Unified search and analytics
Run full-text, fuzzy, geospatial, vector, and hybrid search against analytical data through one database engine.
SereneDB
SereneDB is a search-OLAP database that combines full-text, vector, hybrid, geospatial search, and analytical SQL over shared data. It is aimed at developers and data teams that want PostgreSQL-compatible access and a single-binary workflow for querying local or remote data.
Search and analytics in one database
SereneDB is a search-OLAP database for running search and analytical workloads over the same data. It combines full-text, fuzzy, geospatial, vector, and hybrid search with columnar storage and SQL execution, giving teams one engine for workflows that might otherwise use separate search, vector, and analytics systems.
The product uses the PostgreSQL wire protocol and ships as a single binary, so it can be started with the Linux installer or Docker and accessed with psql or other PostgreSQL-compatible clients. Its documented workflows also read remote Parquet, CSV, and JSON data, generate embeddings from SQL, and apply search predicates, semantic ranking, and aggregations in one query.
Core capabilities
Vectorized SQL execution
Use SQL for joins, unions, aggregations, and large analytical queries with vectorized execution powered by Velox.
Hybrid storage
Combine search indexes, columnar storage, remote tables, and RocksDB-backed durable updates in a hybrid storage design.
Full-text and vector indexing
Create inverted indexes that combine text analysis with IVF vector indexing, then use operators and ranking methods such as @@, BM25, and vector distance.
SQL-based embedding generation
Generate embeddings from SQL with ai_embed and configure an OpenAI-compatible provider, including a local Ollama endpoint, without a separate vector service.
Remote and external data access
Read Parquet, CSV, and JSON directly from object storage, HTTP, or the Hugging Face Hub, and index external files on S3 or disk for zero-ETL search workflows.
Practical use cases
Search-driven applications
Build applications that need keyword matching, semantic ranking, and structured filtering or aggregation without synchronizing a search cluster, vector store, and warehouse.
Semantic and hybrid retrieval
Load a catalog, document collection, or other remote dataset, generate embeddings in SQL, and support lexical and semantic retrieval against the same table.
Search over data-lake files
Query Parquet, CSV, or JSON in object storage or on external files, then apply search indexes and analytical queries without a conventional batch import step.
Search-filtered analytics
Run dashboards or investigative SQL queries on records selected by a text or vector search predicate, such as grouping matching documents by category or calculating averages.
Local development and evaluation
Prototype a search-analytics service locally or in a container using the single-binary distribution and familiar PostgreSQL clients.
Pros and Cons
Pros
- Combines full-text, vector, hybrid, geospatial search, and SQL analytics in one engine.
- PostgreSQL protocol compatibility allows connections from psql and existing PostgreSQL-compatible clients and drivers.
- Single-binary distribution simplifies the documented local setup, with Linux installation and Docker options.
- Can query remote files and object-storage data, including Parquet, CSV, and JSON, without a separate import job in the documented workflows.
- Supports SQL-side embedding generation and an OpenAI-compatible endpoint, including local Ollama.
Cons
- The supplied pricing page does not expose plan names, prices, licensing terms, or evaluation limits.
- Public setup information is strongest for Linux and Docker; broader deployment targets and production operations are not detailed in the supplied material.
- Authentication is not described in the reviewed documentation beyond the quick-start note that no credentials are required by default in its local example.
FAQ
How do you install SereneDB?
SereneDB is distributed as a single binary. The quick-start documentation provides a Linux installer command, a Docker image example, and links to direct downloads and GitHub releases. A sample Docker run publishes port 7890.
Can existing PostgreSQL tools connect to SereneDB?
Yes. SereneDB speaks the PostgreSQL wire protocol and supports SQL, so users can connect with psql or other PostgreSQL-compatible clients. The quick-start example connects to localhost on port 7890, with no credentials required by default in that example.
What data sources can SereneDB read?
SereneDB can read Parquet, CSV, and JSON from object storage, HTTP, and the Hugging Face Hub. The documentation also describes indexing external files in place on S3 or disk, supporting search without a separate import workflow for those data sources.
How does SereneDB combine keyword, vector, and analytical queries?
The documented workflow keeps full-text search, vector search, semantic ranking, and SQL analytics on the same data. Embeddings can be generated with ai_embed from SQL using an OpenAI-compatible endpoint, including a local Ollama server, after which an inverted index can support lexical and vector queries.
Quick Facts
- Category
- Search-OLAP database
- Interface
- SQL and PostgreSQL wire protocol
- Distribution
- Single binary; Linux installer and Docker image documented
- Search types
- Full-text, fuzzy, geospatial, vector, and hybrid
- External data
- Parquet, CSV, and JSON from object storage, HTTP, or Hugging Face Hub
- Primary workflow
- Search, semantic ranking, and aggregation over shared data
SereneDB Alternativen
Mindcase
Mindcase is a web data API and console for extracting structured data from websites and major online platforms. It helps users retrieve clean rows of data through prompt or API access, with prepaid per-record billing.
ByteAsk
ByteAsk is a terminal-first AI coding agent for C and C++ that edits repositories and verifies changes with the real compiler, debugger, sanitizers, and tests before showing a diff. It offers a free tier plus paid plans, with editor connectors and zero-retention handling described in the source.
CreateOS Sandbox
CreateOS Sandbox is an isolated compute environment for running code and agent workloads inside Firecracker micro-VMs. It is designed for workflows that need machine-level isolation, private networking between sandboxes, and programmatic control through SDK, CLI, or MCP.
GitBot
GitBot is a local browser-based workspace for creating reusable coding bots powered by Claude Code, Codex, or OpenCode. It helps developers run those agents across repository folders with persistent threads and configurable permissions.
hob
hob is an independent workspace for coding agents that keeps agent sessions, terminals, history, and follow-up work organized around the tools and providers you already use. It is aimed at developers who want local control over routing, history, and workspace structure rather than a bundled model stack.
Ably Chat
Ably Chat is a chat API platform for building custom realtime chat applications. It supports room-based messaging, typing indicators, presence, reactions, and message updates, with usage-based pricing options for different deployment stages.