# MyScale vs Polygres

MyScale records 19,350 monthly visits with +21.3% month-over-month growth and Contact for Pricing, Free pricing. Polygres records 4,018 monthly visits with -52.3% growth and Freemium pricing.

| Metric | [MyScale](https://openaitools.app/tool/myscale-com) | [Polygres](https://openaitools.app/tool/polygres) |
| :--- | :--- | :--- |
| Website URL | https://myscale.com | https://polygres.com |
| Categories | AI Search Engine, Large Language Models (LLMs), AI Chatbot, AI Developer Tools | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API |
| Pricing Model | Contact for Pricing, Free | Freemium |
| Monthly Visits | 19,350 | 4,018 |
| MoM Growth Rate | +21.3% | -52.3% |
| Primary Market | United States | Global |
| Direct Visits | 7,728 | 1,605 |
| Review Score | 4.6 | 4.1 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## MyScale

MyScale is a vector database built around a simple premise: developers already know SQL, so an AI database should speak it too. Rather than adopting a new query language and separate analytics stack, you create vector indexes on columns and run familiar SELECT statements—including joins that mix vector similarity with structured filters—inside one system. That lowers the barrier for teams who want vector search but do not want to rebuild their data workflows around an unfamiliar API.

The core technical differentiator is the MSTG vector engine, which MyScale positions as delivering roughly three times the speed and cost efficiency of specialised vector databases. Alongside vector search, the platform provides BM25 full-text search with configurable tokenizers for many languages, metadata filtering for precise results, and advanced SQL vector join queries. Together these enable retrieval-augmented generation pipelines that combine semantic and keyword retrieval for better grounding, a common requirement for accuracy-sensitive AI applications. Text-to-SQL translation is also supported for complex querying.

Operationally, MyScale is offered as a fully managed service with integrations for popular languages and frameworks in the GenAI stack, plus observability tooling such as MyScale Telemetry for LLM applications. Security features include SQL-based role-based access control, SOC 2 compliance and ISO 27001 compliance. Teams typically start on a free allowance to prototype and then move to paid capacity as workloads grow; because configurations vary by scale, enterprise pricing is quoted directly rather than published.

**Pricing & Plans:** Contact for pricing / free. MyScale offers a free tier suitable for prototyping and learning, with paid and enterprise capacity quoted directly based on workload and support needs. Exact limits and rates are confirmed with the vendor.

- **Fully SQL-compatible vector database:** Run vector search, text search, filtered search and complex SQL-vector join queries using ordinary SQL syntax, indexes created with a single ALTER TABLE statement.
- **MSTG vector engine:** A purpose-built index engine marketed as delivering around 3x the speed and 3x the savings versus specialised vector databases, with faster ingestion and index building.
- **Hybrid search for RAG:** Combine vector similarity with BM25 full-text search and metadata filtering to improve retrieval quality and grounding in retrieval-augmented generation pipelines.
- **Managed, integrated service:** A fully managed offering with integrations across popular languages, frameworks and LLM tooling, including observability for AI applications.
- **Enterprise-grade security:** SQL-based role-based access control with SOC 2 and ISO 27001 compliance to support governed, production deployments.

**Strengths:** Uses standard SQL, so teams can adopt it with minimal new learning; Fuses vector, full-text and analytical queries in one managed system; MSTG engine targets strong speed and cost efficiency; Enterprise security posture with SOC 2 and ISO 27001 compliance

**Limitations:** Enterprise pricing is not published, so costs require a vendor conversation; As a managed cloud-focused product, self-hosting and portability options are more limited; Advanced tuning of vector indexes still requires understanding of embeddings and index behaviour

[Profile (Markdown)](https://openaitools.app/tool/myscale-com.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 MyScale and Polygres compare in monthly traffic?

MyScale has 19,350 monthly visits, while Polygres has 4,018. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

MyScale has a month-over-month growth rate of +21.3%; Polygres has -52.3%.

### How does pricing compare?

MyScale: Contact for Pricing, Free. Contact for pricing / free. MyScale offers a free tier suitable for prototyping and learning, with paid and enterprise capacity quoted directly based on workload and support needs. Exact limits and rates are confirmed with the vendor. 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 | [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 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | The data, evaluations and systems behind AI that has to be right. | [.md](https://openaitools.app/tool/scale.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 | [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) |
| 7 | [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) |
| 8 | [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) |
| 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 | [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) |

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Source: https://openaitools.app/compare/myscale-com-vs-polygres

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