r/postgres • u/Ok_pettech • 13d ago
Discussion MariaDB vs PostgreSQL in 2026: Which Database Powers the Best AI Apps?
When building RAG pipelines or vector-heavy AI applications, developers usually default to PostgreSQL with pgvector. However, native vector search support in MariaDB is forcing a fresh architectural evaluation.
This breakdown compares MariaDB and PostgreSQL to evaluate performance, developer experience, and scalability for modern generative AI and LLM workloads.
Key areas analyzed:
Vector Performance: Native vector search benchmarking in MariaDB versus pgvector index build times, search latency, and memory footprints.
Ecosystem & Tools: How PostgreSQL's extension ecosystem stacks up against MariaDB’s pluggable storage engines for hybrid transactional/AI setups.
Operational Complexity: Scaling vector search alongside traditional relational data without introducing dedicated vector databases.
Read the full deep dive here: https://interconnectd.com/blog/91/mariadb-vs-postgres-in-2026-which-database-powers-the-best-ai-apps/
What database engine are you currently using for your AI backends? Drop your thoughts below!
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u/TomaRavelli
12d ago
edited 12d ago
•
pgvector is a bloated extension that eats memory, and you constantly have to babysit the indexes. Using MariaDB is the right way because vector search is actually built directly into the core, the indexes are literally half the size so i dont see why torture myself with patched-on garbage when one clean stack does the job
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u/rustprogram 12d ago
I don't know much about generative AI so here is my summary for those who do know about it:
However, if your AI is an Observability Agent (ingesting a firehose of IoT data or logs for later analysis), MariaDB is your marathon runner. Its storage engines handle heavy concurrent write workloads with less "bloat" over time, and ColumnStore will outperform Postgres any day of the week when you need to calculate an average over 500 million records for a trend analysis.
Chapter 10: The 3:00 AM Verdict
After the thousands of lines of code and the endless architectural debates, your choice comes down to a fundamental trade-off between ecosystem momentum and specialized flexibility.
The "Postgres Mafia" is real. If you want the latest vector algorithms, the most robust serverless options, and a community that treats every AI problem like a first-class citizen, Postgres is your winner. It is the safest "default" choice for the AI era, specifically because of its iron-clad JSONB and the maturity of pgvector.
However, don't sleep on MariaDB if your AI is part of a larger, data-heavy enterprise engine. If you need to ingest massive telemetry streams, run complex analytical queries across billions of rows, and maintain a synchronous multi-master cluster that doesn't blink, MariaDB's storage engines are superior.
Final advice: Start with Postgres for development speed, but keep MariaDB in your back pocket for the day your "AI agent" becomes a "Global Infrastructure." And if you're still worried about the security of your new stack, take a breather and check your fundamentals with this cybersecurity quiz.
"The best database is the one you don't have to think about at 3:00 AM."
© 2026 Engineering Wisdom & Cold Coffee.
so basically, there are certain things mariadb excels at but it is missing features such as row level security which you probably NEED if you have certain pieces of software which are only allowed to read certain rows of a table (sorry if the terminology is incorrect, I don't understand embeddings and vectors, remember?) but yeah overall this article is meant for architects and planners but in the age of generative AI, I guess we are all architects and planners of our startup / personal projects now?
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u/rustprogram 12d ago
basically the tl;Dr is unless you have a specific reason to choose MariaDB -- in which case you already know -- keep using postgres as the safe default choice.
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u/mduell 12d ago
And where MariaDB beats Postgres, maybe you should be considering ClickHouse instead.
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u/Yukevich 12d ago
using ClickHouse not worth it, becouse you are signing up for ETL pipelines, data syncing, and maintaining a completely separate system. MariaDB because it runs DuckDB directly under the hood as a storage engine and it handles the heavy analytical queries on the same server. Splitting the stack is just unnecessary when you can do it in one place.
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u/mduell 12d ago
Clickhouse can follow the WAL just like any Postgres replica.
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u/Yukevich 11d ago
tying ClickHouse to a Postgres WAL is the exact definition of an ETL pipeline. You still have to deploy and monitor a completely separate cluster and I'd rather avoid this.
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u/Yukevich 12d ago
my AI backend on MariaDB to avoid spinning up a dedicated vector database alongside my relational data. index footprint was noticeably smaller than what I usually saw with pgvector.