Sieve
Video and audio AI infrastructure API for developers - run transcription, object detection, lip sync, and custom ML models on media files at scale.
Sieve is a developer-facing video and audio AI platform that provides APIs for media intelligence tasks including transcription, speaker diarization, face detection, object segmentation, lip sync, background removal, and video dubbing. Developers call Sieve's APIs to run these models on uploaded media files without provisioning GPU infrastructure. Sieve also allows teams to deploy custom ML models as API endpoints, making it a serverless ML inference layer for media workloads. Founded in 2022 and Y Combinator-backed, Sieve targets engineering teams building AI-native video and audio features who want managed infrastructure rather than running GPU clusters. The platform offers usage-based pricing with a free tier covering the first $10 of monthly usage.
Key Features
- Pre-built APIs for transcription, speaker diarization, face detection, and object segmentation
- Lip sync and video dubbing API for generating synchronized video from translated audio
- Background removal and video segmentation models available as one-call APIs
- Custom model deployment - wrap any HuggingFace or custom model as a managed API endpoint
- Async media processing with webhook callbacks for long-running video jobs
- Usage-based pricing with $10 free monthly credit covering initial experimentation
Use Cases
- Product teams adding AI video features - transcription, dubbing, segmentation - without GPU management
- Developers building media intelligence pipelines that need multiple models chained together
- Startups prototyping video AI products before investing in their own infrastructure
- Research teams running custom media ML models without writing deployment infrastructure
Pros
- Combines 10+ media AI models in one platform - no need to integrate Whisper, Diarization, and YOLO separately
- Custom model deployment turns Sieve into a managed inference layer for proprietary media models
- Usage-based pricing with a $10 free tier lets teams evaluate real workloads before committing
Cons
- Smaller model selection than general ML platforms like Replicate for non-media AI tasks
- Media processing latency is higher than self-hosted inference for time-critical real-time applications
- Custom model deployment requires Docker familiarity to containerize models for Sieve deployment
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