Sieve

Name: Sieve
Brand: sieve

AI Native

A service that allows you to store all your bookmarks, notes, movies and images in one place.

Est 2025|Raised $500K

LinkedIn 684|Website|Web traffic 2K|FTEs 1

01Competitive Landscape

Because Sieve is primarily positioned as a video dataset provider, comparisons include both dataset/data-service competitors and API video AI platforms (adjacent).

Narrative analysis

- Sieve vs Scale AI: - Scale AI is typically selected for very large annotation programs that require workforce management and established enterprise processes. Sieve’s differentiation is presented as video-specific curation and indexing, along with a research-oriented approach. - Sieve vs cloud video APIs (AWS/Google/Azure): - Cloud providers offer reliable, documented APIs, extensive procurement channels, and public compliance documentation. They do not typically provide licensed training datasets in the same way dataset vendors do. - Sieve vs other dataset vendors: - Many dataset providers operate via custom deals; differentiation often depends on provenance guarantees, licensing terms, labeling quality, and delivery timelines. Vendor statements about rapid delivery (claims of very short timelines on vendor materials) should be contractually validated.

	Sieve (vendor product pages)	Scale AI (vendor product pages)	AWS Rekognition (vendor product pages)
Starting Price	Not public (custom dataset agreements)	Typically custom/enterprise for many programs (varies)	Public cloud pricing (pay-as-you-go)
Most Appropriate For	Teams needing curated/licensed video datasets at scale	Large-scale labeling programs and workforce-managed annotation	Standard video analysis APIs integrated with cloud provider ecosystems
Key Strength	Vendor-stated end-to-end curation pipeline (source/filter/index/annotate/query) and compliance emphasis	Mature service organization and broad data labeling footprint	Enterprise-grade infrastructure, documentation, and procurement support
Key Weakness	Limited public third-party validation (few accessible reviews/case studies) and opaque dataset pricing	Can be expensive; engagement model may be heavyweight	Not focused on providing licensed, training-ready datasets as a service

01Competitive Landscape

Because Sieve is primarily positioned as a video dataset provider, comparisons include both dataset/data-service competitors and API video AI platforms (adjacent).

Narrative analysis

Sieve vs Scale AI:
- Scale AI is typically selected for very large annotation programs that require workforce management and established enterprise processes. Sieve’s differentiation is presented as video-specific curation and indexing, along with a research-oriented approach.
Sieve vs cloud video APIs (AWS/Google/Azure):
- Cloud providers offer reliable, documented APIs, extensive procurement channels, and public compliance documentation. They do not typically provide licensed training datasets in the same way dataset vendors do.
Sieve vs other dataset vendors:
- Many dataset providers operate via custom deals; differentiation often depends on provenance guarantees, licensing terms, labeling quality, and delivery timelines. Vendor statements about rapid delivery (claims of very short timelines on vendor materials) should be contractually validated.

	Sieve (vendor product pages)	Scale AI (vendor product pages)	AWS Rekognition (vendor product pages)
Starting Price	Not public (custom dataset agreements)	Typically custom/enterprise for many programs (varies)	Public cloud pricing (pay-as-you-go)
Most Appropriate For	Teams needing curated/licensed video datasets at scale	Large-scale labeling programs and workforce-managed annotation	Standard video analysis APIs integrated with cloud provider ecosystems
Key Strength	Vendor-stated end-to-end curation pipeline (source/filter/index/annotate/query) and compliance emphasis	Mature service organization and broad data labeling footprint	Enterprise-grade infrastructure, documentation, and procurement support
Key Weakness	Limited public third-party validation (few accessible reviews/case studies) and opaque dataset pricing	Can be expensive; engagement model may be heavyweight	Not focused on providing licensed, training-ready datasets as a service

Sieve

01Competitive Landscape

Narrative analysis

02Market Map

01Competitive Landscape

Narrative analysis

02Market Map