Textract vs Dagster — Comparison

Textract

Amazon Textract is a machine learning (ML) service that uses optical character recognition (OCR) to automatically extract text, handwriting, and data

Automatically extract printed text, handwriting, layout elements, and data from any document Drive higher business efficiency and faster decision-making while reducing costs. Extract key insights with high accuracy from virtually any document. Scale up or scale down the document processing pipeline to quickly adapt to market demands. Securely automate data processing with data privacy, encryption, and compliance standards. Accurately extract critical business data such as mortgage rates, applicant names, and invoice totals across a variety of financial forms to process loan and mortgage applications in minutes. Better serve your patients and insurers by extracting important patient data from health intake forms, insurance claims, and pre-authorization forms. Keep data organized and in its original context, and remove manual review of output. Easily extract relevant data from government-related forms, such as small business loans, federal tax forms, and business applications, with a high degree of accuracy. As part of the AWS Free Tier, you can get started with Amazon Textract for free. The Free Tier lasts for three months, and new AWS customers can analyze up to: Total pages processed = 100,000 Total pages processed = 2,000,000 Price per page = $0.0015 for first 1 million and $0.0006 for pages after 1 million Total pages processed = 5,000 pages Price for page with table = $0.015 Price for page with form (key-value pair) = $0.05 Price per page with Queries = $0.015 Total pages processed = 2,000,000 pages Price for page with Tables, Forms and Queries = $0.070 for the first one million and $0.055 for the next one million Let’s assume you want to extract data from 100,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for 1 million pages is $0.01 and you process 100,000 invoices. The total cost would be $1,000. See the calculation below: Total pages processed = 100,000 Let’s assume you want to extract data from 1,500,000 invoices using the Analyze Expense API. The pricing per page in the US West (Oregon) region for one million pages is $0.01 per page and $0.008 per page after one million. The total cost would be $14,000. See the calculation below: Total pages processed = 1,500,000 Price per page = $0.01 for the first 1 million and $0.008 for the next 500,000 Let’s say you want to extract information from 100,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page for up to 100,000 pages. The total cost would be $2,500. Total pages processed = 100,000 Let’s say you want to extract information from 600,000 identity documents using the Analyze ID API. The pricing per page in the US West (Oregon) Region for 100,000 pages is $0.025 per page and $0.01 per page after 100,000. The total cost would be $7,500. Total pages processed = 600,000 Let’s say you want to extract information from 200,000 pages of mort

Dagster

Dagster is the data orchestrator platform that helps you build, schedule, and monitor reliable data pipelines - fast, flexible, and built for teams.

Dagster Labs is the organization behind Dagster, the open-source project, and Dagster Cloud. We’re a small, well-funded, and collegial team with a proven track record of shipping open-source software with global adoption. We are fortunate to be able to partner with some of the best venture capital investors in the business. We are a team that is intrinsically driven and executes with fierce urgency. We think big, aim high and are here to be the best at what we do. We value grit, resilience, and are able to persevere to get to the best outcome. We play to win and we do not mistake motion for progress, striving to quickly focus in on what really matters and avoid work about work We hold ourselves to high standards and trust each other to do the same. We do not believe that quality and velocity are at odds with each other, and taking our craft seriously means we can move fast with excellence. We we do what we say we’re going to do. We work from first principles and solve fundamental problems. We provide continuous, direct, and thoughtful feedback to one another in order to improve. When failures happen, we learn from them as an opportunity to improve our future outcomes. Our workplace should reflect the full diversity of interests, backgrounds, and ideas of all of our employees. We invest in creating experiences to foster meaningful connections and encourage everyone to connect genuinely with colleagues. Building is hard and we believe it will be more sustainable, and we will have more fun when we engage authentically and inject some levity into our daily interactions. We optimize for the group, the company, and not just for the individual. We have a mutual responsibility to support one another to succeed and multiply our impact beyond the sum of our individual parts. We sometimes put aside the work that’s most important within our focus area to help with higher-priority work in other areas. We empower people to have sufficient context across the company to be able to work cross-functionally. We sometimes operate outside of our defined responsibility and never say that something is “not our job”. We act as owners, roll our sleeves up to pitch in, and fix problems and gaps that we see. We started off as an OSS project - our community has been with us the entire journey and they are the reason Dagster Labs exists. The developer experience at Dagster Labs is everyone’s responsibility. We are dedicated to doing everything we can to improve their experience working with data platforms. This means that everyone is invested in our community, their success and their sentiment towards our products. Nick is the founder of Dagster Labs. Prior to that, he was a Principal Engineer and Director at Facebook between 2009-17, where he founded the Product Infrastructure team and co-created GraphQL. Pete previously led teams at Twitter, co-founded Smyte, and was a member of the early React team at Facebook. Yuhan was a senior software engineer and tech lead o

Textract

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Textract

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Dagster

Textract

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