# Polygres vs Zilliz

Polygres records 4,018 monthly visits with -52.3% month-over-month growth and Freemium pricing. Zilliz records 108,886 monthly visits with -16.4% growth and Freemium, Paid, Free, Contact for Pricing pricing.

| Metric | [Polygres](https://openaitools.app/tool/polygres) | [Zilliz](https://openaitools.app/tool/zilliz) |
| :--- | :--- | :--- |
| Website URL | https://polygres.com | https://zilliz.com |
| Categories | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API | AI Developer Tools, AI Search Engine, AI Knowledge Base, Large Language Models (LLMs) |
| Pricing Model | Freemium | Freemium, Paid, Free, Contact for Pricing |
| Monthly Visits | 4,018 | 108,886 |
| MoM Growth Rate | -52.3% | -16.4% |
| Primary Market | Global | United States |
| Direct Visits | 1,605 | 36,843 |
| Review Score | 4.1 | 4.7 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## 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)

## Zilliz

Zilliz Cloud is a managed Milvus platform designed for production AI retrieval at scale. The core value proposition is that teams get the performance of a purpose-built vector database without operating the cluster themselves — provisioning, scaling, backups, and upgrades are handled by the service. Zilliz states the platform is production-tested across more than 10,000 enterprises over eight years and engineered to handle 100B+ entities and 10K+ QPS with consistent latency.

The platform has expanded beyond pure vector search into a broader "Vector Lakebase" concept, unifying real-time serving, on-demand discovery, and batch analytics over lake-native storage. Highlights include tiered architecture options (performance-optimized, capacity-optimized, and tiered-storage solutions), unlimited namespaces for multi-tenant AI apps, hybrid retrieval combining vector, BM25 full-text, JSON, and geospatial filtering with reranking, and an on-demand compute mode that bills per query rather than per provisioned capacity.

Zilliz supports multiple deployment paths: a fully managed SaaS on Zilliz Cloud, Bring Your Own Cloud (BYOC) where data stays in your VPC, migrations from other vector databases, and self-hosted open-source Milvus. It is available across cloud regions on AWS, Google Cloud, and Azure, with a developer hub, documentation, notebooks, and a community Discord for the Milvus ecosystem.

**Pricing & Plans:** Zilliz follows a freemium model. There is a free tier for getting started with managed Milvus, flexible paid plans including a Business Critical tier, and enterprise options priced by contact. The website provides a cost calculator and a public list-price page detailing every billing item, so teams can estimate spend before committing.

- **Fully Managed Zilliz Cloud:** A managed Milvus service handling provisioning, scaling, reliability, and upgrades so teams focus on AI applications rather than cluster operations.
- **Billion-Scale Vector Search:** Engineered for 100B+ entities and 10K+ QPS with consistent, predictable latency across tiered performance and storage configurations.
- **Hybrid and Full-Spectrum Retrieval:** Combine vector search with BM25 full-text, JSON, and geospatial queries, plus filtering and reranking for expressive multimodal retrieval.
- **Multi-Tenancy and Global Clusters:** Unlimited namespaces for multi-tenant AI apps, plus multi-region deployment with replication and failover for low-latency global access.
- **Lake-Native Storage and On-Demand Compute:** Keep data and indexes on S3 in open formats and pay per query with on-demand compute, cutting cost for spiky or low-traffic workloads.

**Strengths:** Managed service removes the operational burden of running Milvus at scale; Backed by the original creators of Milvus, with an active open-source ecosystem; Global multi-cloud deployment across AWS, Google Cloud, and Azure regions; Transparent pricing with a cost calculator and detailed public list prices

**Limitations:** Full feature depth and tiering options can be complex for small teams to evaluate; Enterprise-grade pricing may be more than hobby projects need versus self-hosted Milvus; Advanced lake-native and BYOC features require cloud engineering knowledge to set up; Vector database market is crowded, making benchmarking against alternatives necessary

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

## Frequently Asked Questions

### How do Polygres and Zilliz compare in monthly traffic?

Polygres has 4,018 monthly visits, while Zilliz has 108,886. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Polygres has a month-over-month growth rate of -52.3%; Zilliz has -16.4%.

### How does pricing compare?

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. Zilliz: Freemium, Paid, Free, Contact for Pricing. Zilliz follows a freemium model. There is a free tier for getting started with managed Milvus, flexible paid plans including a Business Critical tier, and enterprise options priced by contact. The website provides a cost calculator and a public list-price page detailing every billing item, so teams can estimate spend before committing.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [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) |
| 2 | [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) |
| 3 | [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) |
| 4 | [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) |
| 5 | [Jina AI](https://openaitools.app/tool/jina-ai) | 280,782 | -16.5% | Freemium, Paid | Search foundation built from embeddings, rerankers, and reader APIs | [.md](https://openaitools.app/tool/jina-ai.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 | [TwelveLabs](https://openaitools.app/tool/twelvelabs) | 133,085 | -3% | Freemium, Contact for Pricing | Video intelligence infrastructure that makes every second of footage searchable and analyzable | [.md](https://openaitools.app/tool/twelvelabs.md) |
| 9 | [Meilisearch](https://openaitools.app/tool/meilisearch) | 119,713 | -21.5% | Free Trial, Contact for Pricing | Lightning-fast search engine you can self-host or run in the cloud | [.md](https://openaitools.app/tool/meilisearch.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/polygres-vs-zilliz

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)