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Company profile:
Startup name: RealtyCrawl
Tagline: Real estate data for apps and agents
Elevator Pitch: RealtyCrawl provides real estate data for developers, research teams, and AI workflows. Search supported listing sources and inspect property observations through REST, a hosted MCP server, or an installable agent skill.
Responses preserve source links, currencies, area units, timestamps, and request outcomes. Available fields and operations vary by source. Eligible new accounts receive 500 signup credits once.
Target Market: Developers, proptech teams, research teams, and builders of AI real estate workflows.
How will you make money?: Freemium: eligible new accounts receive 500 signup credits once; advertised paid plans start at $15/month. Usage is credit-metered.
How much capital have you raised?: null
Website: https://realtycrawl.dev/
City/Country: null
AI-assisted summary:
RealtyCrawl is a developer-focused real estate data API that provides documented requests across a catalog of public property-listing sources, with common metadata for provenance, availability, and usage accounting. (Source: https://realtycrawl.dev/about) Its relevance is straightforward: property-data products often need to work across fragmented listing sources while retaining information about where each observation originated and whether a source request actually completed. (Source: https://realtycrawl.dev/)
Problem and target users
RealtyCrawl targets developers, researchers, real estate teams, and builders of agent-based tools that need listing, property, or market observations from supported public sources. (Source: https://realtycrawl.dev/about) The underlying implementation challenge includes source-specific identifiers, pagination, missing fields, temporary failures, and changing source behavior—details that can make direct integrations costly to maintain. (Source: https://realtycrawl.dev/about)
The company’s positioning is not that every catalogued source is continuously available: it distinguishes documented source coverage from live availability and asks users to check its capabilities endpoint before executing requests. (Source: https://realtycrawl.dev/about) That distinction matters for teams building customer-facing search or analytics products, where a documented endpoint alone is not a guarantee of usable data at a given time. (Source: https://realtycrawl.dev/platforms)
Product and solution
RealtyCrawl offers a shared API contract for real estate data, with a catalog that currently lists more than 200 researched platforms and more than 60 endpoints in a preview release stage. (Source: https://realtycrawl.dev/about) The catalog includes major consumer portals such as Zillow, Redfin, Realtor.com, Rightmove, and idealista, alongside rental, commercial, land, auction, and international property platforms. (Source: https://realtycrawl.dev/platforms)
Its API supports source-specific listing searches as well as supported multi-source searches, while returning source outcomes so an application can distinguish completed sources from failed ones. (Source: https://realtycrawl.dev/about) RealtyCrawl also emphasizes preserving source identifiers, observation timestamps, currencies, area units, request identifiers, completeness information, and credit usage alongside returned records. (Source: https://realtycrawl.dev/)
For AI-enabled workflows, the product is available through REST, OpenAPI, and a hosted or local Model Context Protocol server, allowing an agent to inspect operations, availability, input schemas, and expected credit cost before making a request. (Source: https://realtycrawl.dev/mcp-server) A public GitHub repository provides MCP tooling for the service and describes free capability discovery alongside explicitly budgeted endpoint calls. (Source: https://github.com/realtycrawl/realtycrawl-mcp)
Business model, pricing signals, and traction
RealtyCrawl uses a credit-based, usage-oriented pricing model rather than a recurring subscription requirement: its published plans include 100 free evaluation credits, a $15 Starter pack with 2,500 credits, a $49 Growth pack with 20,000 credits, and a $299 Pro pack with 150,000 credits. (Source: https://realtycrawl.dev/pricing) The company says paid credits do not expire, there is no automatic recharge, and failed provider calls are refunded. (Source: https://realtycrawl.dev/pricing)
The pricing page states that a successful source crawl generally uses 50 credits and a stored read uses one credit, while endpoint-specific charges are shown before purchase or execution. (Source: https://realtycrawl.dev/pricing) RealtyCrawl publicly labels the product as being in preview and says its near-term priority is converting documented coverage into verified, usable requests. (Source: https://realtycrawl.dev/about)
Expert take
RealtyCrawl’s strongest positioning is operational transparency: it treats coverage, source provenance, incomplete results, and request cost as first-class parts of the developer experience rather than hiding them behind a generic data API. That is a sensible design for founders building data-dependent real estate workflows, especially where traceability matters.
The principal unknown is reliability at scale across individual sources and geographies. Prospective customers should validate the exact source-operation-country combination needed for their workflow, test returned fields, and design products to handle partial coverage rather than assuming a universal property-data feed.
Note: Information based on publicly available sources at the time of writing, and summarized by AI.
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