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.
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.
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.
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.
Apache Kafka isn’t a traditional relational or NoSQL database, but it can function as a database for streaming data. By storing events durably, enabling replay, and supporting real-time processing through Kafka Streams and ksqlDB, Kafka is ideal for event sourcing, data pipelines, and microservices communication. However, it’s not suited for transactional workloads, long-term archival, or general-purpose CRUD operations. The best approach is to use Kafka alongside open-source databases like PostgreSQL, ClickHouse, Redis, or TimescaleDB to build modern, scalable data infrastructures that balance real-time event streaming with persistent storage.