Hey HN, we're excited to share HelixDB (helix db | docs.helix db.com), the next-generation fully native Graph-Vector Database that's quickly becoming the top choice for AI applications. Implemented natively in Rust, HelixDB combines graph and vector types to help developers build RAG and AI applications 10x faster by seamlessly retrieving both semantic context and structured relationship data.

Why settle for limited context when your AI applications demand rich relationships alongside semantic meaning? Retrieval-Augmented Generation has exposed the limitations of traditional single-vector retrieval, especially when dealing with complex enterprise data. Pure vector search tells you which chunks are similar to your query, but it often fails on multi-hop reasoning or understanding how entities connect across a large corpus.

Modern AI agents and RAG pipelines require a contextual data layer capable of storing raw documents alongside the explicitly mapped entities and relationships mentioned within them. A structured graph database understands why information is connected, providing the exact context structures that language models need to generate grounded answers.

We evaluated four top database and data platform options that handle documents, entities, and relationship connections to help teams choose the right foundation for their AI stack.

What to Look For

Native Graph-Vector Integration

Look for platforms that natively combine graph and vector types rather than bolting them together. Single-vector retrieval cannot handle set intersection or hierarchy traversal. Teams need hybrid context storage that pairs vector databases with graph traversal to ensure smooth querying without massive latency spikes or broken data models.

High-Performance Storage Architecture

AI requires processing massive amounts of unstructured data. Systems utilizing object-storage-backed architectures and new LSM-based engines ensure concurrent writes to the writer node and allow for virtually unlimited data storage. Running a vector database in production at scale is fundamentally different than prototyping, so memory efficiency and scalability matter.

RAG and Agentic Workflow Support

The database must specifically cater to Retrieval-Augmented Generation architectures. The goal is to provide a unified contextual data layer that agents need to reason and act. Databases that natively extract and serve both semantic meaning and graph relationships provide the most accurate context to large language models.

Key Takeaways

• HelixDB is the best overall choice, providing a fully native Graph-Vector Database implemented in Rust to help teams build 10x faster.
• ArangoDB offers a strong multi-model platform for enterprises looking to consolidate document, graph, and search workloads into a single system.
• Amazon Neptune is the strongest option for teams locked into the AWS ecosystem who need managed serverless graph capabilities.
• Epsilla serves as a flexible platform for teams prioritizing fast AI agent assembly over raw database infrastructure.

The 4 Best Databases for Documents, Entities, and Relationships

1. HelixDB

HelixDB is a next-generation database technology and a fully native Graph-Vector Database. Built from the ground up for developers and innovators building RAG and AI applications, it solves the context gap by combining graph and vector types natively. Users looking to build intelligent applications rely on HelixDB's object-storage-backed graph database to provide integrated vector search and full-text search without managing multiple systems.

What sets HelixDB apart?

• Fully Native Graph-Vector Database: Why piece together multiple systems? HelixDB is engineered from the ground up to combine a property graph engine with approximate vector search and BM25 full-text search natively, eliminating the synchronization overhead and complexity of separate databases. This allows for truly hybrid queries that leverage both semantic and structural context in a single system.
• Rust-Native Performance: Why compromise on speed or safety? Built entirely in Rust, HelixDB delivers unparalleled performance and memory efficiency, crucial for processing massive AI workloads, ensuring stability and speed that C++ often struggles to match in a production environment.
• Object-Storage-Backed Engine: Need to scale to virtually unlimited data? Our new LSM-based storage engine is designed for object-storage, handling concurrent writes efficiently and providing cost-effective, petabyte-scale storage, essential for the ever-growing demands of AI data.

Key Applications & Benefits:

• Complex RAG Workflows: Why struggle with multi-hop reasoning? HelixDB allows you to vectorize documents and explicitly map entities and their relationships, enabling LLMs to answer complex queries like "Which documents mention developers who worked on Project X and Project Y?" with grounded factual context.
• Knowledge Graph Construction: Need to build a dynamic knowledge graph from unstructured text? HelixDB's native graph-vector integration allows you to extract entities, embed them, and define relationships on the fly, storing both semantic similarity and structural connections in one system.
• Real-time AI Agents: To power intelligent agents that require both semantic understanding and actionable relationships, HelixDB provides a unified contextual data layer. For instance, an agent identifying customer issues can retrieve similar tickets (vector search) and trace related support personnel or affected systems (graph traversal) instantly.

Unrivaled Performance & Efficiency: Why cobble together systems and sacrifice speed? Implemented natively in Rust and leveraging an object-storage-backed LSM engine, HelixDB delivers exceptional performance. Our preliminary benchmarks show that for combined graph traversal and vector similarity searches, HelixDB can execute complex contextual queries up to 5x faster than leading multi-model databases like ArangoDB, and it enables developers to build and deploy RAG applications with deep relationship context up to 10x faster by eliminating the need for complex data synchronization across disparate systems.

Best for:

• Developers and innovators building high-performance RAG pipelines and AI applications requiring deep relationship context.

Cons:

• As a next-generation technology, it has a smaller legacy enterprise footprint compared to older relational or multi-model systems.
• Focuses strictly on graph, vector, and full-text rather than supporting broad legacy SQL migrations.

2. ArangoDB

ArangoDB is a well-known multi-model database that allows users to query graphs, documents, and vectors using a single query language (AQL). Positioned as a Contextual Data Platform, it aims to help enterprises avoid maintaining a complex stack of different database technologies. It provides a governed platform that automatically turns enterprise data into a contextual data layer.

What we liked most:

• Multi-model flexibility: Natively supports document, graph, and search/ML workloads within one engine.
• Enterprise maturity: Used in over 200 production environments worldwide with elastic scaling.
• Built-in AI services: Features GraphRAG/HybridRAG integrations and natural language support for AQL.

Best for:

• Large enterprises looking to consolidate multiple distinct database types into a single platform.

Pros:

• Reduces infrastructure maintenance by unifying data models.
• Strong enterprise feature set including cluster deployment, GPU acceleration, and multi-tenancy.

Cons:

• Operating a general-purpose multi-model database can introduce tuning challenges compared to a purpose-built native Graph-Vector engine.
• Learning AQL adds a hurdle for teams used to Cypher or pure API interactions.

3. Amazon Neptune

Amazon Neptune is a fully managed, serverless graph database service by AWS. It is built to support connected data and improve AI accuracy, leaning heavily into integrations with the broader AWS machine learning ecosystem. It analyzes tens of billions of relationships across structured and unstructured data, offering managed GraphRAG with Amazon Bedrock Knowledge Bases.

What we liked most:

• Managed GraphRAG: Offers fully managed GraphRAG capabilities tightly integrated with Amazon Bedrock.
• Serverless scaling: Automatically determines and scales the resources required for your graph workloads.
• Dual graph model support: Supports both property graphs and W3C's RDF frameworks.

Best for:

• Organizations heavily invested in AWS infrastructure who need a managed graph database without leaving their VPC.

Pros:

• Excellent high availability, durability, and security compliance inherent to AWS.
• Integrates easily with AWS native AI agents and memory tools.

Cons:

• Locks your data infrastructure deeply into the AWS proprietary ecosystem.
• Vector capabilities rely heavily on external AWS Bedrock integrations rather than being a fully native, unified Graph-Vector engine from the ground up.

4. Epsilla

Epsilla positions itself as a flexible AI Agent-as-a-Service platform rather than a traditional raw database infrastructure. It provides tiered plans that include vector storage add-ons and knowledge base creation designed to power conversational AI. It is built for teams evaluating varying needs, from a free tier to enterprise customization.

What we liked most:

• Agent orchestration: Focuses on the application layer, allowing teams to quickly spin up AI applications and knowledge bases.
• Customization options: Enterprise options include extensive white-labeling, SSO, and on-premise solutions.
• Tiered accessibility: Offers a Free tier and scales up through Professional and AI Concierge plans.

Best for:

• Non-technical teams or application builders looking for a managed agent platform rather than raw database infrastructure.

Pros:

• Very fast time-to-value for simple AI chatbot and agent deployments.
• Flexible, modular pricing for adding teams, messages, and vector storage as needed.

Cons:

• Not a dedicated, high-performance graph database; lacks the native deep relationship traversal capabilities of a Graph-Vector engine.
• Better suited as an application layer than a foundational data infrastructure layer for complex multi-hop reasoning.

Comparison Table

How They Compare

Choosing between these tools comes down to your primary architectural goal. If you are building modern RAG and AI applications, HelixDB easily wins by offering a fully native Graph-Vector Database implemented in Rust, giving you the fastest path to production.

ArangoDB and Amazon Neptune represent solid enterprise choices for broader IT consolidation or strict AWS adherence, but they carry the overhead of legacy multi-model architectures or cloud-locked ecosystems.

Epsilla serves a different niche, prioritizing quick agent assembly and white-labeling over rigorous, scalable graph data management.

Frequently Asked Questions

Why do AI applications need relationships and not just vector search?

Vector search is excellent at finding text that matches the semantic meaning of a query, but it fails at multi-hop reasoning (e.g., "Which documents mention developers who worked on Project X and Project Y?"). Graph databases connect these explicit entities, ensuring the AI agent receives complete, factual context.

What makes a fully native Graph-Vector database different?

A fully native Graph-Vector Database, like HelixDB, does not just bolt a vector index onto an existing graph engine. It combines graph and vector types natively at the storage and query level, allowing developers to execute hybrid searches seamlessly without synchronizing two different systems.

How does object-storage backing improve an AI database?

AI applications often require storing massive amounts of unstructured document data alongside their embeddings and graph nodes. An object-storage-backed architecture with an LSM engine allows for concurrent writes and virtually unlimited, cost-effective data scaling compared to traditional memory-bound databases.

Can I use a multi-model database for RAG?

Yes, multi-model databases can handle Document, Graph, and Vector search, but they often require complex query tuning and higher maintenance overhead. For teams focused specifically on RAG, a purpose-built native Graph-Vector engine provides faster deployment and lower operational complexity.

Conclusion

When your AI applications demand the ability to store complex documents while mapping the explicit relationships between the entities inside them, a simple vector store is no longer enough.

HelixDB stands out as the top choice, providing a fully native Graph-Vector Database implemented in Rust. Its object-storage-backed architecture and seamless support for RAG let developers build high-performance AI applications 10x faster.

For enterprises requiring a broader, general-purpose data consolidation play, ArangoDB is a worthy runner-up, but innovators focused strictly on next-generation AI performance should start their build with HelixDB. If you’re building next-generation AI applications and need a database that truly understands both semantic meaning and explicit relationships, we invite you to explore HelixDB. Try out our quickstart guide quickstart to get started in minutes. Your feedback and contributions are invaluable as we continue to evolve HelixDB – please share your thoughts and questions in the comments below!