. The right choice will save you a migration in year three.

The short answer

For most new SaaS apps shipping in 2026, default to Postgres. The ecosystem has tilted: Supabase, Neon, Railway, Render, and Vercel Postgres all default to it; Rails 7+ and Django both prefer it; pgvector made Postgres the obvious pick for any product with embeddings or RAG; and AWS, GCP, and Azure have all invested heavily in managed Postgres offerings.

MySQL is still the right pick if you are running a high-read content site (think WordPress, Shopify-style storefronts), if your team has deep MySQL ops experience, or if you need the specific replication topology MySQL offers out of the box. It is not a worse database. It is a different shape.

MySQL overview

MySQL has been the default web database since the LAMP era. It is fast, well-understood, and runs more of the internet than people realize. Facebook, YouTube, Shopify, GitHub, Uber, and Airbnb all run massive MySQL fleets (most with heavy customization, but still MySQL at the core).

Where MySQL wins:

Read-heavy workloads. MySQL's storage engine (InnoDB by default) is tuned for fast point lookups and simple range scans. For a content site serving 50,000 reads per second against indexed columns, it is hard to beat.
Replication maturity. MySQL's async replication is older, simpler to reason about, and supported by every hosted provider. PlanetScale, AWS RDS, Aurora MySQL, and Vitess all ship production-grade MySQL clustering.
Operational simplicity. Smaller memory footprint, easier to back up with mysqldump, simpler config surface. A solo founder can run MySQL on a $10 droplet and not lose sleep.
Existing ecosystem fit. WordPress, Magento, phpMyAdmin, and a generation of PHP/Java codebases assume MySQL. If you are forking one of these, do not switch.

Where MySQL hurts:

JSON support is a second-class citizen. MySQL has a JSON column type, but indexing, querying, and updating nested JSON is awkward compared to Postgres JSONB.
No native vector search. As of 2026, there is no equivalent to pgvector that ships with stock MySQL. You will end up bolting on Pinecone, Weaviate, or a separate vector DB.
Weaker ANSI SQL compliance. Window functions and CTEs only landed in MySQL 8.0 (2018), and edge cases still bite. Postgres has had them for over a decade.
No true partial indexes, no expression indexes on functions, weaker constraint handling. These sound academic until your query planner falls over at 10 million rows.

Postgres overview

Postgres started as an academic database and never lost the academic obsession with correctness. It is slower to learn, has more features you'll never use, and rewards teams that read the docs.

Where Postgres wins:

JSONB. Store schemaless JSON with full GIN indexing, query into nested fields with operators, and treat half your table as a document store while the other half stays relational. There is no real MySQL equivalent.
Extensions. pgvector for embeddings, PostGIS for geospatial, TimescaleDB for time-series, pg_partman for partitioning, pg_trgm for fuzzy search. The extension ecosystem is the secret weapon.
Concurrency model. Postgres uses MVCC by default with no read locks, which makes mixed read/write workloads behave better at scale. You will hit MySQL's gap-locking issues before you hit Postgres equivalents.
AI workloads. If you are building anything with embeddings (semantic search, RAG, recommendation, dedup), pgvector is the path of least resistance. Storing your operational data and your vectors in the same database removes a whole class of sync bugs.
Stricter typing. Postgres will refuse to insert a string into an integer column. MySQL will silently truncate or coerce. In 2026 this matters more, not less, because LLM-generated code makes type errors easier to ship.

Where Postgres hurts:

Connection overhead. Each Postgres connection is a forked process with ~10MB of memory. Hit a few hundred concurrent connections and you need PgBouncer or a similar pooler. MySQL handles this natively.
VACUUM. The MVCC model means dead tuples accumulate and need to be vacuumed. Misconfigured autovacuum on a high-write table can ruin your week. MySQL's InnoDB sidesteps this entirely.
Replication is younger. Logical replication landed in Postgres 10 (2017). It works. It is not as battle-tested as MySQL's replication, and tooling (Debezium, etc.) is still maturing for some use cases.
More config surface. postgresql.conf has hundreds of knobs. The defaults are conservative and usually wrong for production.

Head-to-head comparison

Factor	Postgres	MySQL
Default for new SaaS in 2026	Yes (most providers default to it)	No (still huge install base)
JSON / JSONB support	Best in class, GIN-indexable	Adequate, awkward to query
Vector / AI workloads	pgvector ships everywhere	No first-party option
Read-heavy raw throughput	Strong	Slightly stronger
Concurrent writes (MVCC)	Excellent	Good (gap locks can bite)
Replication maturity	Solid (logical repl since 2017)	Best in class (decades old)
Geospatial	PostGIS (industry standard)	Basic, third-party for serious use
Full-text search	Native, good enough for most	Native, weaker
Hosted options	Supabase, Neon, RDS, Aurora, Cloud SQL, Render, Railway, Vercel, Crunchy	RDS, Aurora, PlanetScale, Cloud SQL, Vitess
Operational simplicity	Medium (vacuum, pooling)	High
Strict typing / data integrity	Strict	Lax (coerces silently)
Best fit	Mixed workloads, AI features, complex models	Read-heavy CRUD, simple schemas, existing MySQL teams

When to choose Postgres

You are starting a new SaaS in 2026 and have no strong reason to pick otherwise. The ecosystem default has shifted, and most senior engineers will reach for Postgres first.
You want vector search, semantic similarity, or any AI feature involving embeddings. pgvector keeps your vectors next to your operational data.
Your data model has any of: nested JSON, geospatial coordinates, time-series, custom types, or queries that need window functions and CTEs.
You expect mixed read/write workloads (a typical B2B SaaS dashboard) and want predictable concurrency.
Your team is small enough that paying the connection-pooling tax (PgBouncer or Supavisor) is fine.

When to choose MySQL

You are running WordPress, Magento, or any off-the-shelf PHP stack. Don't fight the framework.
Your workload is dominated by simple key-based reads at very high QPS, and you want the lowest-overhead path. Vitess and PlanetScale are exceptional here.
Your team has 5+ years of MySQL operational experience and your replication, backup, and monitoring setup all assume MySQL. Switching costs real time.
You are fine without first-party vector search, geospatial, or rich JSON, and your schema is genuinely relational and stable.
You need horizontal sharding at scale and want to use Vitess (the YouTube/Slack-grade sharding layer for MySQL).

What about SQLite, DuckDB, and the embedded tier?

Worth naming because the question keeps coming up. SQLite (especially with Litestream or LiteFS) is now production-credible for small SaaS apps. Levels.fyi runs on SQLite. Tailscale's coordination plane runs on SQLite. If you are building something where every customer can be served by a single writer, SQLite is the cheapest, simplest answer in 2026.

DuckDB is the OLAP analog: in-process columnar engine, ideal for analytics dashboards over moderate data. It is not a replacement for Postgres or MySQL, it is a replacement for "should we set up Snowflake yet?" The honest answer for most pre-Series-A startups is no, run DuckDB on the data you already have.

Neither displaces the Postgres vs MySQL choice for transactional web apps, but they should sit in your decision tree before you reach for either.

The third option most people miss

Most posts in this category end with "pick one and move on." The thing that actually slows founders down is not the database choice, it is having someone on the team who can set up replication, tune autovacuum, write the right indexes, and keep query plans honest as the schema evolves.

If that person is you and you've done it before, fine. If it is not, the answer is to bring in someone who has, for the 1 to 3 weeks it takes to get the foundation right, and then own it yourself.

That is the gap Cadence fills. Founders book vetted senior engineers by the week (Senior tier is $1,500/week, Lead tier is $2,000/week for architecture and scaling work). Every engineer on the platform is AI-native by default, vetted on Cursor, Claude Code, and Copilot fluency before they unlock bookings. There is no monthly contract, no recruiter, and a 48-hour free trial so you can see if the engineer can actually tune your Postgres before you pay anything. Same shape works if you are stuck on a MySQL migration or a Vitess rollout.

Compared to hiring a full-time DBA (12 weeks, $180K+ loaded cost in the US market), or going to a freelance marketplace and waiting for proposals, the booking model is faster and reversible. Compared to doing it yourself with Stack Overflow and Claude, you skip the 3-week ramp where you don't know what you don't know.

What to do this week

If you are starting fresh: spin up a Postgres instance on Supabase or Neon. Both have generous free tiers. Use Drizzle or Prisma so you can move providers later without rewriting queries.
If you are on MySQL and it is working: stay. Migrations cost weeks and are rarely worth it unless you specifically need pgvector or PostGIS.
If you are on MySQL and you want vector search: evaluate keeping MySQL for transactional data and adding pgvector-on-Postgres or a managed vector DB for embeddings, before doing a full migration.
If you are stuck on a query plan, replication setup, or index strategy: book a Senior or Lead engineer on Cadence for a week. Database tuning has a fast feedback loop and is exactly the kind of bounded-scope work the booking model is built for.

Try Cadence for one week. Book a senior engineer who has shipped Postgres and MySQL in production, get a 48-hour free trial, and replace the engineer any week with no notice. See how Cadence compares.

FAQ

Can I migrate from MySQL to Postgres later?

Yes, but it is non-trivial. Tools like pgloader and AWS DMS handle 80% of the work; the painful 20% is usually around stored procedures, triggers, and SQL dialect differences (MySQL's LIMIT x, y vs Postgres's LIMIT y OFFSET x, JSON function names, etc.). Plan for 2 to 6 weeks for a mid-sized app. The bigger reason to pick right the first time is that the code paths assuming MySQL semantics (silent type coercion, lax constraints) are scattered everywhere by year two.

Which is faster, Postgres or MySQL?

Both are fast enough for 99% of workloads. MySQL is typically faster at simple key-based reads at very high QPS. Postgres is faster at complex queries, mixed workloads, and anything involving JSON or full-text search. If you are picking a database based on benchmark microseconds, you are probably optimizing the wrong thing.

Is Postgres really better for AI and LLM apps?

Yes, mostly because of pgvector. Storing embeddings in the same database as your operational data eliminates an entire sync layer, and the indexing options (HNSW, IVFFlat) are good enough for production RAG. MySQL has no equivalent in 2026 stock builds, so you end up running a separate vector store, which adds operational complexity and consistency bugs.

Should I just use a managed service and not think about this?

For most early-stage startups, yes. Supabase (Postgres), Neon (Postgres), or PlanetScale (MySQL) handle the operational pain. The tradeoff is vendor lock-in around proprietary features (Supabase's auth, PlanetScale's branching). Use the SQL-standard subset where possible so you can move later. The team building a startup without a technical co-founder should especially default to managed: ops time you don't have is ops time you should buy.

What about NoSQL options like MongoDB or DynamoDB?

For most web app workloads in 2026, the answer is no. Postgres with JSONB covers 90% of the "we need flexible documents" case, and you keep the option of joining and constraining when you need to. DynamoDB is excellent for known-access-pattern, hyperscale workloads (think: every request is a known key lookup); it is a poor fit if your query patterns are still evolving, which they will be for any pre-Series-B startup.

All posts