The multimodal data warehouse

    Turn any data
    into answers.

    Turn any video, image, audio or document into answers your apps and agents can query. Files stay in your bucket. What's inside them becomes rows you can search, analyze and discover.

    Index once. Query every way. Or try a retriever, no key

    10 videos indexed
    00:12.4
    Try it now · no account, no key

    See it work on data we've already indexed.

    Each demo app runs on a marketplace retriever. Open the app, or run the same pipeline on your own files.

    Files tables

    Every file becomes a table you can query.

    Mixpeek reads each file where it lives and writes what's inside it as columns. Every column names the model that produced it, so you can see exactly what you're querying.

    Browse models
    s3://media/**/*.mp4 collection scenes
    ColumnProduced by
    Try it on a video file
    “Every scene in our ads where a known public figure appears”
    Your files
    Video · ad scenes
    collection ad_scenes
    00:01.2
    00:04.1
    00:07.8
    00:11.5
    Marketplace
    Image · public figures
    collection public_figures
    32,667 reference faces
    ⋈ Joinedpromo_cut_b.mp4 · 00:04.1 · scene + public figure, one row
    then: analyze by campaign
    spring_launch14
    holiday_promo9
    creator_series4
    Illustrative query and frames
    Joins + analytics

    Join your files with ours. Then analyze.

    Line up what's seen, said and shown at the same moment, across your own files and pre-indexed marketplace datasets, then analyze the results like any warehouse table.

    For creative strategists

    Find what makes your best ads work.

    Point Mixpeek at your past ads and their results. See which hooks show up in your winners.

    Hook patternswinnerslibrary
    • Hands-on close-up in first 2s
    • Face to camera + price on screen
    • Reveal, then reaction
    • Product on white, voiceover only
    Example
    Start from a template

    Pick a job. Deploy it in one click.

    A template is models, data and a retrieval strategy put together for one job, in one manifest. Deploy it to Studio, then point it at your own bucket.

    See all templates
    A camera operator silhouetted against a lit setScene · 0.91Camera · 0.89Video · footage.webp

    Find the take in an archive nobody has watched end to end.

    Models
    Vertex AI multimodalembedding@001
    Data
    footage-activeadseditor-assets
    Retrieval
    feature_searchllm_filter
    1.27M
    scenes in the reference corpus
    5 → 3
    sources into clean collections
    How Mixpeek retrieves

    Describe it once. It keeps getting better.

    Mixpeek is a composable, self-optimizing retrieval engine. Developers and agents describe what they want to retrieve, and the system assembles the optimal architecture from models, datasets and retrieval strategies, shifting retrieval from manually engineered pipelines to declarative, autonomous infrastructure.

    01Describe it

    Say what you want to retrieve. Mixpeek assembles the architecture.

    1You say

    “Find every scene in our ads where a known celebrity appears.”

    2Mixpeek picks
    Models
    SAM3 · Gemini embeddings
    Dataset
    Celebrity faces · marketplace
    Strategy
    Multi-vector face join
    3You get
    namespacecollectionsretrieverready to query
    Start from a template today
    02It adapts

    As your queries change, the engine re-tunes itself.

    1Your queries shift
    Week 1
    Week 8
    find the sceneanalyze by brand
    2The engine adjusts
    rerank on every queryfilter on brand_mark first
    top_k 200top_k 50
    3You keep
    Accuracy
    held at target
    Latency
    ↓ lower
    Cost
    ↓ lower

    You set the targets for accuracy, latency and cost. Every signal and stage lives in your collections, so anything the engine changes, you can see and override.

    How retrieval is optimized

    From manually engineered pipelines to declarative, autonomous retrieval infrastructure, built for agents as the main querier.

    For developers

    Every model, open to inspect.

    See what each model does, what it reads and writes, how it runs, and whether we've measured it on real data. Agents can read the whole catalog as JSON.

    Browse models
    ModelTaskIn → outRuns as
    Gemini Embedding 2embeddingtext·image·video → vectorProvider API
    SigLIP Base patch16 224embeddingimage·text → vectorServed on Mixpeek
    Multilingual E5 Large Instructembeddingtext → vectorServed on Mixpeek
    Vertex multimodalembedding@001embeddingimage·video·text → vectorProvider API
    Gemini 2.5 Flashvideo / image Q&Avideo·image → textProvider API
    The full Mixpeek catalog · filter by task, modality, license, hardware
    Ways in

    Choose how much you want Mixpeek to handle.

    $ curl -fsSL https://mixpeek.com/install | sh

    # then, in Claude Code, Codex, Gemini CLI or Cursor: “Deploy the Creative DNA template on s3://ads/ and find our strongest hooks”

    Or add it to Claude Code by hand
    $ claude mcp add --scope user --transport http mixpeek https://mcp.mixpeek.com/mcp --header "Authorization: Bearer YOUR_API_KEY"
    The category

    Why a warehouse, not another pipeline.

    What is a multimodal data warehouse?
    What you bring
    Who does the understanding
    Query across modalities
    Shared datasets
    Built for
    Assembled stack
    Separate tools and the glue between them
    Transcription, frame sampling, embeddings, a vector DB, glue code
    You, per vendor
    Custom code
    None
    One workflow at a time
    Multimodal lakehouse
    Open storage, your own pipelines
    Your own pipelines and models on open storage
    You
    SQL + vector search over what you've built
    Bring your own
    ML teams training models
    Multimodal data warehouse
    Mixpeek
    Files and what you want from them
    Mixpeek, with models you can inspect
    Search, joins and analytics built in
    Marketplace datasets, joinable
    Apps and agents asking questions
    Your bucket is the source

    No migration. Point it at where your files already live.

    Reads S3, GCS, R2, Azure or any S3-compatible bucket. Your storage stays the system of record. Run it in our cloud or yours.

    S3GCSR2AzureBYO-Cloud
    Founded by engineers from
    MongoDBBerkeleyNVIDIAEtsyAmazon Web ServicesEquinixIAB Tech Lab
    Enterprise-ready
    SOC 2-ready Encrypted at rest BYO-Cloud Self-hosted option Audit trails SSO / SAML
    Pricing

    Free to try. From $25/mo on your own files.

    Marketplace
    Free

    Query hosted datasets with no account or key, up to a rate limit.

    Build
    $25/mo plus usage

    Templates and managed indexing on your own files, or bring your own vectors.

    Enterprise
    Custom
    From $10,000/mo

    Dedicated infrastructure, your own models, self-hosting, SSO and an SLA.

    What is a multimodal data warehouse?

    A system that turns files into data you can query. It reads video, images, audio and documents where they live, writes what's inside them as durable fields, and serves search, joins and analytics across every modality to apps and agents.

    How is it different from a lakehouse or a vector database?

    A lakehouse stores multimodal data for teams that build their own pipelines. A vector database stores embeddings you already made. A warehouse does the understanding for you and answers questions across all of it.

    Do I have to move my data?

    No. Mixpeek reads from your existing S3, GCS, R2, Azure or S3-compatible bucket, and your storage stays the system of record.

    Where should I start?

    Try a marketplace retriever with no key, then deploy the template closest to your use case on your own files. Developers can start from the model catalog.

    Start from a template that works.

    Deploy a working use case in one click, see what it finds, then point it at your own bucket.