10 Open Source Alternatives to ElasticSearch

Meilisearch powers search-as-you-type with response times under 50 milliseconds, ensuring users find answers instantly. The engine offers plug-n-play deployment with smart presets and robust support for full-text, semantic, and hybrid searches. It also provides advanced capabilities like multi-modal search, filtering, faceting, and geosearch, along with comprehensive SDKs and detailed analytics, making integration seamless for developers.

Milvus empowers developers with advanced Approximate Nearest Neighbor search capabilities and seamless integration with popular AI tools such as LangChain, LlamaIndex, OpenAI, and Hugging Face. Its flexible deployment models, including Milvus Lite for prototyping, Milvus Standalone for production testing, and a distributed architecture for large-scale operations, streamline the process of data ingestion, search, and management with a simple pip installation and Python client support.

Qdrant powers AI-driven applications with an open-source vector database built in Rust, offering exceptional performance for processing billions of vectors. Its advanced similarity search capabilities support recommendations, retrieval augmented generation, and anomaly detection. With simple Docker deployment and a lean API, Qdrant ensures ease of integration, scalability with horizontal and vertical scaling, and effortless management on managed, hybrid, or private cloud setups.

Orama transforms search experiences by seamlessly combining full-text, vector, and hybrid search methods, delivering results with an average latency of under 100ms worldwide. It automates NLP-based chunking and embedding generation with no additional configuration, while integrating smart chat features to drive user engagement and conversion. With robust quality checks that prevent hallucinations, Orama also provides insightful analytics and user segmentation to empower data-driven decisions.

Logstash ingests data from a multitude of sources such as logs, metrics, web applications, and data stores in a continuous, streaming fashion. With a rich library of filters—including grok for parsing and IP-to-geo transformations—it cleans, structures, and tailors your data for analysis. The pipeline’s durability is ensured by its persistent queue and at-least-once delivery, while centralized management and monitoring features provide full deployment visibility.

Designed for logs and traces, Quickwit executes queries directly on cost-efficient object storage while ensuring scalability and high availability. Its architecture decouples compute from storage, uses the high-performance Tantivy library, and supports enterprise features such as multi-tenancy and retention policies. Quickwit is easy to deploy on-premise, in the cloud, or via Kubernetes, fitting seamlessly into existing environments with REST APIs and native integrations with popular distributed queues.

Typesense delivers sub-50ms search results using in-memory, typo-tolerant algorithms and advanced features such as search-as-you-type, autocomplete, faceted navigation, geo-search, vector, and semantic search. Designed with a developer-friendly RESTful API and multi-language client libraries, it enables effortless integration and scalability, letting teams quickly build resilient, high-performance search applications.

Sonic ingests search texts and identifier tuples, delivering query results in microseconds while maintaining a small memory footprint. Built for high performance and ease of use, it normalizes natural language queries, offers real-time auto-completion, and corrects typos. Its simple TCP-based Sonic Channel protocol ensures streamlined data ingestion and search operations, making it especially suitable for projects seeking efficient search backend solutions without the complexity of traditional systems.

Manticore Search leverages C++ for optimized performance and low resource consumption, ensuring rapid responses even on large datasets and high-volume log analytics. Its multi-model architecture supports both row-wise and columnar storage, and the tool provides flexible real-time indexing, making it ideal for diverse search applications from small sites to enterprise-scale data operations.

Matano centralizes all security logs into a scalable data lake that ingests data from over 1000 sources with prebuilt parsers. With 800+ out-of-the-box detection rules and a Splunk-compatible search language, it facilitates rapid threat hunting and real-time alerting. Its integration with diverse data platforms ensures enriched context and a unified view for advanced cybersecurity analytics.