Cloudglue

Cloudglue sits in a narrow but real gap: generic multimodal APIs will describe a single video well, but they do not give you a library you can query, a schema you can extract against repeatedly, or citations your agent can play back. That combination is the product. The workflow is: upload footage, pay 4 credits per minute for transcribe/describe and extract, segment scenes, define entity schemas, index collections, then chat and search at 1 credit per request or search. The credit ledger is the part to study before you commit — the same currency pays for ingestion, indexing, chat and search, so a chatty end-user surface can burn budget in ways a one-off extraction job will not. Strengths. Speed and duration at scale are the headline claim and they are specific: 2 hours of video in about 3 minutes, with chat and search working across hundreds of videos. The agent-native framing is not decoration — tool calling and playable citations are exactly what you need if the consumer of this data is an LLM rather than a human analyst. Schema-driven extraction is the sleeper feature: it is the difference between a demo and a pipeline that produces the same fields for video 1 and video 10,000. Server-side model management means you are not the one migrating when the multimodal stack shifts, which matters more than any single model choice. And the vendor's research work on sales transcripts was accepted to a KDD workshop, which is a reasonable signal that the video-to-structured-data problem is being worked on seriously rather than wrapped. Weaknesses and honest boundaries. There is no no-code route here — this is an API for people who write code, and the value only appears once you have built the retrieval and UI around it. Real-time and edge processing are out of scope; this is a batch indexing pipeline. Offline or local processing is not offered, so if your footage cannot leave your environment, stop here. Cost scales with minutes, and the jump from Mini ($15/mo, 2 hours indexed) to Starter ($45/mo, 6 hours indexed) to Builder ($350/mo, 63 hours indexed) means any serious library pushes you up fast; the volume tiers carry 22% and 33% discounts, which tells you the per-hour economics improve a lot at scale but only if you commit to the pack. Where it fits. A startup building a support or sales assistant over recorded calls, an edtech team making lecture archives searchable with timestamp links, a security or ops team generating structured reports from footage against a fixed schema. Where it does not: agencies that want to click through a UI, teams needing frame-level real-time inference, or anyone whose video cannot be sent to a managed service. Compared with wiring up a general vision model yourself, you are paying for the collection layer, the schema consistency and the agent plumbing — which is usually the part that takes the longest to build and the part nobody wants to maintain.