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LabelLLM vs Prodigy

Both stand on ai data labeling tools. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

LabelLLM

Paid Web
Visit LabelLLM

Prodigy

Paid APILinuxMac+3

Prodigy combines broad annotation coverage with developer control over workflows, interfaces, storage, and deployment. Its paid licensing and technical setup make it a closer fit for teams with Python skills than for casual annotators.

Visit Prodigy

The facts

LabelLLMProdigy
Free planNot publishedNo
Open sourceNot publishedNo
Runs onWebAPI, Linux, Mac, Self-hosted, Web, Windows
Lowest paid planNot publishedSee the plans
Plans publishedNone2
MakerNot publishedNot published
Websitegithub.comprodigy.ai
On ai data labeling tools#22 of 22#18 of 22

LabelLLM: what it does well

    Prodigy: what it does well

    • 23 interfaces cover varied annotation tasks.
    • Custom workflows use Python recipes.
    • Integrates with spaCy and major LLM APIs.
    • Can run locally, air-gapped, or on-premise.
    • Exports data as JSON and JSONL.

    What to know first

    • No free plan; Personal starts at $390, paid once.
    • Company licensing requires packs of five seats.
    • Hosted trials are not offered to individuals.
    • Setup assumes Python and command-line familiarity.

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