Sieve is designed for researchers and AI developers who need access to research-grade data, multimodal scale, and custom data collection for training AI models.

What can I do with Sieve?

With Sieve, you can capture and aggregate multimodal data, perform custom data collection tailored to specific research needs, and access densely annotated datasets for effective AI training.

How much does Sieve cost?

Sieve publishes a custom / contact-sales pricing model — get a quote on their site.

How is Sieve different from alternatives?

Sieve focuses on providing comprehensive multimodal data sourcing, custom collection capabilities, and compliance-first data handling, which distinguishes it in the AI Data category.

Does Sieve have a free trial?

Check Sieve's site for current trial availability.

Sieve Review: Pricing, Features & Alternatives

Q: What is Sieve?

Sieve is an AI Data tool that builds the data and environments frontier AI labs use to train the next generation of multimodal systems.

The multimodal data lab

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About Sieve

Sieve builds the data and environments frontier AI labs use to train the next generation of multimodal systems.

Quick facts about Sieve

Sieve is an AI tool tracked by Relve in the AI Creative Tools category. It uses a Paid pricing model and runs on the web at sievedata.com.

The Relve catalog tracks 200+ live AI tools in this category. Sieve currently sees roughly 5K monthly site visitors, with a Domain Rating of 27 on Ahrefs' authority scale.

Closest alternatives: AiPassportPhotos, Builder.io, SkyReels, NightCafe, Lumeo. Compare Sieve head-to-head with any of these on the /compare surface — same feature axes, pricing tiers, and traffic side-by-side.

Best for: teams looking for ai creative tools-class capabilities with a paid entry point. The Relve editorial team refreshes traffic, ranking, and feature data for Sieve on a rolling 24-hour cycle, so the numbers above reflect the most recent snapshot of where the tool sits in the market.

Key Features

Data Sourcing

Source

We capture and aggregate multimodal data across real-world, digital, and simulated environments. This allows users to access a diverse range of data types that are essential for training AI models. The comprehensive sourcing ensures that the data reflects various contexts and scenarios.

Custom collection

We can capture targeted real-world, digital, and simulated workflows based on the exact capabilities your team wants to improve. This feature allows for tailored data collection that meets specific research needs, enhancing the relevance and applicability of the datasets.

Data Quality

Filter

We score for semantics, rights, artifacts, task quality, and more. This filtering process ensures that only high-quality data is selected for use, which is crucial for effective AI training. Users can trust that the data they receive meets stringent quality standards.

Dense annotations

Captions, transcripts, object labels, action metadata, camera signals, UI events, and custom schemas are added to the datasets. These annotations enhance the usability of the data by providing context and detailed information, making it easier for AI models to learn from the data.

Compliance-first

We support filtering, licensing, consent, retention, and permission requirements based on your training data needs. This feature ensures that all data handling complies with legal and ethical standards, providing peace of mind for users concerned about data governance.

Data Delivery

Deliver

We package training-ready datasets, evaluation sets, and environments for secure delivery. This feature streamlines the process of obtaining data, allowing users to quickly access the resources they need for their AI projects. The secure delivery ensures that sensitive data is handled appropriately.

Secure delivery

End-to-end encryption, custom data retention, secure transfer, and SOC 2 Type 2 controls are implemented to protect data during delivery. This feature provides users with confidence that their data is secure and compliant with industry standards, which is critical for sensitive AI applications.

Data Indexing

Index

We index billions of videos, images, audio clips, and interaction traces with purpose-built detectors and embeddings. This indexing process allows for efficient retrieval and utilization of data, enabling users to quickly find the specific datasets they need for their AI models.