PostgreSQL vs MariaDB: which database fits your self-hosted stack
Choose PostgreSQL when you are building new applications and want a database under the PostgreSQL License, a liberal license similar to BSD or MIT, with no single vendor controlling the code. Its major versions arrive about once a year and each one is supported for five years after its initial release, which gives you a predictable upgrade calendar. For high availability, PostgreSQL does not detect a failed primary or promote a standby by itself, so you pair it with Patroni on virtual machines or CloudNativePG on Kubernetes. Choose MariaDB when you already run MySQL-based applications and need a drop-in path with a high degree of MySQL compatibility, a GPLv2 server maintained by the MariaDB Foundation, and Galera synchronous multi-primary clustering if writes must land on several nodes. Neither choice is a fallback from the other: each is a sound answer for a different starting point.
The decision turns on four questions. First, application compatibility: if your software speaks the MySQL dialect, connection behavior and SQL features will decide the migration effort, and that check should happen before any infrastructure work. Second, release tolerance: MariaDB publishes a yearly long-term release with community binaries supported for three years after general availability and critical and security fixes for two more years, plus quarterly rolling releases, so you must confirm which track your upgrade process can absorb. Third, HA design: MariaDB's built-in replication with global transaction IDs and Galera are different failure models from PostgreSQL's hot standbys with external failover, and you should map each to your recovery objectives. Fourth, operator skills: a team fluent in Patroni or CloudNativePG will run PostgreSQL differently from a team fluent in Galera, so ask who on your staff will own failover drills and major upgrades. Confirm current support dates on the MariaDB Foundation's About page and the PostgreSQL versioning policy before you commit.
Which database fits which scenario
This table maps common scenarios to the database that usually fits better, with the condition that makes each fit hold and what to confirm before committing. Read each row as a starting point for your own workload tests, not as a verdict on either engine. The main takeaway is that the MySQL history of your application and your need for multi-primary writes or vector search usually decide the choice more than general engine quality.
| Scenario | Better fit | Why and what to confirm |
|---|---|---|
| New application with no MySQL history | PostgreSQL | The PostgreSQL License is permissive and the release model is annual majors with quarterly minors. Confirm that your team is comfortable with PostgreSQL SQL and that your ORM or driver supports it. |
| Existing MySQL or MariaDB application | MariaDB | Lower porting effort when the application relies on MySQL behavior and client drivers. Confirm the exact MySQL features your code uses and test them against the target MariaDB version. |
| Multi-primary writes across sites | MariaDB | MariaDB offers Galera synchronous multi-primary clustering, while PostgreSQL replication is primary to standby. Confirm the write latency across sites, since synchronous multi-primary commits add network round trips to every write. |
| Workloads needing vector search | Either, depending on test results | MariaDB lists a native VECTOR type with HNSW indexing since 11.8, and PostgreSQL vector support depends on extensions. Confirm index types, recall and query speed on your own data before choosing. |
| Yearly LTS track with a quarterly rolling track | MariaDB | MariaDB announces a yearly long-term release and quarterly rolling releases. PostgreSQL ships one major per year with supported majors for five years, so confirm which cadence your change process can absorb. |
| Heavy MySQL-specific SQL or Oracle SQL mode needs | MariaDB | MariaDB keeps a high degree of MySQL compatibility and offers an Oracle SQL mode with PL/SQL-style routines. Confirm each routine and query pattern with a compatibility test, as compatibility is not complete. |
How each database is built and what that means for operations
PostgreSQL and MariaDB Server both replicate data and both can be made highly available, but they get there with different building blocks. The cards below separate the process model, storage engines, replication and clustering choices, and the on-call consequences of each, so the team can match the design to the skills it already has.
PostgreSQL single-server process model
PostgreSQL runs one primary per cluster, and hot standbys serve read-only queries while replicating with either synchronous replication, where a commit waits for standbys, or asynchronous replication. PostgreSQL does not detect a failed primary or promote a standby on its own, so an external tool must do that work. On call, check which failover tool owns promotion, how it fences the old primary, and how much data loss the chosen replication mode allows.
Failover is an external decision
Because PostgreSQL leaves failure detection and notification to outside software, the failover design lives in your tooling rather than in the database itself. Teams should confirm who runs the failover manager, where its configuration store sits, and whether a failed node can rejoin without manual repair. Test this path on a schedule, since a runbook that has never been exercised is the most common gap during an incident.
MariaDB pluggable storage engines
MariaDB separates the SQL layer from storage, offering InnoDB for transactional workloads, Aria, MyRocks, ColumnStore, Spider and S3 for other patterns. Engine choice changes backup behavior, crash recovery, disk usage and which tables can be moved to another node. Before committing, list which tables use which engine, because a mixed schema means on-call staff must understand several recovery and tuning models.
MariaDB replication with global transaction IDs
MariaDB offers asynchronous, semi-synchronous and parallel replication, and it tracks transactions with global transaction IDs that simplify repointing replicas after a switch. Semi-synchronous mode narrows the data-loss window but still depends on how replicas acknowledge commits. Confirm which mode your application requires, how replica lag is alerted on, and whether your promotion procedure uses the global IDs correctly.
Galera synchronous multi-primary clustering
Galera gives MariaDB a synchronous multi-primary model in which writes can land on several nodes and are certified across the cluster. This is a different operating model from primary and replica pairs, with its own rules for quorum, node rejoin and conflicting writes. Teams choosing it should confirm they can handle certification conflicts in the application and can recover a node that falls out of the cluster.
HA tooling changes the skills
Patroni on virtual machines needs a distributed configuration store and expertise in leader election, while CloudNativePG relies on the Kubernetes API for failover and on Kubernetes operations skills. MariaDB's built-in clustering shifts the work toward Galera or replication topology knowledge. Map each option to the people on call before choosing, because the failover tool you pick determines which runbooks, monitoring and training your team must maintain.
PostgreSQL vs MariaDB on replication, clustering and vector search
This table sets PostgreSQL and MariaDB Server side by side on the capabilities that most often decide a self-hosted database choice. Where the researched facts do not confirm a detail, the cell tells you what to verify in your own environment before you commit.
| Capability | PostgreSQL | MariaDB |
|---|---|---|
| License | PostgreSQL License, a liberal permissive license similar to BSD or MIT | GPLv2, which the MariaDB Foundation states will remain free and open source software |
| Governance | Community-run project with no single commercial owner of the server code | Built by a community together with the MariaDB Foundation and MariaDB plc, with the Foundation stating independence from any commercial entity |
| Major release cadence | A new major version about once a year, with minor releases at least every three months | A new long-term release announced yearly, and rolling releases announced quarterly |
| LTS support | Each major version supported for 5 years after its initial release, with no separate LTS track | Community binaries supported for 3 years after GA, then critical and security fixes for 2 more years; 12.3 is an LTS release with a May 28, 2026 GA date |
| Replication modes | Streaming hot standbys with synchronous or asynchronous replication, plus logical replication for table subsets | Asynchronous, semi-synchronous and parallel replication with global transaction IDs |
| Multi-primary clustering | Not built in; multi-primary designs need third-party products, so confirm the approach you would use | Galera synchronous multi-primary clustering; confirm version support and operating model with your DBA team |
| Automatic failover | Not provided by the server itself; external tools such as Patroni or CloudNativePG are required | Confirm the failover tooling and topology you plan to use for your MariaDB version |
| Vector search | Confirm the extension and index options you need in your PostgreSQL version and deployment | Native VECTOR data type with HNSW indexing since MariaDB 11.8 |
| Authentication methods | Password with SCRAM (MD5 deprecated in 18), GSSAPI, SSPI, LDAP, RADIUS, certificate, PAM and OAuth 2.0 | Pluggable authentication including ed25519, PAM and GSSAPI, plus role-based access control |
| Encryption | Confirm TLS and at-rest options against your PostgreSQL build and extensions | TLS and data-at-rest encryption are listed in the server README |
| Storage engines | A single core storage design; confirm extension-based storage options you need | Pluggable engines including InnoDB, Aria, MyRocks, ColumnStore, Spider and S3 |
| MySQL compatibility and Oracle SQL mode | Not a MySQL-compatible engine; application migration needs SQL and driver review | High degree of MySQL compatibility, plus an Oracle SQL mode with PL/SQL-style stored routines |
How licensing, governance and release tracks differ
PostgreSQL is released under the PostgreSQL License, a liberal open source license similar to the BSD and MIT licenses. MariaDB Server is licensed under GPLv2, and the MariaDB Foundation states that it will remain free and open source software under that license, independent of any commercial entity. The Foundation is a non-stock, not-for-profit organization incorporated in Delaware.
- Redistribution: The PostgreSQL License is permissive, so you can modify and redistribute the code with the notice retained. GPLv2 is copyleft, so distributing modified MariaDB Server binaries brings source-availability obligations.
- Governance evidence: MariaDB's independence commitment is a public statement from the Foundation, and the README says the server is built by a global community together with the Foundation and MariaDB plc. Confirm how PostgreSQL's project governs its own roadmap and contributions directly with the project.
- Team that never buys support: Both databases can run in production with no vendor contract, because both are available under open licenses and community releases.
- Forks and vendor changes: Ask who would maintain your chosen branch if a commercial party changed its direction, since the licenses alone do not answer that question.
Check with your legal team how the license applies to your distribution model, especially if you embed the database in a product or ship it to customers.
PostgreSQL releases a new major version about once a year, ships minor releases at least once every three months, and supports each major version for five years after its initial release. MariaDB announces a long-term release yearly, supports its community binaries for three years after general availability, and provides critical and security fixes for two more years. It also publishes rolling releases quarterly.
- PostgreSQL dates: Version 18 was first released on September 25, 2025 and is supported until November 14, 2030. Version 14 is supported until November 12, 2026.
- MariaDB LTS: Version 12.3 is an LTS release with a general availability date of May 28, 2026. The maintained LTS releases are 11.8, 11.4 and 10.11.
- Rolling track: Version 13.0 is a rolling release with community end of life in Q4 2026, so it is a weak base for a database you expect to run for years.
- Upgrade paths: PostgreSQL minor updates replace executables and restart without changing the storage format. Major upgrades can use dump and restore, pg_upgrade, or logical replication, which allows a major upgrade with only several seconds of downtime.
Map each support end date to your change calendar before you fix a version, and confirm the current dates on each project's support pages, since the dates here are the supplied ones.
The supplied research does not describe a paid edition, enterprise license or commercial support tier for either PostgreSQL or MariaDB Server, so this page does not compare paid editions. Both databases can be used under their open licenses without a contract. If you want contracted support, it comes from service providers or vendors, and their terms differ.
- What is not established: The sources do not describe a paid edition of either database, so do not assume one exists or that a feature is locked behind one.
- Who is paid for what: A support contract pays for a provider's engineers, response commitments and sometimes patching for specific branches. The software stays under its open license in either case.
- Recurring and one-time costs: Support is usually a recurring charge, while setup, migration and testing are usually one-time work. Quotes should separate the two.
- Branch coverage: With PostgreSQL's five-year major versions and MariaDB's LTS and rolling tracks, a support quote must name the exact versions it covers.
Before you sign, request written terms that name the versions covered, end of support dates, severity definitions, response expectations and the scope of security fixes for each branch.
What drives the running cost of each database
The running cost of either database is mostly people time, not licenses, because both engines are open source and neither charges for the server software. The cost components to separate in any quote are the infrastructure (VMs or Kubernetes capacity, storage, backup targets), the skills to operate replication and failover, the HA tooling you choose, backup and restore testing, upgrades, and any commercial support you buy. One-time costs include the migration of schemas and application SQL, any rework of queries for MariaDB or PostgreSQL behavior, and the initial HA build. Recurring costs include patching, minor and major upgrades, on-call coverage, and periodic restore tests. Ask the implementation partner to price each of these as a separate line, with the person or role paid for each task named.
On PostgreSQL, the largest recurring driver is the major upgrade path. Minor releases never change the internal storage format, so they mean replacing executables and restarting. For major versions, the three documented routes are dump and restore with pg_dumpall, pg_upgrade, and logical replication, which works between major versions with only several seconds of downtime according to the PostgreSQL upgrading documentation. Logical replication adds build and monitoring work during the cutover, while pg_upgrade is cheaper in time but needs a tested rollback plan. Support windows also matter: the supported versions run from 14 through 18, and 14 reaches end of support on November 12, 2026, so budget the upgrade work against those dates. Failover tooling is not bundled, so the HA tool you choose, such as Patroni or CloudNativePG, adds its own recurring operating effort.
On MariaDB, the cost driver is the release track you commit to. The Foundation announces a new long-term release yearly, with community binaries supported for 3 years after general availability and critical and security fixes for 2 more years, while rolling releases arrive quarterly. An LTS track lowers upgrade frequency, but a rolling release such as 13.0 has a community end of life in Q4 2026, so staying on rolling releases means upgrading more often. If you run Galera synchronous multi-primary clustering, expect extra recurring work around cluster state, node recovery and testing of quorum behavior. Before you sign, ask the partner to state which track they will support, how many major upgrades are in scope per year, and whether restore tests against your backup tooling are included in the recurring fee.
Moving a MySQL or MariaDB application to PostgreSQL, and the reverse
This sequence takes an application from one relational engine to the other while keeping the data verified, the cutover reversible for a defined window, and the high availability and backup jobs rebuilt rather than assumed to carry over.
Inventory dialect, routines and engine-specific features
The application team and the DBA list every SQL dialect dependency: stored procedures, triggers, events, views, user-defined functions, storage engine choices, character sets and collations, and any MariaDB-only features such as native VECTOR columns or Oracle SQL mode routines. Each item is tagged as portable, rewritable or blocking. The output is a signed inventory that the cutover plan references. You know it is done when every routine in the schema appears on the list with a disposition and an owner.
Map schema and data types
Map each MySQL or MariaDB column type to its PostgreSQL equivalent, paying attention to auto-increment columns versus identity columns, unsigned integers, TINYINT(1) booleans, ENUM and SET types, DATETIME and TIMESTAMP time zone behavior, and case sensitivity of identifiers and collations. Decide how indexes, foreign keys and unique constraints are carried across. The output is a reviewed DDL file for the target schema. You know it is done when a test load of a representative subset succeeds with no silent truncation or type coercion, and row counts and checksums match on sample tables.
Change driver, authentication and connection settings
Replace the MySQL or MariaDB client driver with a PostgreSQL driver and update connection strings, pooling settings and ORM dialect configuration. Review how the application authenticates: PostgreSQL deprecates MD5 password authentication in favor of SCRAM, and it also documents certificate, GSSAPI, LDAP and OAuth methods, so the team picks the method that matches its identity model and confirms it in the server configuration. The output is a branch of the application that connects to a test PostgreSQL instance. You know it is done when integration tests pass against that instance with the chosen authentication method and TLS enabled.
Rewrite queries and routines
Rewrite the queries that rely on MySQL-specific syntax, such as backtick quoting, LIMIT variants, GROUP BY behavior and implicit type conversions, and port stored routines to PL/pgSQL or the application layer. Routines are usually the largest manual effort, and each rewritten routine needs its own tests. The output is a set of migrated routines and a list of queries changed in the application. You know it is done when the regression suite covers each rewritten routine and the results match the source system for the agreed test data.
Choose the cutover method and rehearse it
Choose between a dump and restore with a write freeze, and a logical replication approach that keeps the target in step while the source stays live. Logical replication across engines is not provided by PostgreSQL itself for MySQL sources, so the team must confirm which change-data-capture tool it will use and whether that tool supports the data types in the inventory. Rehearse the full cutover on a copy of production data and record the elapsed time and the rollback steps. The output is a runbook with a go and no-go checklist. You know it is done when at least one rehearsal completes end to end and the team has timed it against its own window.
Validate data and application behavior
Compare row counts, checksums and sampled records between source and target, then run the application's end-to-end test suite and a set of business reports against both systems. Check sequence and identity values so that new inserts do not collide with migrated keys, and confirm that transactions, isolation levels and locking behavior meet the application's requirements. The output is a signed validation report. You know it is done when every mismatch is either fixed or accepted in writing by the application owner.
Cut over, keep a rollback path, then retire the old system
Freeze writes or switch the replication path at the agreed point, point the application at PostgreSQL, and keep the source in read-only mode for a period the team sets in advance. Once the rollback window closes and the reverse replication or data export path has been confirmed as no longer needed, decommission the MySQL or MariaDB instance and remove its credentials and firewall rules. The output is a closed change record and a retired host. You know it is done when the source is unreachable from application networks and the change record names who approved retirement.
Rebuild high availability, backups and monitoring
Rebuild the operational layer that does not transfer: the replication topology, failover tooling, backup jobs and monitoring. PostgreSQL does not detect primary failure on its own, so automatic failover needs an external tool, and a Galera or MariaDB replication design does not map one to one onto PostgreSQL streaming replication. Backup and point-in-time recovery must be rebuilt with PostgreSQL tooling and restore-tested. Dashboards and alerts are rewritten for PostgreSQL metrics. The output is a tested failover drill and a successful restore test. You know it is done when both have been run and their results are recorded.
What implementing each database involves and where projects run long
Implementing PostgreSQL in production is mostly a set of decisions you make early and then have to live with. Schema design and extension choices come first, because extensions you add now become dependencies during upgrades. High availability is the next decision: Patroni on virtual machines needs a distributed configuration store such as etcd, Consul, ZooKeeper or Kubernetes, while CloudNativePG on Kubernetes handles failover through the Kubernetes API. Backup and point-in-time recovery need continuous archiving configured and tested, and authentication needs a deliberate choice among SCRAM, certificates, LDAP, GSSAPI or OAuth 2.0, since MD5 password authentication is deprecated in favor of SCRAM. Confirm with your own security team which methods your identity provider can support before the cluster design is fixed.
MariaDB shifts the early work toward storage and replication. You select storage engines per table, typically InnoDB for transactional workloads, and decide whether you need asynchronous, semi-synchronous or parallel replication with global transaction IDs, or Galera synchronous multi-primary clustering. Each choice changes how writes conflict, how failover behaves and what your application must tolerate. The routine that follows is backup, upgrade and monitoring design, which must match the release track you choose: an LTS line for a longer support window, or a rolling release if you accept more frequent change. Confirm the current support dates for the branch you plan to run in the MariaDB Foundation's published policy before you commit.
Where projects run long is rarely the database install. Application SQL compatibility testing is the most common delay, because MySQL-style queries, stored routines, character sets and session settings behave close to MySQL but are not guaranteed identical, so plan a full test pass against production-like data. Failover testing takes longer than teams expect on both sides, especially for Patroni switchover and for Galera node loss under load. Backup restore drills must restore into a clean environment and prove the application starts, not just that a dump file exists. Finally, aligning the release track with an upgrade calendar is a recurring cost that many plans omit. DeployOpen delivers this work as fixed-scope engagements, with scope, deliverables and timelines agreed in writing before work starts. The scope depends on your environment, including your virtualization or Kubernetes platform, number of applications, compliance obligations and whether you need a managed upgrade routine, so request a written scope based on those facts.
Questions buyers ask before choosing PostgreSQL or MariaDB
How much effort does migrating from MariaDB to PostgreSQL take?
Effort depends mostly on how much of the application depends on MySQL or MariaDB behavior, not on the row count alone. The main drivers are SQL dialect differences in queries and DDL, stored routines and triggers, how the application handles case sensitivity, auto-increment and character sets, and any ORM layer that hides these differences. Teams with a thin data access layer and few stored procedures usually face a smaller job than teams with routines spread across the database. Ask for an inventory of stored objects, queries and driver usage before agreeing a scope, and ask whether the migration will include a parallel run for validation. Because the answer changes with the codebase, no duration is given here.
Can our team run PostgreSQL or MariaDB without outside help?
Yes, many teams run either database in-house, and the question is whether your staff have the skills and time for backups, patching, replication, failover testing and monitoring. Both databases need those tasks regardless of vendor, so the deciding factor is your existing operations maturity and on-call coverage. A team that already runs PostgreSQL in production may find the day-two work familiar, while a team that has only run MariaDB for a single primary may need training on high availability. DeployOpen can plan, implement, document and support either database in your infrastructure, or you can keep the work in-house after handover.
What skills does our internal team need for either database?
Plan for a database administrator or a platform engineer who can own backups, restores, upgrades, replication topology and access control. For PostgreSQL, the team should understand physical and logical replication, the authentication methods in pg_hba.conf, and how major upgrades work with pg_upgrade or logical replication. For MariaDB, the team should understand the chosen replication model, whether asynchronous, semi-synchronous or Galera, and how to handle its storage engines and TLS settings. Also assign someone to track the release calendar, since MariaDB offers both LTS and rolling tracks and PostgreSQL requires a yearly major upgrade cycle within its support windows.
Does PostgreSQL offer automatic failover without extra tools?
No. PostgreSQL provides hot standby servers and synchronous or asynchronous replication, but it does not include software that detects a primary failure and promotes a standby on its own. Automatic failover needs an external tool. On virtual machines, teams commonly use Patroni, which is MIT licensed and uses a distributed configuration store such as etcd, Consul, ZooKeeper or Kubernetes. On Kubernetes, CloudNativePG is an Apache-2.0 operator that handles failover through the Kubernetes API. Choosing between them is a design decision about where your databases run and which skills your team has.
Which MariaDB release track should we pick?
Choose the long-term release track for production systems that need a stable upgrade path, and use rolling releases only when you need newer features and can absorb more frequent upgrades. The MariaDB Foundation announces a new long-term release each year, with community binaries supported for 3 years after general availability and critical and security fixes for 2 more years. MariaDB 12.3 is an LTS release with a general availability date of May 28, 2026, and 11.8, 11.4 and 10.11 are also maintained LTS releases. MariaDB 13.0 is a rolling release with community end of life in Q4 2026, so it is a poor fit for a system you must keep running for years. Confirm the current end-of-life dates on the MariaDB Foundation pages before you commit.
Does vector search mean we must choose MariaDB?
No. MariaDB's README lists a native VECTOR data type with HNSW indexing since MariaDB 11.8, so it is a reasonable choice if you want similarity search inside a MySQL-compatible server. PostgreSQL can also be extended for vector workloads, but its extension ecosystem and indexing options are a separate evaluation. Before deciding, test your own query patterns, index build times, recall targets and memory use on your hardware, and compare the operational model with a dedicated vector store if one is already in your stack. The database choice should follow the wider application requirements, not the vector feature alone.
What should a quote for a PostgreSQL or MariaDB project include?
A useful quote lists the scope in writing before work starts. Ask for the target versions and release tracks, the hosting model (virtual machines, Kubernetes, bare metal or air-gapped), the replication and failover design, backup and restore tests, and the monitoring and access control to be configured. Also ask for the migration inventory, the validation approach, documentation and handover deliverables, and any ongoing support or upgrade retainer. Costs are one-time for design, implementation and migration, and recurring for support, patching and upgrades. DeployOpen fixed-scope engagements are agreed in writing with deliverables and timelines before work begins, and DeployOpen does not publish a price list, so the quote follows from that scope.
How DeployOpen helps choose, implement or migrate
DeployOpen is a Chainscore Labs company that plans, implements, hardens, backs up, upgrades and documents self-hosted open-source platforms inside the client's own infrastructure. For PostgreSQL vs MariaDB, we start from your application compatibility, replication and release-support requirements, then build and document the chosen database in your environment.
How we scope
- Written scope lists the applications, drivers and SQL features the database must serve.
- Scope records the target PostgreSQL or MariaDB versions and their support windows.
- Engagements are fixed-scope, with deliverables and timelines agreed in writing before work starts.
How we build
- We deploy the chosen database on your virtual machines or Kubernetes clusters.
- We configure replication, high availability tooling and authentication to match your written design.
- We test failover, backup restore and version upgrade steps before anything reaches production.
How we hand over
- Documentation covers configuration, replication topology, backup procedures and upgrade runbooks.
- Your team receives the evidence and runbooks needed for compliance assessments, without any audit claim from us.
- Knowledge transfer sessions walk your operators through routine maintenance and incident response.
How we support
- Ongoing support, upgrades and continuous testing are available as retainers after handover.
- Retainers can cover minor version updates and planned major upgrades on the release schedule you adopt.
- Ask any provider to confirm which support windows and patch cadence apply to your chosen version.
