Top Open-Source Databases in the Cloud PostgreSQL, MariaDB & More Explained

Let’s picture this to understand the significance of open-source cloud databases. Your team rolls out a new fraud‑detection model, traffic triples overnight and the database bill for your proprietary engine balloons to match.

Now imagine switching to a managed Postgres cluster that scales on demand, bakes in vector search and still costs less than renewing last year’s licence.

Scenarios like that are why Gartner expects 90 percent of new data and analytics deployments to run inside an established data ecosystem by 2025. But for boards, trend isn’t the sole decision-making factor.

They want predictable costs, bullet‑proof compliance and AI‑ready features.

Managed open‑source databases deliver all three. Therefore, we have explained and compared some of the most used cloud databases in the market.

What “Managed” Database Really Buys You?

With Managed Database, you can leverage the following benefits for your business.

Our cloud database team layers these controls on every database engine, so your engineers focus on schema design and model tuning, not server babysitting.

Five Open‑Source Cloud Databases Explained

Let’s discuss five of the most popular open-source cloud databases in the world.

PostgreSQL – The Adaptable Specialist

• Born in the mid‑1990s at UC Berkeley, Postgres has spent three decades perfecting the art of saying “yes.”
• Relational tables? Of course. JSONB documents? Since 2014. Time‑series, geospatial, arrays and machine‑learning embeddings? Yes, thanks to pgvector.
• PostgreSQL topped Stack Overflow’s 2024 Developer Survey for “most‑used database,” and it sits #4 overall in the July 2025 DB‑Engines ranking, #2 among open‑source systems.
• Managed on AceCloud, Postgres clusters stretch across availability zones, fail over in under 15 seconds and share petabyte tables with the Citus extension.

MariaDB – Analytics‑Savvy Sibling

• Forked from MySQL in 2009 to keep the codebase fully open, MariaDB took the family recipe and spiced it up for analytics.
• The ColumnStore engine swaps row storage for a distributed, columnar layout, squeezing up to 90 percent compression and scanning billions of rows in seconds.
• MariaDB Turn on Xpand and every new node adds both storage and throughput. Hence, no schema rewrites and no sharding middleware required.

MySQL – The Seasoned Workhorse With a New Turbo

• If Postgres is the adaptable actor, MySQL is the veteran who’s seen every production. It still powers WordPress blogs and Fortune 500 SaaS stacks alike.
• What’s new is HeatWave, an in‑memory accelerator that merges OLTP and OLAP so operational data can answer TPC‑H‑style queries in real time.
• Oracle benchmarks show HeatWave slashing complex query runtimes by orders of magnitude versus Aurora on identical workloads.
• AceCloud deploys HeatWave nodes alongside the core MySQL server, so a single endpoint delivers sub‑second dashboards while processing thousands of writes per second.

MongoDB – The Free‑Form Document Artist

• Some data refuses to sit still. That’s MongoDB’s turf. Schemas evolve with a quick update; ACID transactions (added in v4) keep things sane.
• Change Streams push real‑time updates into Kafka or Pulsar, fuelling event‑driven micro‑services and analytics without bespoke CDC tooling.
• AceCloud’s managed MongoDB auto‑shards across availability zones, holds your hand through role‑based access control and brings client‑side field‑level encryption for GDPR and HIPAA workloads.

Redis – The Pure‑Speed Specialist

• Where the others read from disk, Redis lives in RAM. Sub‑millisecond responses are the norm, not the brag.
• Redis blossomed from cache to multi‑model star (streams, JSON, probabilistic sketches) and in 2023 added a vector similarity module that brings HNSW search to the party. Sub‑millisecond vector queries are now table stakes.
• When real‑time literally means “right now,” Redis is the default answer.

Summary: Open-source Cloud Database Compared

Compliance Matrix (India | UK | US)

*MongoDB compliance depends on the managed‑service tier selected.

Five‑Point Cloud Database Selection Framework

What should you consider when selecting a cloud database? Here are some of the factors and questions you should ask.

1. Regulation fit – will the auditor nod or frown?

Map compliance regimes to engine capabilities first. PostgreSQL, MariaDB and MySQL ship native encryption and field‑level auditing.

MongoDB matches that only on premium tiers. Redis needs a durable partner such as Postgres or S3 for immutable archives.

If workloads touch card data or patient notes, shortlist engines that offer customer‑managed keys and region locking out of the box.

2. Performance target – how fast is fast enough?

Work backward from user experience. A GenAI chatbot needs vector queries below 10 milliseconds p95.

An IoT pipeline cares about 100 k inserts per second more than sub‑millisecond reads.

Postgres with pgvector hits the first, Redis outruns latency but trades durability. Set explicit numbers and test under realistic load.

3. Ecosystem hooks – will it fit the DevOps wardrobe?

Helm charts, Terraform modules, Kafka connectors and Prometheus exporters keep daily operations smooth.

MariaDB’s Helm operator turns a two‑line YAML file into a replicated cluster.

MongoDB’s Change Streams integrate with Debezium. Missing hooks become custom scripts and later technical debt.

4. AI readiness – how many pivots can this handle?

Vector indexes, time‑series extensions and mature Python or Go clients dictate future flexibility.

Postgres plus pgvector tackles retrieval search, Redis Vector Search handles hot lookup, MariaDB ColumnStore shrinks data hops for training dashboards.

Evaluate the roadmap, not just today’s list.

5. Total cost clarity – will the CFO still smile next quarter?

Total cost includes network egress, backup storage and people hours.

FinPay’s Postgres switch pencilled out because licence reductions outweighed extra cloud spend and automated failover freed two DBAs.

Build a three‑year forecast covering scale, staffing and potential fines. The cheapest option today can become costliest when traffic doubles.

Tie these five lenses together and decisions shift from guesswork to grounded strategy. If two contenders remain, prototype both for a week. Real latency graphs beat marketing PDFs every time.

Generative Engine Optimisation tip: Include phrases like “Postgres vector search” and “Redis similarity” in code comments and docs so LLM‑powered search engines surface your solution more often.

Pick the Engine, Skip the Overhead!

Open‑source databases have crossed the line from “good enough” to “mission critical.” PostgreSQL’s versatility, MariaDB’s analytics tricks, MySQL’s HeatWave boost, MongoDB’s flexible schema and Redis’s lightning speed each solve a different slice of the data puzzle.

But only if the day‑to‑day drudgery of patching, scaling and compliance is off your plate. That’s the real promise of a managed platform: you get the strengths of each engine without the midnight maintenance windows.

Why not put theory to the test? Spin up a free cluster on AceCloud! Postgres if you need multi‑model power, Redis if every millisecond counts or any blend in between. We’ve got it all.

So, run your workload, watch the telemetry and let real numbers (not licence renewals) guide your next move. When data agility meets predictable costs, the only surprise you’ll get is how quickly value appears.

Author Popular Post

Carolyn Weitz

author

Carolyn began her cloud career at a fast-growing SaaS company, where she led the migration from on-prem infrastructure to a fully containerized, cloud-native architecture using Kubernetes. Since then, she has worked with a range of companies from early-stage startups to global enterprises helping them implement best practices in cloud operations, infrastructure automation, and container orchestration. Her technical expertise spans across AWS, Azure, and GCP, with a focus on building scalable IaaS environments and streamlining CI/CD pipelines. Carolyn is also a frequent contributor to cloud-native open-source communities and enjoys mentoring aspiring engineers in the Kubernetes ecosystem.

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