# HydraDB OSS vs Polygres

HydraDB OSS records 29,635 monthly visits with +5.8% month-over-month growth and Freemium, Contact for Pricing pricing. Polygres records 4,018 monthly visits with -52.3% growth and Freemium pricing.

| Metric | [HydraDB OSS](https://openaitools.app/tool/hydradb-oss) | [Polygres](https://openaitools.app/tool/polygres) |
| :--- | :--- | :--- |
| Website URL | https://hydradb.com | https://polygres.com |
| Categories | Graph AI, AI Knowledge Graph, AI Agent, AI Knowledge Base | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API |
| Pricing Model | Freemium, Contact for Pricing | Freemium |
| Monthly Visits | 29,635 | 4,018 |
| MoM Growth Rate | +5.8% | -52.3% |
| Primary Market | India | Global |
| Direct Visits | 17,146 | 1,605 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## HydraDB OSS

HydraDB OSS is an open-source graph database designed for the memory and context layers of AI agents. Rather than stitching together a vector database, a graph store, and a relational database, it puts entities, relationships, traces, and preferences into a single graph that lives on object storage. The result is a database whose storage cost scales like S3 while queries stay fast enough for interactive agents.

The architecture separates write, index, and read nodes: Cypher writes land in a write-ahead log, an indexer builds GraphBLAS-backed indexes, and readers merge the index with pending WAL entries for strong consistency. Tiered storage moves hot data from in-memory cache to NVMe to cold object storage on demand. Built-in retrieval combines graph traversal with vector, BM25, and reranking so a query like "what breaks if I change this function" can be answered in a single call.

HydraDB OSS ships under an open-source license, so teams can self-host on any S3-compatible store, import graphs from other databases, and use the supplied SDKs and an HTTP API. The same engine is offered as a managed cloud service for teams that would rather not operate the cluster themselves. Helpful extras include assisted ontology creation, entity resolution, and temporal versioning that tracks what changed and when.

**Pricing & Plans:** The open-source edition is free to self-host with community support. Managed cloud starts around $25/month minimum usage, a Scale tier around $300/month, and an Enterprise plan with custom (contact sales) pricing.

- **Object-storage-native engine:** Graph, WAL, value, and index data all live in S3-compatible storage with a hot/warm/cold tiering strategy, cutting infrastructure cost dramatically versus memory-resident graph databases.
- **Unified retrieval layer:** One query can traverse graph relationships and run vector, BM25, and reranked search together, so agents do not have to orchestrate four separate systems.
- **Temporal versioning and memory:** Every change is tracked, letting agents recall what happened, when, and in what sequence, which supports auditability and long-running agent workflows.
- **Neo4j-compatible interfaces:** OpenCypher over Bolt plus an HTTP API and SDKs make it approachable for teams already familiar with property graphs.
- **Assisted graph creation:** Ontology suggestions and entity resolution help teams build usable knowledge graphs without hand-designing every schema relationship.

**Strengths:** Free open-source edition with an active GitHub community; Storage costs scale with object storage rather than reserved database volumes; Combines graph, vector, and keyword retrieval in one system; Neo4j-compatible Cypher and Bolt reduce the learning curve

**Limitations:** Self-hosted deployment requires operating multiple node roles; Managed cloud has a monthly minimum spend; Newer project than established graph databases, so ecosystem maturity varies

[Profile (Markdown)](https://openaitools.app/tool/hydradb-oss.md)

## Polygres

Polygres is a retrieval platform that treats PostgreSQL as the backbone for AI-native applications. Rather than forcing teams to migrate to a new vector-only database, it virtualises a GraphRAG-style retrieval layer on top of Postgres, so existing relational records, embeddings and text indexes stay where they are while agents gain the ability to search by meaning, follow relationships and match exact wording in one request.

The platform bundles four complementary retrieval modes: semantic vector search for meaning, graph traversal for multi-hop relationships and shortest-path style queries, full-text search for exact terms, and hybrid search that blends them. Developers reach these capabilities through a hosted playground, a CLI for managing projects and imports, and a typed Python SDK for production applications.

Behind the managed platform are two open-source engines, pgGraph for graph traversal inside Postgres and pgContext for AI search, which teams can inspect, benchmark and self-host. Polygres supports both a managed database hosted by the company and connecting an existing Postgres instance, and it markets itself around agent memory, grounded answers with sources, product semantic search and recommendation systems.

**Pricing & Plans:** Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

- **Unified retrieval API:** Query graph, vector, full-text and hybrid retrieval through one consistent interface instead of stitching multiple systems together.
- **Graph traversal on Postgres:** The open-source pgGraph engine follows relationships across your records, enabling multi-hop and shortest-path retrieval without moving data.
- **Vector and hybrid search:** Semantic search over embeddings combines with sparse and full-text matching so queries that mix meaning and exact terms still work.
- **Developer toolkit:** A dashboard playground, CLI for imports and configuration, and a typed Python SDK make it straightforward to prototype and then ship.
- **Managed or bring-your-own Postgres:** Start on a Polygres-hosted database or connect an existing Postgres instance and extend what you already run.

**Strengths:** Keeps your existing PostgreSQL instead of forcing a migration to a specialised vector store; Combines four retrieval modes in one platform, which reduces the number of moving parts in a RAG stack; Core engines pgGraph and pgContext are open source, so the internals are inspectable and self-hostable; Familiar Postgres tooling, SQL knowledge and connection patterns carry over for most teams

**Limitations:** Team and community are relatively early-stage, so long-term support and ecosystem maturity are still developing; Being a newer product, third-party tutorials and integrations are thinner than for established vector databases; Fully managed convenience depends on using the hosted offering rather than the open-source engines alone

[Profile (Markdown)](https://openaitools.app/tool/polygres.md)

## Frequently Asked Questions

### How do HydraDB OSS and Polygres compare in monthly traffic?

HydraDB OSS has 29,635 monthly visits, while Polygres has 4,018. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

HydraDB OSS has a month-over-month growth rate of +5.8%; Polygres has -52.3%.

### How does pricing compare?

HydraDB OSS: Freemium, Contact for Pricing. The open-source edition is free to self-host with community support. Managed cloud starts around $25/month minimum usage, a Scale tier around $300/month, and an Enterprise plan with custom (contact sales) pricing. Polygres: Freemium. Polygres follows a freemium model with a free tier for getting started and paid managed plans for production workloads. Exact tiers are published on the company's pricing page, so check there for current numbers before budgeting.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Notion](https://openaitools.app/tool/t) | 173,441,670 | +0.1% | Freemium | The AI workspace where teams and agents get work done together | [.md](https://openaitools.app/tool/t.md) |
| 2 | [Exa](https://openaitools.app/tool/exa-ai) | 1,062,574 | +16.8% | Freemium, Contact for Pricing | Web search API and crawler built for AI agents | [.md](https://openaitools.app/tool/exa-ai.md) |
| 3 | [You.com](https://openaitools.app/tool/you) | 948,349 | +7% | Freemium | Search and inference APIs that give AI models real-time web data | [.md](https://openaitools.app/tool/you.md) |
| 4 | [Algolia](https://openaitools.app/tool/algolia) | 689,970 | -11% | Freemium, Contact for Pricing | AI search and retrieval platform for modern apps and commerce | [.md](https://openaitools.app/tool/algolia.md) |
| 5 | [Pinecone](https://openaitools.app/tool/pinecone) | 286,732 | -31% | Freemium, Paid, Contact for Pricing | The managed vector database and knowledge engine for production AI | [.md](https://openaitools.app/tool/pinecone.md) |
| 6 | [supermemory](https://openaitools.app/tool/supermemory-1) | 156,579 | -7.7% | Contact for Pricing | A memory and continual-learning engine that gives any AI agent persistent context | [.md](https://openaitools.app/tool/supermemory-1.md) |
| 7 | [Yep.com](https://openaitools.app/tool/yep-com) | 155,834 | +6.2% | Free | Independent web search and agent grounding API built on the Ahrefs index | [.md](https://openaitools.app/tool/yep-com.md) |
| 8 | [Honcho](https://openaitools.app/tool/honcho) | 137,752 | +2.2% | Paid, Contact for Pricing | Open-source AI memory that reasons, not just stores | [.md](https://openaitools.app/tool/honcho.md) |
| 9 | [Zilliz](https://openaitools.app/tool/zilliz) | 108,886 | -16.4% | Freemium, Paid, Free, Contact for Pricing | Fully managed Vector Lakebase and vector database for enterprise AI, built by the creators of Milvus | [.md](https://openaitools.app/tool/zilliz.md) |
| 10 | [Qdrant](https://openaitools.app/tool/qdrant-io) | 72,992 | -38.4% | Freemium, Contact for Pricing | Open-source, high-performance vector search at any scale | [.md](https://openaitools.app/tool/qdrant-io.md) |

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Source: https://openaitools.app/compare/hydradb-oss-vs-polygres

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