Cloudglue Frequently Asked Questions

Cloudglue Frequently Asked Questions. Cloudglue converts video/audio into clean, structured, LLM-ready data—powering smarter AI workflows in seconds.

FAQ from Cloudglue

What is Cloudglue?

Cloudglue is an API-native platform that ingests video and audio files and outputs structured, model-ready data — including time-aligned transcripts, speaker diarization, keyframe descriptions, topic clusters, and vector embeddings — all optimized for consumption by LLMs, retrieval systems, and autonomous agents.

How to use Cloudglue?

Developers integrate Cloudglue using RESTful APIs. Start with `POST /v1/query` for immediate video Q&A, or compose modular pipelines using dedicated endpoints for transcription, summarization, entity extraction, and multimodal embedding — all with consistent authentication and error handling.

What does Cloudglue do?

It eliminates the video-to-data bottleneck: converting unstructured multimedia into standardized, machine-actionable formats — enabling AI applications to interpret, search, reason over, and respond to video content as naturally as they do text.

How fast is Cloudglue?

Processing scales linearly and predictably: a 50-minute video yields complete, structured output — including embeddings and metadata — in ≤3 minutes. Latency remains sub-second for queries on indexed libraries, regardless of scale.

What kind of control does Cloudglue offer over data extraction?

Full spectrum control — from lightweight `transcribe_only` mode (fast, low-cost) to `multimodal_deep` (visual + audio + contextual analysis). You define granularity per use case: segment duration, speaker resolution, confidence thresholds, and output schema.

Is Cloudglue suitable for enterprise use?

Absolutely. Cloudglue supports SSO, audit logging, private VPC deployment options, SLA-backed uptime, and compliance-ready architecture — trusted by fast-growing AI teams building mission-critical video intelligence products.

How are API Credits consumed?

Credits are deducted per successful request, based on media duration and selected feature tier. For example: `transcribe` uses 2 credits/minute; `extract` (with speaker + summary + entities) uses 6 credits/minute; `embed` consumes 4 credits/minute. Unused credits roll over monthly.