# Polygres vs Shaped

Polygres records 4,018 monthly visits with -52.3% month-over-month growth and Freemium pricing. Shaped records 11,871 monthly visits with -39.2% growth and Contact for Pricing pricing.

| Metric | [Polygres](https://openaitools.app/tool/polygres) | [Shaped](https://openaitools.app/tool/shaped) |
| :--- | :--- | :--- |
| Website URL | https://polygres.com | https://www.shaped.ai |
| Categories | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API | AI Search Engine, AI API, Large Language Models (LLMs), AI Social Media |
| Pricing Model | Freemium | Contact for Pricing |
| Monthly Visits | 4,018 | 11,871 |
| MoM Growth Rate | -52.3% | -39.2% |
| Primary Market | Global | United States |
| Direct Visits | 1,605 | 4,726 |
| Review Score | 4.1 | 4.8 |
| Saved Count | 0 | 3 |
| 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)

## Shaped

Shaped positions itself as "the only vector database with a feedback loop." Instead of a static embedding store, it continuously learns from every user interaction — clicks, purchases, watch time — so results get sharper the more your product is used. A single ShapedQL query can retrieve by text, user ID, or item ID, apply filters, score with ML models, and re-rank for diversity all at once.

The platform is built on a three-layer architecture. The query layer handles real-time retrieval and ranking with sub-50ms latency. The intelligence layer manages dynamic embeddings, user and item context, and continuous model training. The data layer connects to 30+ sources including Snowflake, BigQuery, Redshift, Kafka, PostgreSQL, Shopify, and Amplitude, unifying batch and streaming data under a single schema without ETL.

Shaped advertises enterprise-grade security with SOC 2 Type II certification, GDPR/CCPA/HIPAA compliance, and a 99.95% uptime SLA, operating at 5B+ document scale. The site notes that Shaped has been acquired by Whatnot, while the product continues to operate under the Shaped brand with a free $100 credit tier and no credit card required to start.

**Pricing & Plans:** Contact for pricing. New users receive $100 in free credits with no credit card required; plans for Shaped Cloud are quoted based on usage and scale.

- **ShapedQL unified query language:** Retrieve, filter, score, and re-rank across semantic search, keyword search, and behavioral models in a single SQL-like query.
- **Personalized hybrid search:** Blends semantic and keyword retrieval, then re-ranks results by predicted click-through and engagement for each individual user.
- **Continuous learning feedback loop:** ML models retrain on live interaction signals so ranking quality improves automatically as your product generates traffic.
- **30+ native connectors:** Stream data directly from warehouses, analytics tools, and event pipelines like Snowflake, Kafka, BigQuery, and Shopify with no ETL.
- **Sub-50ms serving with enterprise scale:** Handles 5B+ documents at 99.95% uptime SLA, with SOC 2 Type II, GDPR, CCPA, and HIPAA compliance.

**Strengths:** Unifies vector search, ML re-ranking, and training in one query instead of a fragile multi-vendor stack; Continuous learning from real interaction signals keeps results improving automatically; Broad connector ecosystem ingests batch and streaming data without custom ETL; Strong compliance posture (SOC 2 Type II, GDPR, HIPAA) and a published 99.95% uptime SLA

**Limitations:** Pricing is quote-based, so costs are hard to estimate before contacting sales; The ShapedQL and pipeline concepts carry a learning curve for teams new to relevance engineering; Now part of Whatnot, so long-term standalone roadmap clarity may depend on the acquirer

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

## Frequently Asked Questions

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

Polygres has 4,018 monthly visits, while Shaped has 11,871. 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%; Shaped has -39.2%.

### 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. Shaped: Contact for Pricing. Contact for pricing. New users receive $100 in free credits with no credit card required; plans for Shaped Cloud are quoted based on usage and scale.

## 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 | [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) |
| 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/polygres-vs-shaped

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