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Twelve Labs vs Vivu

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors. Only Twelve Labs has a free tier. Both ship a public API.

Twelve LabsAPIs for video understanding — search, embed and analyze footage with the Marengo and Pegasus modelsVivuTurns a company's video library into a queryable context source AI agents can pull from via API or MCP.

Side by side

FactTwelve LabsVivu
CategoryVideo Analysis & SearchVideo Analysis & Search
Pricing modelFreemiumContact
Public APIYesYes
PlatformsWeb, APIWeb, API
CompanyTwelveLabs, Inc.Vivu Inc.
Launched2021—
Last verifiedSep 6, 2026Sep 1, 2026

What our research says

Twelve Labs

The most direct route to adding real video search and Q&A to a product without training multimodal models yourself, priced by the minute rather than by seat. Costs scale linearly with archive size, so large libraries need budgeting up front, and free-tier indexes expiring after 90 days keeps that tier strictly for prototyping.

Pros

  • Purpose-built video models rather than frame-sampled image models
  • Transparent per-minute metered pricing with a real free tier
  • SDKs, playground and SOC 2 Type II support production use
  • Analyze handles continuous reasoning over long footage

Cons

  • Indexing costs accumulate quickly across thousands of hours
  • Free-tier indexes expire after 90 days
  • It is infrastructure, not a turnkey app — integration takes engineering

Vivu

Vivu addresses a specific infrastructure gap -- making a company's existing video footage queryable by AI agents the same way text documents already are -- rather than building another consumer video tool. It's early enough that there's no self-serve product or public pricing yet, so evaluating it means booking a demo rather than testing it directly.

Pros

  • Team background in video AI at scale (cited prior work reaching 10M+ users, 100M+ hours of video)
  • Offers both a direct API and an MCP server for agent integration
  • Targets a concrete, underserved problem (video as agent context) rather than a crowded consumer category

Cons

  • No public pricing or self-serve access
  • No named customers or case studies visible on the site
  • Early-stage product with limited independent verification of claims

Which one fits

Choose Twelve Labs if you need

  • Developers building search over large video archives
  • Media and sports teams automating highlights and logging
  • Products that need video Q&A or summarization via API

Choose Vivu if you need

  • Companies with large unindexed video libraries wanting to expose them to AI agents
  • Teams already building on MCP who want video as an additional context source
  • Robotics or support teams whose institutional knowledge lives in recorded video

More comparisons

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