VideoVector
VideoVector turns large video, audio and image libraries into schema-defined metadata, timestamped segment evidence and multimodal embeddings, then exposes them through multimodal search, SQL, agentic MediaRAG, a REST API, a Python SDK and webhooks.
What is VideoVector?
VideoVector is the media intelligence platform published by VectorMethods, a company based in Toronto, Canada. It takes large libraries of video, audio and images, from long-form broadcasts and uncut sports feeds to CCTV and bodycam exports, podcasts, webinars and archive footage, and turns them into timestamped metadata, structured fields, multimodal embeddings and searchable evidence. The platform is organised around five stages. Define sets the extraction engine: the signals, entities and summaries each run must produce, the per-segment output schema, the video-level rollup and the fields that become vector-searchable. Ingest brings media into governed indexes through manual upload or cloud connectors for GCS, S3, Azure and R2. Analyze executes extraction against an index, folder, asset or batch, producing segment-level evidence and a consolidated asset-level result. Deliver pushes results out through webhooks, cloud exports and APIs. Search queries indexes, extracted fields, segments and embeddings with vector similarity, natural-language semantics, image references, SQL and multi-step agentic retrieval. Two design choices set it apart. Models are selectable rather than in-house: work can be routed to Gemini families on GCP Vertex AI, Amazon Nova models on AWS Bedrock, Azure AI Content Understanding, Azure OpenAI, or open-source options such as Qwen2.5-VL and VideoLLaMA 3. Segmentation is a choice too, between narrative instruction-driven splitting documented for videos up to two hours, computer-vision detection using histogram analysis, velocity vectors, shot momentum and CLIP, or fixed-duration windows. Outputs are meant to leave the platform. Structured responses are defined with Pydantic schemas and nested entities, embeddings and extracted artifacts can be exported in full, and downstream systems are notified through webhooks fired on import, processing and export events. Developers work through a REST API, a Python SDK and an MCP server exposing browse, extract, retrieve and inspect tools to AI assistants. The site documents real-time detection of user-defined events across IP cameras, RTSP streams, VMS platforms and cloud video, and publishes playbooks for newsroom publishing, streaming catalogues, contextual advertising, rights and licensing, multi-source evidence review, dataset curation and grounded video RAG. Cloud, private cloud, dedicated instance and on-premise deployments are advertised for Enterprise plans.
What it does
- Extract schema-aware JSON metadata from video, audio and image libraries, with nested entities and operational taxonomies
- Search media with vector similarity, natural language, image references, structured filters, SQL and agentic retrieval
- Segment long recordings by narrative context, computer-vision signal detection or fixed-duration windows
- Generate multimodal video-to-vector embeddings from visual context, speech, transcripts and structured fields
- Build grounded VideoRAG, AudioRAG, ImageRAG and agentic MediaRAG applications over timestamped evidence
- Connect GCS, S3, Azure and R2 buckets so every new file is processed and delivered automatically
- Detect user-defined events in real time across IP cameras, RTSP streams, VMS platforms and cloud video
When to use VideoVector / When not to
A quick filter to help you decide if VideoVector is the right fit.
When to use VideoVector
- Media and archive teams that need scene-level metadata across broadcasts, production footage and historical libraries
- Newsroom, streaming and publishing operations looking for soundbites, B-roll, chapters, thumbnails and ad-break markers
- Public-sector, security and operations analysts reviewing CCTV, bodycam, dashcam, drone and submitted media together
- Developers and data teams building grounded VideoRAG or MediaRAG applications on APIs, SDK, MCP and exportable embeddings
- AI and machine-learning teams curating, clustering and labelling timestamped video examples for training and evaluation sets
When not to use VideoVector
- Creators looking for a video editor or a generative video tool: VideoVector analyses and indexes media, it does not produce it
- Individuals wanting a mobile app: no iOS or Android application is advertised, the product is web, API and MCP only
- Teams that need a non-English interface or documentation: only English is attested on the site
- Buyers who require published security certifications, since no SOC 2, ISO 27001 or HIPAA claim appears on the collected pages
- Organisations that need a fixed, published unit price before committing, as credit pack pricing is not disclosed
How to use VideoVector
A typical end-to-end flow, from setup to results.
- Open the playground on app.vectormethods.com or start on the Free plan, which requires no checkout and includes 100 video processing credits
- Create an index to organise a media collection, its extraction executions, its search scope and its permissions
- Add media by manual upload for controlled or one-off analysis, or connect a GCS, S3, Azure or R2 bucket
- Define an extraction engine: the signals, events, entities and summaries each execution must produce
- Design the segment output schema and the video-level rollup, and choose which metadata fields become vector-searchable
- Pick a segmentation mode: narrative and instruction-driven, computer-vision signal detection, or fixed-duration windows
- Run the extraction against an index, a folder, a single asset or a batch, and review the timestamped segment results
- Query the results with text, image, multimodal, SQL, conditional or agentic search, scoped to one or several indexes
- Connect delivery: cloud exports, webhooks on import, processing and export events, or direct REST API and Python SDK calls
- Generate API keys with the right scope, or attach the MCP server to an AI assistant for browse, extract, retrieve and inspect tools
Pros & Cons
Pros
- Model choice is explicit: work can be routed across commercial and open-source providers instead of a single in-house model
- The output schema belongs to the customer, defined with Pydantic and nested entities rather than a fixed metadata format
- Three segmentation strategies can be selected per workflow, balancing precision against compute budget
- Results are portable: full embedding and artifact exports, SQL access, webhooks and connectors move data into the customer's systems
- Every search result stays traceable to its source segment, extracted fields and timestamped evidence
- A complete developer surface with REST API, Python SDK, MCP server and documented API key scopes
- A genuine free entry point with 100 processing credits and no checkout, before any paid commitment
Cons
- No security certification is published: nothing on SOC 2, ISO 27001 or HIPAA appears on the collected pages
- No named subprocessor list, even though media transit through third-party model providers
- The privacy policy covers the public website, not the hosted product, whose rules live in unpublished agreements
- The vendor's position on training models with customer data is not documented anywhere on the site
- Real cost depends on credit consumption, and the price of additional credit packages is not published
- No company page, no named team and no social profiles: publisher traceability rests on an address and a domain record
- Interface, documentation and support materials are English-only
Pricing & Plans
A permanent free plan is available at no cost and without checkout, including 100 video processing credits, entry-level throughput and community support. The lowest paid entry point is the Starter plan at USD 49.00 per month, billed as a monthly Stripe subscription and including 500 video processing credits. Additional credits can be purchased on demand, though the price of those credit packages is not published.
- USD 0
- no checkout
- 100 video processing credits
- entry-level API and processing throughput
- community support
- USD 49 per month
- monthly Stripe subscription
- 500 video processing credits
- connector
- webhook
- export and API key access
- email support
- on-demand credit purchases
- USD 149 per month
- monthly Stripe subscription
- 2
- 000 video processing credits
- highest self-serve throughput
- priority support
- bulk processing workflows
- custom commercial terms on request
- custom credit allocation
- enterprise API and processing limits
- archive-scale onboarding
- dedicated instances
- SLAs
Data, GDPR & hosting
A consolidated view of how VideoVector handles your data.
GDPR overview
The privacy policy, last updated on 28 April 2026, addresses the GDPR explicitly but never claims compliance in so many words. Where the GDPR or UK GDPR applies, it names its legal bases: contractual necessity, legitimate interests, consent for optional marketing and non-essential cookies, and legal obligation. It lists data subject rights: access, correction, deletion, export, restriction, objection, withdrawal of consent and complaint to a regulator. Canadian and Californian regimes are covered as well. All requests go to a single address, support@vectormethods.com. Three things are missing: no Article 27 representative is designated, no data protection officer is named, and no data processing agreement is published, although a data processing addendum is mentioned as a possible separate contract. Note also that the policy covers the public website, not the hosted product.
Who owns the data?
The terms of use draw a clear line. The website, its content, branding and underlying materials belong to VectorMethods or its licensors, and no rights are granted beyond browsing the public site. Customers remain responsible for the rights, notices, consents and lawful basis attached to any media, uploads or connector content they submit through product, trial, API or integration workflows. Nothing in the collected pages claims ownership of customer media or of extraction outputs. One exception deserves attention: any feedback, suggestion or improvement request sent to the company grants VectorMethods a perpetual, irrevocable, worldwide and royalty-free right to use and exploit it. Hosted product use may be governed by separate signed agreements.
Reuse rights
For the public site, reuse is deliberately narrow. Visitors may use pages and documentation for informational, evaluation and business communication purposes only. Copying, modifying, redistributing, republishing, reverse engineering or creating derivative works from the site or its public materials requires prior written permission, except where applicable law allows it. The terms also forbid using public materials to build competing datasets, to benchmark unfairly, to train models on restricted content or to harvest contacts. For content processed inside the product, the picture is different: customers keep control of their own media and outputs, and the site advertises full exports of embeddings and extracted artifacts, SQL access, webhooks and connectors precisely so that indexed context and structured results can be moved into the customer's own systems. Product-specific agreements may add further conditions, and they are not published.
Data retention & training
Hosting summary
The privacy policy states that information may be processed or stored in Canada, the United States and other jurisdictions where the company or its service providers operate, and warns that those jurisdictions may apply different data protection rules and that information may be accessible to courts, law enforcement or regulators where legally required. No selectable hosting region is offered on the self-serve plans, and no EU data residency is advertised. The company is based in Toronto and its terms are governed by Ontario law. For Enterprise deployments the site advertises dedicated instances, private cloud and on-premise options, which are the only documented way to control where processing happens. Note that this policy describes the public website and pre-sales workflows: the hosting of media, indexes and extraction outputs inside the hosted product is not documented publicly and should be confirmed contractually.
Things to keep in mind
Risks and trade-offs to weigh before adopting VideoVector.
- The published privacy policy covers the public website only: the rules applying to customer media depend on separate, unpublished agreements
- Nothing on the site states whether customer content is used to train models, and no exclusion option is documented
- No subprocessor list is published, although media are processed by third-party model providers such as GCP, AWS, Azure and Hugging Face
- Sensitive use cases are promoted, including CCTV, bodycam and evidence review, without any published security certification
- The terms warn that AI outputs may be incomplete, delayed, biased or inaccurate, and require human review before consequential decisions; treating a summary or a label as established fact is the real risk
- Liability is capped at CAD 100 or the amount paid over twelve months, under Ontario law and Ontario courts
- Cost is driven by credits whose unit price is not published, which makes budgeting for a large archive difficult before a sales conversation
Setup & Integrations
Technical difficulty
Trying the platform is easy: the playground and the free plan need no installation and no checkout. Production is an integration project. Teams create indexes, design an extraction schema and a video-level rollup, choose a segmentation mode, generate scoped API keys, wire cloud connectors and handle webhook events. A Python SDK, an MCP server and a detailed API reference are provided, but schema design remains the structuring step and assumes engineering involvement. Managed rollout support is offered for operating model design, metadata schema planning and workflow integration.
Deployment
Integrations
Supported languages
Behind VideoVector
Resources
All the official URLs gathered for verification and reference.
Alternatives
Tools that compete with or complement VideoVector.
Frequently asked questions
What kinds of media can VideoVector analyse?
Can teams define their own extraction schema?
Which AI models does VideoVector use?
How do downstream systems receive the results?
Can indexes, embeddings and extracted metadata stay in our own systems?
Is there a free way to evaluate the platform?
Does VideoVector support on-premise or private cloud deployment?
Where is the data hosted?
Is there a minimum age to use the service?
Who publishes VideoVector?
Should you pick VideoVector?
VideoVector addresses a narrow but expensive problem: organisations sit on hours of footage nobody has time to watch, and the value is locked inside it. Rather than offering one managed model and one search box, VectorMethods sells control. The customer chooses the model provider, the segmentation strategy, the output schema and the delivery layer, and can export embeddings and extracted artifacts in full. For teams with engineering capacity, that combination is genuinely differentiating, and the company argues it openly against TwelveLabs on its own comparison page. The developer surface backs the claim: a documented REST API with scoped keys, a Python SDK, an MCP server, cloud connectors, webhooks and SQL over extracted fields. Entry is inexpensive, with a permanent free plan of 100 processing credits, then USD 49 and USD 149 per month, and an Enterprise track for archive-scale rollouts, dedicated instances and on-premise deployment. The reservations are about disclosure rather than capability. The published privacy policy covers the marketing site, not the hosted product, so the rules that apply to customer media live in agreements nobody can read before signing. No subprocessor list, no security certification, no documented position on training with customer data, and no data processing agreement is published, even though the platform is marketed for CCTV, bodycam and evidence workflows where those questions decide the purchase. The publisher itself is lightly documented: no company page, no named team, no social presence, and a domain registered in 2024. The sensible route is the one the site suggests: validate one workflow on the free plan, confirm retrieval quality on your own footage, then put the governance questions to the sales team in writing before any archive-scale commitment.
- Choosing a selection results in a full page refresh.
- Opens in a new window.