--- title: "Is MariaDB Used in Production? Real-World Setups, Proof & AI" publish_date: 2025-10-16 updated_date: 2025-11-13 author: "Alejandro Duarte" tags: - name: "ai" url: "/resources/blog/tag/ai.md" - name: "architecture" url: "/resources/blog/tag/architecture.md" - name: "Cloud" url: "/resources/blog/tag/cloud.md" - name: "High Availability" url: "/resources/blog/tag/high-availability.md" - name: "Vector Search" url: "/resources/blog/tag/vector-search.md" --- # Is MariaDB Used in Production? Real-World Setups, Proof & AI When interacting face-to-face with developers at meetups and conferences, I frequently get asked whether MariaDB is used in production for mission critical and big applications. **Short answer: yes—MariaDB is used in production by banks, telcos, SaaS, and high-traffic platforms.** If you’re a developer wondering whether *“mariadb used in production”* is reality or just a marketing line, this post gives you **proof, typical production architectures**, and **how AI/vector search fits in**—so you can make an informed call. ***Disclosure: I work in Developer Relations at MariaDB and collaborate with teams running real production workloads. The guidance below reflects that hands-on exposure plus public case studies.*** ## What “production” means for MariaDB (HA, performance, security, scale) When I say “production,” I’m not talking about **a toy demo** or a single quiet Docker container on a dev laptop. **Production means your database:** - [**Survives hardware failures**](https://www.youtube.com/watch?v=y8eLffT9-8Q) without you scrambling to restart services. - **Handles planned outages gracefully** (e.g., [upgrades](https://www.youtube.com/watch?v=yqqeUXBMcJ4)). - [**Performs well**](https://mariadb.org/how-mariadb-and-mysql-performance-changed-over-releases/) under load. - Is [**secure by default**](https://www.youtube.com/watch?v=2qgi65XeN9Y). - [**Scales**](https://staging-mdb.com/database-topics/scalability/) up and down as needed. MariaDB has tools for all of this. [MaxScale](https://staging-mdb.com/products/maxscale/) reroutes connections [**automatically**](https://programmingbrain.com/2024/03/high-availability-and-resiliency-with-maxscale) when a primary fails and can **replay in-flight queries** so the app doesn’t notice (developers love this). [mariadbbackup](https://staging-mdb.com/docs/server/server-usage/backup-and-restore/mariadb-backup) takes **hot physical backups**, so you don’t have to schedule downtime just to be safe (DBAs love this). Add **TLS, encryption at rest, and long-term support releases** like [MariaDB 11.8 LTS](https://www.youtube.com/shorts/B5KtyM4jNkw), and you get a stack stable enough for on-call… **with sleep** (DevOps love this). And if you’d rather not manage clusters,[ **MariaDB Cloud**](https://staging-mdb.com/products/cloud/) gives you **fully managed** HA, backups, serverless deployments, and scaling. ## Who uses MariaDB in production? Logos, industries & case studies MariaDB runs **mission-critical** workloads at **banks, telcos, SaaS, and media**. Highlights from public [customer stories](https://staging-mdb.com/resources/customer-stories/) include: - **DBS Bank** (multiple production applications) - **ServiceNow, Nokia, Telefónica, Visma, Virgin Media O2, Samsung SDS** - **Wikipedia** runs on MariaDB There are many more, but that list alone shows it’s not just a dev toy. **It’s also** [**enjoyable**](https://www.youtube.com/shorts/3PH5bnfqROs) **to build with**. ## Common production architectures (single node, Galera, MaxScale, Kubernetes, Cloud) From what I’ve seen, most teams standardize with one of these topologies: 1. **Single node:** One node handling **non-mission-critical** apps. Clean path to scale later. 2. **Galera clusters:** Multi-primary replication. Reads and writes on any node. **High-availability** for critical apps. 3. **Primary + replicas with MaxScale:** A **write primary** with **read replicas**. **MaxScale promotes replicas** automatically on primary failure, enabling **HA and read scalability**. 4. **Kubernetes with Enterprise Operator:** Declarative deployments, backups, automated failover, rolling updates—**GitOps-friendly**. 5. **MariaDB Cloud:** Fully-managed, highly available, scalable, and **AI-ready** MariaDB on **AWS**, **GCP**, and **Azure**. These patterns are “boring” on purpose—reliable, repeatable, and production-proven. You want a database that just works—bonus points if it’s [fun to use](https://staging-mdb.com/resources/blog/building-a-portable-database-server/). ## AI with MariaDB: production-grade vector search & HNSW indexing MariaDB is not only a [relational database](https://www.youtube.com/shorts/v0yTEOebNnk)—it’s also a [vector database](https://www.youtube.com/shorts/ym_qikhh7uM). You can **store and search embeddings** directly in MariaDB: - `VECTOR(N)` [type](https://staging-mdb.com/docs/server/reference/sql-structure/vectors/vector-overview) - Distance functions like `VEC_DISTANCE_COSINE` and `VEC_DISTANCE_EUCLIDEAN` - **HNSW**-based vector index for **fast ANN search** - Load/read helpers `VEC_FromText` and `VEC_ToText` **Plain English:** you can build **semantic search** or **RAG chatbots** **without adding another database**. (If you prefer managed, **MariaDB Cloud** is **AI-ready** out of the box.) Example: ` CREATE TABLE product_embeddings (

id INT PRIMARY KEY,

embedding VECTOR(768)

);





-- Find the most similar embeddings to a given vector

SELECT id

FROM product_embeddings

ORDER BY VEC_DISTANCE_COSINE(

embedding,

VEC_FromText('[0.12,0.34,0.56, ... ]') -- search vector here

) ASC

LIMIT 5; ` ``` CREATE TABLE product_embeddings ( id INT PRIMARY KEY, embedding VECTOR(768));-- Find the most similar embeddings to a given vectorSELECT idFROM product_embeddingsORDER BY VEC_DISTANCE_COSINE( embedding, VEC_FromText('[0.12,0.34,0.56, ... ]') -- search vector here) ASCLIMIT 5; ``` **Performance:** [Independent tests](https://smalldatum.blogspot.com/2025/01/evaluating-vector-indexes-in-mariadb.html?utm_source=chatgpt.com) suggest MariaDB vector indexes can be **very competitive**, sometimes **up to 3× faster than pgvector** at similar [recall](https://en.wikipedia.org/wiki/Precision_and_recall), with **HNSW** delivering strong query latency. [MariaDB’s own benchmarks](https://staging-mdb.com/resources/blog/how-fast-is-mariadb-vector/?utm_source=chatgpt.com) also show wins versus dedicated vector engines like RediSearch. **Want a deeper dive?** Watch the [vector/GenAI webinar](https://go.mariadb.com/25Q1-GLBL-WBN-VectorGenAI-2025-03-26_Registration-LP.html) and see end-to-end patterns for embeddings, indexing, and retrieval. In addition to vector storage, MariaDB provides an official [**MCP server**](https://www.youtube.com/watch?v=N5T7jvvwEW0) that enables AI models to interact with the database. ## Conclusion & next steps **Yes—MariaDB is used in production** by **banks, SaaS, telcos**, and **Wikipedia**. **Architectures are proven** (Galera, MaxScale, Operator, Cloud), and **AI/vector search** runs **in the same database** you already trust for transactions. **Your next steps:** 1. **Pick an HA topology** ([Galera](https://staging-mdb.com/docs/galera-cluster) vs. [primary/replicas](https://www.youtube.com/watch?v=QKUtvt3_y6Y) + [MaxScale](https://staging-mdb.com/docs/maxscale)) that matches RPO/RTO. 2. **Set up backups** with **mariabackup** and test recovery. 3. **Prototype vector search** with **VECTOR(768) + HNSW** and measure latency/recall on your data. 4. Prefer managed? **Start with** [MariaDB Cloud](https://staging-mdb.com/products/cloud/) for a faster path to production. **Ready to go deeper?** **Join the** [**MariaDB 101 session**](https://go.mariadb.com/MariaDB101-2024-10-16_Registration-LP.html), explore [**customer stories**](https://staging-mdb.com/resources/customer-stories/), and try the [**vector webinar**](https://go.mariadb.com/25Q1-GLBL-WBN-VectorGenAI-2025-03-26_Registration-LP.html) to see AI in action.