🌐 English में देखें
T
⚡ फ्रीमियम
🇮🇳 हिंदी
Twelve Labs
Twelve Labs पर जाएं
twelvelabs.io
Twelve Labs क्या है?
Twelve Labs is a video intelligence platform that uses multimodal AI to make video content searchable, analyzable, and actionable through natural language queries. Rather than relying on manually assigned tags or transcripts, the platform processes the full visual, audio, and speech content of a video simultaneously to generate rich spatiotemporal embeddings.
Teams managing large video archives face a real bottleneck: manually reviewing footage to find specific clips, generate summaries, or classify content is time-consuming at scale. Twelve Labs addresses this with two core models. Marengo, its multimodal embedding model, achieves 78.5% composite accuracy across 47 languages and outperforms Google's VideoPrism-G on multiple retrieval benchmarks. Pegasus, its video language model, reasons continuously over full temporal arcs up to two hours, tracking entities and narrative causation rather than sampling isolated frames.
Developers can connect to the Twelve Labs API using REST calls or the Python SDK — indexing video costs $0.042 per minute under the Pegasus 1.2 plan, with a Free tier providing 600 indexing minutes to start. The platform integrates with ApertureDB and Pinecone for downstream vector search workflows. It is not the right choice for teams seeking a no-code editing suite or consumer-facing video production features; the platform is built for engineering teams building video-intelligence applications on top of existing infrastructure.
Teams managing large video archives face a real bottleneck: manually reviewing footage to find specific clips, generate summaries, or classify content is time-consuming at scale. Twelve Labs addresses this with two core models. Marengo, its multimodal embedding model, achieves 78.5% composite accuracy across 47 languages and outperforms Google's VideoPrism-G on multiple retrieval benchmarks. Pegasus, its video language model, reasons continuously over full temporal arcs up to two hours, tracking entities and narrative causation rather than sampling isolated frames.
Developers can connect to the Twelve Labs API using REST calls or the Python SDK — indexing video costs $0.042 per minute under the Pegasus 1.2 plan, with a Free tier providing 600 indexing minutes to start. The platform integrates with ApertureDB and Pinecone for downstream vector search workflows. It is not the right choice for teams seeking a no-code editing suite or consumer-facing video production features; the platform is built for engineering teams building video-intelligence applications on top of existing infrastructure.
संक्षेप में
Twelve Labs is an AI Tool that converts video libraries into searchable, structured data using proprietary multimodal models — Marengo for embedding-based retrieval and Pegasus for full-video temporal reasoning. The platform's REST API supports use cases from sports highlight extraction to brand-safe contextual ad placement, all without manual tagging. Its Free tier allows up to 600 minutes of indexed video, making it accessible for initial proof-of-concept builds before committing to pay-as-you-go pricing.
मुख्य विशेषताएं
Natural Language Search
Twelve Labs' Marengo model converts every video frame, audio channel, and ASR transcript into unified embeddings, enabling queries like 'find the moment the speaker mentions pricing' to return accurate, timestamped results across libraries of any size — no manual tagging required.
Content Generation
Pegasus generates contextually accurate text summaries, chapter titles, Q&A pairs, and structured metadata from full-length videos up to two hours. Output is grounded in the actual temporal narrative rather than isolated scene snapshots, making summaries usable for editorial and SEO workflows.
Video Classification
Automated classification assigns videos and clips to predefined or custom taxonomies in seconds. Sports broadcasters use this to tag play types; media archives use it to sort by topic, tone, and speaker — reducing manual review time from days to minutes.
Scalability
The platform is architected to handle petabyte-scale video libraries deployed on cloud, private cloud, or on-premise environments. Infrastructure pricing scales with indexed video duration rather than seat count, which keeps costs proportional as catalogs grow.
Customization
Organizations can fine-tune Twelve Labs models on domain-specific datasets — a legal firm indexing deposition footage trains a different model profile than a fitness platform tagging exercise categories. Model fine-tuning is available via a custom enterprise engagement.
State-of-the-Art Models
Marengo 2.7 sets benchmarks in zero-shot text-to-video retrieval, surpassing the previous SOTA image foundation model in cross-modal retrieval tasks on the MSR-VTT and ActivityNet datasets. Pegasus 1.2 adds infrastructure pricing at $0.0015 per minute for embedding-level services.
फायदे और नुकसान
✅ फायदे
- Time-Saving — Scene-level retrieval that previously required a team of manual reviewers can be completed via a single API query. Media archives report tasks that took three days now resolving in seconds, freeing editorial staff for higher-value production work.
- High Accuracy — Marengo achieves 78.5% composite accuracy across 47 languages and outperforms Google's VideoPrism-G by 10% on the MSR-VTT benchmark. This level of retrieval precision makes the platform viable for production workflows where false positives carry real editorial cost.
- User-Friendly — The Twelve Labs Playground lets non-technical users test queries against uploaded video without writing any code. The Python SDK and comprehensive API documentation reduce the integration burden for engineering teams building on top of the platform.
- Privacy and Security — Deployment options include private cloud and on-premise environments, giving enterprises full control over where video data is processed and stored. This makes the platform viable for legal, healthcare, and government use cases with strict data residency requirements.
❌ नुकसान
- Learning Curve — Getting meaningful results from Twelve Labs requires familiarity with REST APIs, vector indexing concepts, and the distinction between Marengo and Pegasus use cases. Non-technical teams cannot use the platform productively without developer support during initial integration.
- Customization Requirements — Fine-tuning models for domain-specific performance — such as training on legal deposition vocabulary or specialized sports terminology — requires direct engagement with Twelve Labs' enterprise team. There is no self-serve model fine-tuning interface available on standard plans.
- Pricing Transparency — The Free tier provides 600 indexing minutes, but production-scale pricing under Pegasus 1.2 involves multiple per-minute cost components — video indexing, API input, output tokens, and infrastructure — which require careful usage modeling before committing to a paid deployment.
विशेषज्ञ की राय
For engineering teams at media companies or advertising platforms building video-search or content-automation applications, Twelve Labs delivers measurable retrieval accuracy that manual metadata workflows cannot replicate at scale. The primary limitation is that it requires developer resources to integrate — teams without API experience will need onboarding time before seeing production results.
अक्सर पूछे जाने वाले सवाल
Yes. New accounts are automatically placed on the Free tier, which includes 600 minutes of video indexing at no cost. This allowance accumulates and does not reset when videos are deleted, so it functions as a one-time trial budget rather than a recurring monthly credit.
Marengo is a multimodal embedding model that indexes video content into searchable vector representations, enabling fast retrieval across speech, visual, and audio channels. Pegasus is a video language model that reasons over the full temporal arc of a video — up to two hours — to generate summaries, Q&A answers, and structured narratives. Most production workflows use both models together.
No. Twelve Labs is an API-first platform designed for software engineers building video-intelligence applications. Teams without developer resources will not be able to access its core features. Non-technical users can explore the Playground interface but cannot run production workflows without API integration.
Manual tagging requires human reviewers to watch footage and assign metadata — a process that can take three or more days for large archives. Twelve Labs API queries return timestamped, semantically accurate results in seconds. The trade-off is an upfront integration cost that manual workflows do not require.