LabelStudio Heartex Vs Diffgram

Top Reasons to Choose Diffgram Over Label Studio / Heartex

  1. Diffgram is a Development System.
  2. Diffgram has all code in open source.
  3. Diffgram has more Integrations.
  4. Diffgram scales better.

Diffgram is like Postgres, LabelStudio/Heartex is like SQLite

Similar Things

  • Customizable Interface - Both are customizable
  • Pre-built and customizable templates
  • NLP, Computer Vision, Audio & Speech, Video
  • Review Workflows
  • Data stays on your servers
  • Certifications

Diffgram has all code in open source.

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All Diffgram Code is open source. Label studio removes several important features out of the box like persistent storage (and all the purple icons). Why would access control and persistent storage, two of the most basic possible things be in closed source?

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Diffgram has more Integrations

See:
Action Workflows Introduction
Storage Providers List

Diffgram is more Customizable

See Customization Features

Diffgram Scales Better then Label Studio

Label studio users, using a large amount of hardware, routinely report seeing slow access times of 10s for relatively small datasets in Label Studio. Compare that to Diffgram where a 4 core DB in Diffgram (1/6) the resources, can scale to 100M+ Annotations at ~300MS average response time. Depending on how a person does the math this is literally 720x faster. Of course there are many dimensions to scale and we talk about some of the other ones here..

Startup time and ongoing use

Depending on your background it is possible you may find Labelstudio a little faster to get started. While Diffgram may be just as fast to get started, there are edges cases that do take longer. The main thing to keep in mind is what you are actually getting. With Diffgram you are getting familiar with a major new technology that scales. With Labelstudio you are getting a small fraction of that. It's like setting up Postgres vs SQLite. Sure SQLite maybe is a little easier, but it won't scale to production like Postgres.

Old Comparison Article

ML Backend

See ML Backend