Sold by: Wherobots
Deployed on AWS
Wherobots is the AI Context Engine for the Physical World: the missing infrastructure layer for AI that needs to reason about physical reality. Existing AI context infrastructure was built for text, code and traditional databases. The most consequential enterprise decisions involve assets in the built environment: supply chains, climate exposure, geopolitics, infrastructure, and more.
Wherobots helps teams process satellite imagery at planetary scale, run precise spatial joins across billions of geometries, deploy computer vision models on Earth data, and give AI agents persistent memory of everything their organization does in the physical world. Built by the original creators of Apache Sedona, in use by teams at Amazon, Uber, Overture Maps, Here Technologies, Cotality, Regrid, and thousands more.
Upgrade to the professional edition on AWS Marketplace for a free month of Wherobots valued at $300, and including compute credits for the same amount of data processing value.
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Overview
Video
Wherobots RasterFlow Introduction
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Product video
Every AI application requires a foundation of context: the data, structure, and semantics that ground model outputs in physical reality. Teams have invested heavily in context infrastructure for text. Vector databases, RAG pipelines, semantic search. Physical-world data has no equivalent layer. Location, imagery, and spatial relationships remain outside the reach of AI context infrastructure. Wherobots is that layer. It provides the spatial context that grounds AI in the physical world, so agents and models can reason about the world with the same fidelity they bring to documents and traditional enterprise data.
Key Capabilities: Spatial AI Coding Assistant: connects AI coding environments to spatial datasets via VS Code and MCP, enabling natural language queries, dataset discovery, and job execution against physical-world data.
Global Hub: a single location for every physical-world asset a team builds: data, notebooks, models, workflows, and recipes.
WherobotsDB: 300+ accelerated and efficient spatial functions covering vector geometry and raster processing, with native Spark SQL support for tabular operations, based on Apache Sedona built by the original creators.
RasterFlow: a planetary scale inference engine that ingests satellite and aerial imagery, builds inference-ready mosaics, runs computer vision models at scale, and delivers structured spatial features directly to WherobotsDB or other applications or infrastructure downstream.
Contact us at info@wherobots.com or learn more at <www.wherobots.com >
Highlights
- The AI Context Engine for the Physical World. WherobotsDB delivers 300+ spatial functions covering vector and raster data, with native Spark SQL for tabular operations. RasterFlow runs planetary-scale computer vision inference on satellite and aerial imagery. Map matching, geostatistics, map tiling, and GeoAI ship out of the box.
- Up to 20x faster, serverless, pay-as-you-go. Wherobots runs spatial workloads up to 20x faster and at a fraction of the cost of any other processing engine at scale. Spin up a notebook or a job in minutes. No cluster management, no capacity planning. Pay only for the resources you use.
- Built by the original creators of Apache Sedona. No lock-in. Wherobots is 100% code compatible with Apache Sedona across all spatial functions. Apache Sedona has surpassed 68 million downloads and grows by over two million every month. Shift back to open source anytime. Your code comes with you.
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Dimension | Description | Cost/unit |
|---|---|---|
Consumption unit | Wherobots consumption unit. See wherobots.com/pricing/ for more information. | $0.01 |
Dimensions summary
You pay through a single usage-based dimension: the Wherobots consumption unit. This is a pay-as-you-go model, so your cost scales with how much compute you actually use. A consumption unit reflects the computational capacity provisioned to a serverless runtime. As your spatial workloads grow, your consumption units rise, and your bill scales accordingly. There are no separate tiers or instance sizes to select in this listing. You are billed only for the consumption units you draw. See wherobots.com/pricing for the details behind how units are measured.
Top-of-mind questions for buyers
A consumption unit reflects the computational capacity provisioned to a serverless runtime. One unit provides capacity similar to a 32 vCPU spatial compute cluster running on a stable distributed processing engine. The amount of units drawn depends on the type and size of runtime your workloads use.
Data transfer and managed storage are provided at no extra charge. You are billed only for the consumption units your compute draws. If storage or transfer charges are introduced later, the vendor states it will give notice so you can plan your costs.
Consumption is metered by runtime type and size. Larger or more concurrent runtimes emit more consumption units per hour, so your bill rises with the compute you provision. Serverless runtimes auto-scale, meaning you draw units only while workloads run, not when idle.
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Support
Vendor support
For help with connecting to your private data, accessing and using the global hub, working with the Spatial AI Tools (MCP, CLI, VSCode and other extensions), or running any spatial queries read our documentation or contact support at support@wherobots.com .
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Customer reviews
George W.
Game changer for geospatial workflows
Reviewed on Jun 26, 2025
Review from a verified AWS customer
Wherobots has been instrumental in enabling us to build and execute complex geospatial workloads much faster and more cost-effectively.
In addition to the performance improvement and additional out of the box spatial functions, one of the largest issues we faced with existing solutions was the inability to support large rasters. With WherobotsDB support for loading out-of-database rasters, we're able to extract insights from massive rasters in minutes rather than hours.
Wherobots also provides a great jobs API to automate our workloads - the documentation and product are top-notch and it'll be exciting to watch it develop.
If your team does any distributed geospatial work, I highly recommend giving Wherobots a try.