Databases & backend
SynapCores
Self-hosted AI database combining SQL, vectors, graphs, and durable agents in one engine
Overview
A look at synapcores.com
SynapCores is a single-binary, self-hosted AI database that brings relational data, vector search, graph databases, machine learning, LLM inference, and durable agents into one integrated engine. Unlike traditional AI stacks that chain together separate services, SynapCores consolidates these capabilities under one query language (SQL and Cypher) and one execution environment.
The core architectural principle is that intelligence and data should share the same runtime. Agents are defined as database schema objects using CREATE AGENT syntax, triggered by data changes, and their complete execution history is stored and auditable within the database itself. This eliminates orchestration layers, message queues, and synchronization jobs.
The platform includes SQL queries, vector similarity search with HNSW indexing, Cypher graph traversal, multi-provider AI model integration, bundled local LLM inference, AutoML capabilities, vision and multimodal processing, and 187 ready-to-run recipes. Community Edition is free and runs on a single host; Enterprise Edition adds clustering, fine-grained access controls, and CDC capabilities.
Feature notes
- Durable agents defined with CREATE AGENT that fire on schedule and data changes
- Bundled local LLM inference with llama-3.2-1b via llama-cpp
- Filesystem Collections with automatic ingestion of PDFs, images, CSV, audio, and video
- 187 ready-to-run recipes shipped with the binary
- Single binary installation with no JVM, Python runtime, or external orchestrator required
Pricing
Pricing model: Freemium
Community Edition is free. Enterprise Edition pricing available by contacting the team.
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FAQ
- Is the Community Edition really free?
- Yes. It is available without signup or a credit card, under the Community Edition license. It is proprietary software; free availability does not mean an open-source license. Your hosting costs and any external AI provider charges are separate.
- Does my data leave my infrastructure?
- Local storage and embedded inference can run on your own infrastructure. If you configure an external AI provider, requests to that provider can include your data. Review the model configuration for your workload before using sensitive data.
- Do I need a GPU?
- A GPU is not required for CPU inference. Model choice and workload affect memory needs and response time. Check the current requirements for your system.
- Can I replace MySQL or Postgres without changes?
- Plan a compatibility evaluation. SynapCores supports the MySQL wire protocol, but that alone does not guarantee support for every SQL statement, ORM behavior, or extension. Test your schema, queries, transactions, and tools.
- What is the best way to evaluate it?
- Install locally, run a recipe, and reproduce a small representative workload. Then evaluate correctness, latency, recovery, and the controls you need before choosing a production deployment.
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