# Mireye vs Polygres

Mireye records 10,770 monthly visits with +13.9% month-over-month growth and Freemium, Contact for Pricing pricing. Polygres records 4,018 monthly visits with -52.3% growth and Freemium pricing.

| Metric | [Mireye](https://openaitools.app/tool/mireye) | [Polygres](https://openaitools.app/tool/polygres) |
| :--- | :--- | :--- |
| Website URL | https://www.mireye.com | https://polygres.com |
| Categories | AI API, AI Agent, AI Developer Tools, AI For Data Analytics | AI Developer Tools, AI Agent, AI Knowledge Graph, AI API |
| Pricing Model | Freemium, Contact for Pricing | Freemium |
| Monthly Visits | 10,770 | 4,018 |
| MoM Growth Rate | +13.9% | -52.3% |
| Primary Market | United States | Global |
| Direct Visits | 8,612 | 1,605 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Mireye

Mireye positions itself as infrastructure for physical-world AI agents. Rather than asking developers to stitch together federal downloads, county parcel files and GIS tooling, it exposes a small set of endpoints plus MCP tools that an agent can call in natural language. You can ask a question about a place, enrich a parcel with authoritative identity and attributes, search for parcels matching criteria, or fetch cited fields at any US coordinate. Every returned value carries its source name, fetch timestamp and a confidence level, which matters for insurance, lending and diligence workflows where provenance has to survive scrutiny.

The catalog leans heavily on authoritative US sources such as USGS, FEMA, NOAA, USDA, EPA, EIA, FCC, Census, NREL, BLM and others, complemented by open and licensed layers like Overture and Sentinel-2, for roughly 97 sources in total. Presets bundle common questions - flood risk, solar siting, data center siting, grid interconnection, wildfire underwriting, terrain, utilities and more - into one call. Bulk work is supported through batch fetch of many locations per request, and long jobs run through a separate runs endpoint.

A distinctive idea is the field request loop: if the catalog lacks a field you need, you file a request, Mireye researches, builds and validates it, and the new field becomes permanently available to every future caller. Coverage is the United States, with drive-time analysis extending to Canada. The service is backed by Y Combinator and presents itself as a paid, usage-metered API rather than a consumer app.

**Pricing & Plans:** Free tier with pay-as-you-go usage and a one-time $10 credit that never expires. Enrich costs about $0.02 per parcel, Search about $0.20 per page, Ask about $0.10 per answer, and driving proximity about $0.004 per calculation on the Free plan. Fetch, geocoding and straight-line proximity are free. Paid plans start at $49/month (Build) and $199/month (Growth) with included usage and 10-25% lower operation rates. You can add funds from $10; auto-recharge is optional.

- **Single API plus MCP server:** Agents such as Claude, ChatGPT, Gemini, Cursor or custom builds reach Mireye through one REST API and MCP tools, returning a replayable plan of the steps it ran.
- **Cited data everywhere:** Every returned field includes its source, fetch time and confidence level, so provenance can be forwarded to an end user, underwriter or auditor without extra work.
- **Parcel enrichment and search:** Attach authoritative parcel identity, ownership and attributes to a coordinate, or filter the US parcel universe by state, size and supported fields to get ranked candidates.
- **Preset and batch operations:** Ready-made presets cover flood risk, solar and wind siting, data center siting, grid interconnection, wildfire underwriting and terrain; hundreds of locations can be sent in one call.
- **Field request pipeline:** Request a field the catalog lacks, and once researched and validated it goes live permanently for all callers - requests that cannot be built are rejected honestly.

**Strengths:** Every field ships with a named source and confidence level, which is rare for geospatial APIs; Free Fetch, geocoding and straight-line proximity make prototyping cheap; Single API and MCP server reduce integration work across many agent frameworks; Field request loop lets you extend the catalog instead of switching vendors

**Limitations:** Data coverage is essentially US-only, with Canada limited to drive-time analysis; Per-operation pricing on Ask and Search can add up quickly at high volume; Real estate and infrastructure workflows still require domain knowledge to interpret results

[Profile (Markdown)](https://openaitools.app/tool/mireye.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 Mireye and Polygres compare in monthly traffic?

Mireye has 10,770 monthly visits, while Polygres has 4,018. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Mireye has a month-over-month growth rate of +13.9%; Polygres has -52.3%.

### How does pricing compare?

Mireye: Freemium, Contact for Pricing. Free tier with pay-as-you-go usage and a one-time $10 credit that never expires. Enrich costs about $0.02 per parcel, Search about $0.20 per page, Ask about $0.10 per answer, and driving proximity about $0.004 per calculation on the Free plan. Fetch, geocoding and straight-line proximity are free. Paid plans start at $49/month (Build) and $199/month (Growth) with included usage and 10-25% lower operation rates. You can add funds from $10; auto-recharge is optional. 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 | [Composio](https://openaitools.app/tool/composio) | 2,782,603 | +22.8% | Freemium, Contact for Pricing | Give your AI agents secure, authenticated access to 1,700+ apps | [.md](https://openaitools.app/tool/composio.md) |
| 2 | [TinyFish](https://openaitools.app/tool/tinyfish) | 1,124,952 | +199.7% | Freemium, Contact for Pricing | One API key for agent-grade web search, fetch, browsing and automation | [.md](https://openaitools.app/tool/tinyfish.md) |
| 3 | [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) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [Treg](https://openaitools.app/tool/treg) | 452,935 | +223.4% | Freemium | One key for 4,100+ agent tools, pay per call | [.md](https://openaitools.app/tool/treg.md) |
| 8 | [Merge](https://openaitools.app/tool/merge-dev) | 313,201 | +1.7% | Freemium, Contact for Pricing | The universal context engine connecting agents to data, tools, and models | [.md](https://openaitools.app/tool/merge-dev.md) |
| 9 | [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) |
| 10 | [Thor Data](https://openaitools.app/tool/thor-data) | 181,966 | -3.5% | Free Trial, Paid | Proxies and web data APIs for scraping and AI pipelines | [.md](https://openaitools.app/tool/thor-data.md) |

---
Source: https://openaitools.app/compare/mireye-vs-polygres

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