Cerul

Cerul's pitch is narrow and honest: video memory infrastructure that returns verifiable evidence rather than a summary you have to trust. The API surface is deliberately small — upload, check searchability, search, export — and the search response is the product. Each hit carries a relevance score, a start and end second, the matched text, and locator URLs, which is exactly the shape an AI agent needs to cite a claim without hallucinating a timestamp. That evidence object is the differentiator; most teams stitching together a transcription API and a vector database have to invent it themselves. The desktop app is the on-ramp. It's free, runs on macOS Apple silicon, requires no account, and indexes your own folders, YouTube channels, and podcasts locally. The vendor publishes roughly 82ms search latency at p50 on its own example, and the privacy design is explicit: local indexing and search are independent of a Cerul Account, sign-in adds cloud and collaboration benefits, and it does not trigger upload. Media leaves the machine only after explicit approval for cloud execution. Local search also stays unlimited even when a library cap blocks new indexing, so old content remains findable. The capability set has been widening through the v0.0.10x series. v0.0.104 added resumable cloud uploads and index reuse so the same content isn't reprocessed and recharged — a meaningful cost control, since indexing bills by the minute. v0.0.106 combined speech, on-screen text, and key visual frame memory for cloud search and put the app, MCP, and API on one credits balance. v0.0.107 reports speech, visual, and OCR coverage separately, so you can see which modality actually indexed a file, and ties credits to successful media seconds and OCR frames rather than to attempts. That last detail matters: a failed job shouldn't cost you. The constraints are real and worth reading before you architect. The desktop app is macOS Apple silicon only — Windows and Linux are roadmap, not shipped. Scene understanding (places, objects, actions) and embodied/head-mounted footage are roadmap items delivered in the same clip structure, so the API shape is stable but the capability isn't there yet. On the cloud side you pay three ways: per minute indexed, per search, and per gigabyte of retained original video bytes after the first 30 free days per asset. Search at 0.5 credits per request works out to $10.00 per 1,000 searches; indexing at 2 credits per minute means an hour of video costs 120 credits, or $2.40; storage at 2 credits/GB per 30-day month billed daily means a 500 GB archive runs about $20/month in credits alone. Semantic annotation is 15 credits per minute and needs separate activation. Each top-up expires after 12 months, so you can't sit on a large prepay indefinitely. If a daily storage payment fails, new jobs and searches pause, with a 90-day grace period to download and delete, and continued nonpayment can delete workspace assets and indexes after at least 7