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Tensorlake is a serverless runtime Agentic Data Applications. Tensorlake’s serverless runtime turns Python programs into distributed and durable applications. These applications scale out automatically as they process requests, without requiring queues, workflow or map-reduce engines. Tensorlake also provides a fully managed Document Ingestion API to further simplify building applications that process unstructured data like documents, images, and text.

Key Features

Building data applications is cumbersome. They require working with queues, storage systems, workflow engines, UDFs in SQL, and infrastructure tools like Kubernetes and Terraform. Tensorlake provides the fastest way to build and scale data applications:
  1. Zero operations Your application runs automatically when it receives an HTTP request. No Kubernetes or Terraform stacks to manage. You only pay for the compute tied to actual business impact.
  2. Queue free architecture There is no need to wire up queues to handle large volume of requests, retries, and workflow orchestrators. Tensorlake scales your applications as more requests come in.
  3. Familiar programming model Build in Python, use any libraries you want. You don’t have to use Spark or bolt UDFs into SQL to process data. Tensorlake gives you distributed, crash-proof execution without a learning curve.
  4. GPU and CPU processing and autoscaling Tensorlake supports both GPU and CPU processing, and automatically scales your applications based on demand.
  5. Built-in Document Ingestion Most data use-cases depend on ingesting documents, so we built a state-of-the-art document ingestion API to handle most data extraction use-cases from documents.

Common Document Ingestion Use Cases

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