HydraDB vs. Neo4j, Dgraph, Neptune, FalkorDB, Memgraph, and ApertureDB

Graph databases are having a second moment because AI agents need more than isolated records. They need connected context.

An agent may need to know that a customer opened a ticket, that the ticket is connected to a product, that the product was affected by an incident, that an engineer fixed it, and that the fix changes what the agent should do next.

That context keeps growing. It changes over time, gets shared across agents, and can eventually contain years of conversations, documents, events, decisions, relationships, and agent actions.

HydraDB is built specifically for this kind of long-lived context infrastructure.

The architectural bet

HydraDB is an object-store-native graph database. The durable graph lives in S3-compatible object storage, while query nodes and indexers run as separate compute. Memory and local NVMe act as disposable acceleration layers rather than the permanent home of the graph.

It supports an OpenCypher subset, Bolt 5.x, HTTPS, and GraphBLAS-based traversal (engine).

S3 Standard starts at approximately $0.023/GB-month for the first 50 TB in us-east-1 (AWS). HydraDB’s published benchmarks report 912 µs hot 1-hop traversal and 11.1 ms hot 20-hop traversal (benchmark).

Memory, company knowledge, ontologies, and context graphs are built on top of this same engine rather than as separate systems (docs).

The core idea is simple: let durable context grow independently from the compute needed to query it today.

HydraDB vs. Neo4j

Replace Neo4j to HydraDB when your graph becomes long-lived context infrastructure for AI agents for cost efficiency and optimization.

Neo4j is one of the most mature general-purpose graph databases. It offers Cypher, graph algorithms, vector indexes, clustering, visualization, and first-party GraphRAG tooling.

But its infrastructure model ties a growing graph more closely to provisioned database resources. Neo4j Aura Professional starts at $65/GB-month of RAM, while Business Critical is $146/GB-month (pricing). Included disk is 2× RAM, and additional disk is metered at compute rates (billing). Self-managed high availability starts with 3 primary servers (manual).

HydraDB is designed around persistent AI context as a first-class workload. Temporal graph state, connected organizational knowledge, memories, ontologies, and relationship-aware retrieval can all live on the same underlying graph.

More importantly, the durable graph stays in S3-compatible object storage while query and indexing compute scale separately. Years of documents, relationships, events, and agent history can therefore accumulate without requiring the provisioned database cluster to grow at the same rate.

Use HydraDB instead of Neo4j when you want temporal agent context and a graph architecture where durable storage scales independently from compute.

HydraDB vs. Dgraph

Use HydraDB when the graph is persistent context for agents rather than primarily a distributed application database.

Dgraph is a distributed graph database built around GraphQL and DQL. It supports predicate-based sharding and HNSW vector search through @embedding fields (vectors).

Its durable state is stored in Badger on Alpha nodes, and a typical highly available deployment includes 3 Zero nodes and 3 Alpha nodes (architecture). As the durable graph grows, the infrastructure storing and serving that graph must grow with it.

HydraDB separates those concerns. Durable graph history stays in object storage, while query and indexing compute can scale with current workload instead of total historical context.

HydraDB also builds retrieval around agent context: entities and relationships can be extracted during ingestion, while recall combines semantic, keyword, graph, and recency signals.

HydraDB vs. ApertureDB

Use HydraDB for agent context and organizational knowledge. Use ApertureDB when the primary workload is multimodal data.

ApertureDB is a graph-vector database designed around images, video, blobs, embeddings, bounding boxes, frames, and other multimodal assets. Vector search uses FAISS, while metadata is represented through an in-memory graph (docs).

That makes ApertureDB a natural fit for computer-vision and multimodal datasets.

HydraDB solves a different problem. Its graph represents the connected state around what an organization and its agents know and do: customers, tickets, projects, incidents, documents, decisions, people, actions, memories, and ontologies.

HydraDB vs. Amazon Neptune

Use HydraDB when you want persistent context infrastructure for AI agents instead of assembling it around a managed AWS graph database.

Amazon Neptune is a managed general-purpose graph database inside AWS.

In us-east-1, a db.r5.large instance is listed at $0.348/hour, or roughly $251/month. Storage costs approximately $0.10 or $0.225/GB-month, depending on the storage configuration, while Standard I/O is $0.20 per million requests. A small highly available deployment can therefore reach roughly $500–$700+ per month before larger storage and workload requirements (pricing).

At the upper storage price, $0.225/GB-month is almost 10× S3 Standard’s $0.023/GB-month, before accounting for database instances and I/O.

Neptune can also participate in GraphRAG architectures through Amazon Bedrock, but connectors, temporal context, and broader hybrid recall still have to be assembled around the database.

HydraDB brings those pieces into the same context layer: workplace ingestion, versioned relationships, graph traversal, semantic search, lexical search, metadata, and recency-aware retrieval.

Use HydraDB instead of Neptune when you want ingestion, temporal context, retrieval, and the durable graph to operate as one agent-context system.

HydraDB vs. FalkorDB

Use HydraDB when the graph needs to become long-lived context infrastructure rather than remain primarily a high-performance in-memory graph.

FalkorDB is built around GraphBLAS and sparse adjacency matrices. It supports OpenCypher, vector search, full-text search, and GraphRAG workloads.

Its Startup tier is priced at $0.100/GB-hour, or roughly $73/GB-month of RAM. FalkorDB documents approximately 75% usable graph capacity, meaning a 100 GB graph requires around 134 GB of provisioned memory—roughly $9,800/month before HA (Startup).

Its Pro tier uses a different model of $0.20/core-hour + $0.01/GB-hour (Pro).

HydraDB shares the GraphBLAS traversal family but makes a different infrastructure tradeoff: the full durable history lives in object storage rather than requiring the graph to remain provisioned in RAM.

That matters when an agent’s context contains years of Slack messages, GitHub activity, tickets, documents, decisions, relationships, and previous agent actions while only a small portion of that history is actively queried at any moment.

HydraDB also adds the surrounding context primitives: connectors, temporal state, entity relationships, and retrieval combining graph, semantic, keyword, metadata, and recency signals.

Use HydraDB instead of FalkorDB when context will keep accumulating and you want durable storage to grow independently from the memory and compute serving today’s queries.

HydraDB vs. Memgraph

Use HydraDB when you are building persistent agent context that will grow far beyond the graph needed for today’s queries.

Memgraph is a Cypher-compatible graph database built for real-time graph and AI workloads. It supports native vector search and can operate with in-memory or on-disk storage.

Memgraph Enterprise starts at $25,000/year for 16 GB, which works out to approximately $130/GB-month at that SKU (newsletter, pricing).

The infrastructure is therefore sized around provisioned graph capacity.

HydraDB takes a different approach. It keeps connected organizational context and versioned state in S3-compatible object storage, while query compute is sized around current access patterns rather than total accumulated history.

Its retrieval layer also combines graph relationships with semantic and temporal context rather than treating graph traversal and memory as separate application concerns.

References

• HydraDB engine: https://github.com/hydra-db/hydradb

• HydraDB benchmark repo: https://github.com/hydra-db/benchmark

• HydraDB docs: https://docs.hydradb.com/

• AWS S3 pricing: https://aws.amazon.com/s3/pricing/

• Neo4j pricing: https://neo4j.com/pricing/

• Neo4j Aura billing dimensions: https://neo4j.com/docs/aura/billing/billing-dimensions/

• Neo4j clustering (Ops Manual): https://neo4j.com/docs/operations-manual/5/clustering/introduction/

• Dgraph vector similarity search: https://docs.dgraph.io/graphql/queries/vector-similarity

• Dgraph architecture: https://docs.dgraph.io/installation/dgraph-architecture/

• ApertureDB overview: https://docs.aperturedata.io/Introduction/WhatIsAperture

• Amazon Neptune pricing: https://aws.amazon.com/neptune/pricing/

• FalkorDB cloud Startup tier: https://docs.falkordb.com/cloud/startup-tier.html

• FalkorDB cloud Pro tier: https://docs.falkordb.com/cloud/tiers/pro

• Memgraph newsletter: https://memgraph.com/newsletter

• Memgraph pricing: https://memgraph.com/pricing

• HydraDB benchmarks blog (LongMemEval-S): https://benchmarks.hydradb.com/hydradb