Best Twelve Labs alternatives

What a Twelve Labs alternative has to replace

Twelve Labs finds moments in videos and answers questions about what happens. Official docs split Jockey (agent-style questions with cited moments, via Claude, ChatGPT, or the Agents API) from the Models API (Marengo search embeddings and Pegasus generated text). Videos are assets in their index. Core models are cloud-hosted; you do not self-host Marengo or Pegasus. Docs: https://docs.twelvelabs.io/docs/get-started/introduction

An alternative is either another hosted video API, or a pipeline that samples frames (or segments), embeds them, and keeps the clip in a store you query.

How to choose

Separate the video-native model from the catalog that holds the clip.

• Keep Twelve Labs when Marengo search, Pegasus generation, or Jockey over a knowledge store is the product.
• Pick Mixpeek when you want a managed retrieval API over a bucket you already have (scenes, speech, on-screen text).
• Pick a general multimodal model API (for example Gemini) when video is one input among text and images, not a dedicated video index.
• Pick CLIP (or another embedding) on sampled frames when you own the stills, the timestamps, and the HTTP route.
• Call Marengo as a column on segments you own when you want the hosted embedding without giving up the catalog.

The tools

Mixpeek is a managed multimodal retrieval API: it reads video, images, audio, and documents in your bucket and searches scenes, speech, and on-screen text over HTTP. It is not a SQL database you operate. Site: https://www.mixpeek.com/ and docs: https://docs.mixpeek.com/

Google Gemini documents native video understanding and Gemini Embedding 2 for text, images, video, audio, and PDF. That is a model API, not a video catalog. Docs: https://ai.google.dev/gemini-api/docs/embeddings

Open CLIP-family models embed frames you sample. Search is nearest-neighbor over those stills. You run ffmpeg or an iterator, store the frames, and serve HTTP. The clip stays in a table or a bucket you own.

Twelve Labs remains on this list because Jockey versus Models is a real fork: skip the Models API if you want cited moments from an assistant, and skip Jockey if you want to call Marengo or Pegasus yourself.

Last reviewed

Facts on this page were checked against vendor documentation on 2026-10-28. Product surfaces move; treat version names and APIs as directional and follow the official links in Related.

Twelve Labs alternatives compared (last reviewed: Oct 2026)

Where Pixeltable fits

Use Pixeltable when the clip should stay in a Video column you query, version, and attach other columns to. First-party twelvelabs.embed.using(model_name='marengo3.0') on a video_splitter view (minimum 4 second segments) is the documented path to Marengo without giving up the catalog. Cross-modal similarity(string=), similarity(image=), similarity(audio=), and similarity(video=) are supported on that index. Use CLIP on a frame_iterator view when you do not want a hosted video encoder. Keep Twelve Labs when the hosted model or Jockey is the product. Keep Mixpeek when a managed retriever over a bucket is the product. Pixeltable vs Mixpeek covers the other hosted retriever.

import pixeltable as pxt
from pixeltable.functions.video import video_splitter
from pixeltable.functions import twelvelabs
from pixeltable.serving import FastAPIRouter

TableModel = pxt.model_base()
embed = twelvelabs.embed.using(model_name='marengo3.0')

class Videos(TableModel, name='videos'):
    video: pxt.Video
    title: pxt.String

class Segments(
    TableModel,
    name='segments',
    base=Videos,
    iterator=video_splitter(
        video=Videos.video, duration=5.0, min_segment_duration=4.0
    ),
):
    __indexes__ = [pxt.EmbeddingIndex(video_segment, embedding=embed)]

@pxt.query
def search_clips(query: str, limit: int = 5):
    sim = Segments.video_segment.similarity(string=query)
    return (
        Segments.order_by(sim, asc=False)
        .limit(limit)
        .select(Segments.video_segment, Segments.title, sim)
    )

api = FastAPIRouter(name='clips')
api.add_insert_route(Videos, path='/videos', inputs=[Videos.video, Videos.title])
api.add_query_route(path='/search', query=search_clips)

Questions