Open-Source Databases

Infographic showing a split design: on the left, a database icon with a gear representing open-source database hosting; on the right, a cloud with a database icon representing cloud providers, along with AWS, Google Cloud, and Azure logos

Open-Source Database Hosting vs Cloud Providers (AWS RDS, GCP, Azure)

This blog compares open-source database hosting with cloud provider databases like AWS RDS, GCP Cloud SQL, and Azure. It explains key differences in cost, control, flexibility, scalability, and vendor lock-in. Open-source hosting offers greater customization and cost savings, while cloud providers deliver convenience, compliance, and managed services. The article also highlights pros, cons, and use cases for each approach, concluding that many businesses benefit from a hybrid model combining both.

Flat-style infographic showing icons for relational, NoSQL, and vector databases with the title “How to Choose Between Relational, NoSQL, and Vector Databases.”

How to Choose Between Relational, NoSQL, and Vector Databases

This guide explains the key differences between relational, NoSQL, and vector databases, highlighting their strengths, best use cases, and examples. Relational databases are best for structured data and transactions, NoSQL excels at scalability and flexibility, while vector databases power AI and semantic search. Learn how to choose the right database—or combine them in a polyglot approach—for your project.

Illustration of databases, a brain symbol for AI, and analytics icons on a dark blue background with the title “Best Open-Source Databases for AI & ML Workloads.”

Best Open-Source Databases for AI & ML Workloads

The best open-source databases for AI and ML workloads include vector databases (Milvus, Weaviate, Qdrant), time-series databases (TimescaleDB), graph databases (Neo4j), and high-performance analytics engines (ClickHouse), alongside PostgreSQL with pgvector as a reliable all-rounder. Each option serves different use cases like semantic search, predictive analytics, fraud detection, and large-scale model training. The right choice depends on your workload—whether it’s embeddings, temporal data, relationships, or high-speed analytics.

Top 10 Open-Source Databases for Startups in 2025

The best open-source databases for startups in 2025 combine low cost, scalability, and flexibility, making them ideal for fast-growing businesses. Relational options like PostgreSQL, MySQL, and MariaDB provide reliable foundations, while FerretDB offers a MongoDB-compatible alternative. For speed and real-time performance, startups can turn to Redis, Valkey, and ScyllaDB, while ClickHouse and TimescaleDB shine in analytics and time-series workloads. AI-driven startups benefit from Neo4j for graph databases and Milvus for vector search, ensuring they can handle modern data challenges. Choosing the right database depends on your use case — SaaS, fintech, e-commerce, IoT, or AI — but open-source ensures freedom, strong community support, and enterprise-grade scalability without vendor lock-in.

Cover image showing a comparison between InfluxDB and TimescaleDB with their logos and the text “Which is better for time-series data?” on a dark background.

InfluxDB vs TimescaleDB: Which is Better for Time-Series Data?

This blog explores the differences between InfluxDB and TimescaleDB, two leading open-source time-series databases. InfluxDB is purpose-built for high-ingestion, real-time workloads like IoT and monitoring, offering speed and simplicity through InfluxQL and Flux. TimescaleDB, built as a PostgreSQL extension, combines time-series performance with full SQL support, making it ideal for hybrid workloads that mix relational and time-series data. The takeaway: choose InfluxDB if you need raw ingestion speed, or TimescaleDB if you want SQL compatibility, long-term scalability, and integration with relational ecosystems.