Mixpeek
Multimodal retrieval that indexes video, image, audio and PDF files straight from your S3, GCS or R2 bucket.
If your media already sits in a bucket and someone is manually scrubbing footage to find a scene, Mixpeek removes that job — the cross-modal joins that resolve to one object and timestamp are the part most teams end up writing themselves. The Build minimum is easy to trial at $25/month with $10 of processing included, but watch the metered indexing line once you push a real archive through it. Text-only search workloads should stay on a cheaper vector database.
Verified 14h ago · liveness 89/100 · cite: rightaichoice.com/tools/mixpeek
- Advertising and media teams running talent search, brand suitability and creative analytics over large video libraries
- Entertainment and archive teams needing timestamped scene search, recommendations and archive access
- Developers composing multimodal retrieval pipelines with cross-modal joins at the same object and time
- E-commerce teams doing visual search, product page enrichment and catalog QA
- Text-only or tabular search workloads where a plain vector database is cheaper and simpler
- Teams that want to hand-tune their own feature extraction pipelines end to end
- Organizations without S3, GCS or R2 object storage already in place
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Skip Mixpeek if you need flat, predictable monthly pricing with no metered indexing component, or if your media doesn't live in S3, GCS, or R2 — the platform extracts from your bucket, and object storage is a prerequisite, not an option.
The $25/month Build minimum includes up to 100K objects per month, and everything past that meters per unit of content processed — a large back catalogue produces a much larger first invoice than the minimum suggests.
Mixpeek's Build plan starts at a $25/month minimum with up to 100K objects/month included, then meters per unit of content processed — cheap to start, open-ended at scale. The standalone vector-store path is dominated by $1.56 per million vectors per month plus $0.023 per GB-month and $2 per million queries. A plain vector database like Pinecone or Weaviate is cheaper for text-only lookups because you're not paying for extraction; Twelve Labs is the closer comparison for video-native search.
In short
Mixpeek — Multimodal retrieval that indexes video, image, audio and PDF files straight from your S3, GCS or R2 bucket. Best for Advertising and media teams running talent search, brand suitability and creative analytics over large video libraries, Entertainment and archive teams needing timestamped scene search, recommendations and archive access, Developers composing multimodal retrieval pipelines with cross-modal joins at the same object and time. Plans from $1.56/mo.
No head-to-head comparison for Mixpeek yet — the alternatives below are the closest matches we list.
What's new in Mixpeek
Checked 7 days agoAcross the latest 2 updates: 2 changelog entries.
Range filter on text field now rejected when saving retriever
Creating or updating a retriever with a range filter on a text-indexed field now returns a 422 error that names the field and suggests alternatives, replacing silent acceptance of an invalid filter.
Moment grouping collapsed whole video when time field was in seconds
A bug where moment_group on a seconds-based time field collapsed entire videos into a single result is fixed; grouping now respects the correct time unit.
What people actually say about Mixpeek — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
34 mentions across 3 sources (Hacker News, Product Hunt, Lemmy) · researched Aug 18, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Unified API for video, images, audio, and document search
- +Auto-extracts scenes, faces, OCR, transcripts, and embeddings
- +Multi-stage retrieval pipelines under 100ms
- +Managed Vector Store supports dense, sparse, and BM25
- +Gemini Embedding 2 with 3072D embeddings for state-of-the-art retrieval
- −Limited independent user reviews; most feedback is from founder
- −Potential vendor lock-in with managed indexing
- −Missing details on free tier limits and data retention
- −Requires intermediate developer skills to set up pipelines
- −No self-hosted option, which may worry security-conscious teams
- • Potential overage charges for video processing if not monitored
- • Estimated pricing page placeholders may hide actual costs for high-volume use
Viability Score
How well maintained and how widely used is Mixpeek? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Search video, images, audio and documents in one multimodal retrieval layer
- Auto-extract scenes, faces, OCR, transcripts and embeddings from a connected bucket
- Compose multi-stage retrievers in JSON: filter, join, rerank in under 100ms
- Standalone Mixpeek Vector Store (MVS) with dense, sparse and BM25 search on object storage
- Bring-your-own-vectors path for teams that already generated embeddings
- Generate and store embeddings from 50+ HuggingFace models
- Face and person search across video libraries in milliseconds
- Speaker diarization aligned to the transcript and the timeline
- PDF layout extraction into typed regions: header, chart, body, signature
- Cross-modal joins resolving to the same object and timestamp
- IP and copyright detection across logos, songs and faces
- Brand and ad safety screening before publish at bid-time speeds
- Clusters and taxonomy-driven classification for auto-labeling datasets
- Moment grouping on a time field to roll results into grouped moments
- Range filters with 422 validation errors that name the offending field
About Mixpeek
Mixpeek is a multimodal retrieval platform for teams whose video, image, audio and document files already live in object storage. Point it at S3, GCS or Cloudflare R2 and it reads each file for what's inside: faces and objects in a frame, scene captions, document layout, who is speaking in a recording. Everything it finds becomes a typed, searchable field your software or your agent can filter, rank and join. The extracted features are concrete. Video yields face embeddings, visual embeddings, scene captions, Whisper transcripts and keyframes; images yield visual embeddings, faces and OCR; audio yields speaker-diarized transcripts aligned to the timeline; PDFs yield typed layout regions (header, chart, body, signature) plus text chunks. One video can fan out into hundreds of searchable records, which is why moment-level search feels different from throwing filenames into a search box. On top of that layer you compose retrievers in JSON — vector search, face matching, filters, joins and cross-encoder reranking chained into a single endpoint. Vendor examples show multi-stage queries returning in the 27-41ms range, with cross-modal joins landing the same object at the same timestamp. Recent changelog work tightened the edges: range filters on text-indexed fields now return a 422 naming the offending field instead of silently saving, and moment grouping respects the correct time unit instead of collapsing a whole video into one result. Two billing paths reflect two kinds of buyer. The Standalone Mixpeek Vector Store (MVS) runs dense, sparse and BM25 search over vectors you already generated, dominated by $1.56 per million vectors per month. Managed Indexing handles extraction, embedding and indexing for raw files, starting at the Build plan's $25/month with $10 of processing included and up to 100K objects/month, then metered per unit. Single tenant exists for teams needing their own weights, a specific region or a compliance posture. Against a plain vector database
Behind the Verdict
Where Mixpeek earns its keep is the gap between "we have files in a bucket" and "our app can answer a question about a frame at 00:04:12." Building that yourself means stitching Whisper, a face model, an OCR pass and a layout parser into something that agrees on timestamps. Mixpeek ships that as one indexing pass and exposes the output as fields you query. Pick it when your corpus is genuinely mixed media and moment-level precision matters: talent search across a reel library, brand and ad safety screening before publish on IAB 3.1-style classification, archive access, lecture search, catalog QA. The retriever-in-JSON model suits engineering teams that want a callable endpoint rather than a UI — filter, join and rerank stages without standing up their own orchestration. Pass when the content is text or tabular. A plain vector database is cheaper and simpler, and Mixpeek's extraction pipeline is billing you for perception you don't need. Also pass if a vendor's flat monthly invoice is a hard requirement — Managed Indexing is metered per unit of content processed, and a large first ingest is the line that surprises people. The two-path pricing is the sharpest thing about the commercial model. If you already generated embeddings, MVS keeps you on object storage and the dominant term is $1.56 per million vectors per month. If you're sending raw files, the one-time indexing bill dominates, with Build at $25/month including $10 of processing and up to 100K objects/month. Run the arithmetic on your archive size before you commit either way. Closest alternative depends on what you're missing. Against Twelve Labs, the pitch is breadth across audio and PDFs plus joinable retrievers, not just video understanding. Against Pinecone or Weaviate, it's the perception layer those
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Real-world workflow fit
Concrete scenarios for the personas Mixpeek actually fits — and what changes day-one when you adopt it.
Point Mixpeek at the S3 bucket holding decades of footage, let Managed Indexing run Whisper transcripts, ArcFace faces, and scene descriptions, then build a retriever that chains transcript search with a face filter.
Outcome: Editors query 'the shot where she mentions the merger' and get ranked timestamped clips in under 100ms instead of scrubbing proxies by hand.
Wire Mixpeek in as an MCP server so the moderation agent can call a retriever that runs logo, song, and face detection against a brand safety blacklist before an ad goes live.
Outcome: Pre-publish screening runs at bid-time speeds and returns a single report per creative instead of three separate detection pipelines.
Skip extraction entirely and use the standalone Mixpeek Vector Store — upsert existing dense vectors into a collection on the team's own bucket and query with dense, sparse, and BM25 search.
Outcome: Search runs on infrastructure the team already controls, billed on $0.023 per GB-month and $2 per million queries, with no perception pipeline to maintain.
Use Cases
- Find a specific face across thousands of hours of video in milliseconds using ArcFace face embeddings.
- Detect copyrighted logos or songs in user-generated content before publication with one pipeline and one report.
- Build a visual recommendation system that ranks scenes by similarity using reinforcement learning.
- Automatically transcribe and index lecture videos with Whisper for natural language Q&A.
- Screen ad creatives against brand safety blacklists at bid-time speeds.
- Find the exact frame where a person says a specific phrase via transcript embedding search.
- Auto-label datasets using taxonomy-driven classification and feedback loops.
- Reverse image and video search using an image or clip as the query against visual embeddings.
Models Under the Hood
as of 2026-10-15
Limitations
- Mixpeek turns files in your own object storage (S3, GCS, or R2) into timestamped, searchable scenes, so it operates on buckets you connect rather than on ingested uploads.
- Two paths exist — the standalone Mixpeek Vector Store (MVS) for bring-your-own vectors, and Managed Indexing that auto-extracts scenes, faces, OCR, transcripts, and embeddings — and the configuration surface (collections, namespaces, extractors, retriever JSON) is yours to manage.
- Retrievers are composed in JSON and invalid definitions are rejected rather than silently coerced, e.g. a range filter on a text-indexed field returns a 422 naming the offending field.
- Multi-stage retrieval is advertised at under 100ms, not a sub-10ms guarantee.
as of 2026-10-01
Verification history
We have re-verified Mixpeek 20 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Mixpeek tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Standalone (BYO Vectors / MVS)
$1.56 per million vectors/mo
Ideal for
Data and ML teams that already generated embeddings and want dense, sparse, and BM25 search running on their own object storage without paying for extraction.
What this tier adds
Separate path from Managed: you bring vectors and pay storage and queries, dominated by $1.56 per million vectors per month, plus $0.023 per GB-month and $2 per million queries. No extraction pipeline involved.
Build (Managed)
$25/mo plus usage
Single Tenant
Custom
Ideal for
Regulated or security-sensitive organizations that need their own model weights, a specific geographic region, or a particular compliance posture.
What this tier adds
Custom pricing: a platform fee plus the compute your deployment uses. Adds dedicated infrastructure, model weight control, region selection, and compliance-oriented deployment over the shared multi-tenant Build plan.
Where the pricing makes sense
The company stage and team size where Mixpeek's pricing actually pencils out — and where peers do it cheaper.
Mixpeek's Build plan starts at a $25/month minimum with up to 100K objects/month included, then meters per unit of content processed — cheap to start, open-ended at scale. The standalone vector-store path is dominated by $1.56 per million vectors per month plus $0.023 per GB-month and $2 per million queries. A plain vector database like Pinecone or Weaviate is cheaper for text-only lookups because you're not paying for extraction; Twelve Labs is the closer comparison for video-native search.
Setup time & first value
How long it actually takes to get something useful out of Mixpeek — broken out by persona, not the marketing-page minute.
For developers with object storage already in place, the docs claim first query in about sixty seconds on the standalone vector-store path and under 10 minutes end-to-end for a first multimodal search via LangChain, MCP, or REST. A full retriever over a production bucket is a day-one exercise up to a first working pipeline. Teams needing single-tenant deployment or custom model weights should
Switching to or from Mixpeek
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Pinecone or Weaviate: move embeddings into the standalone Mixpeek Vector Store on your own bucket and add extraction later for the modalities those tools don't cover.
- →From Twelve Labs: re-index source video through Managed Indexing and reproduce the same moment-search queries as retriever stages.
- →From a homegrown perception pipeline: keep your object storage, replace the extraction and indexing layer with Mixpeek's built-in extractors, and expose the result as a retriever endpoint.
- →From a model migration: the docs describe a re-extract, validate, and cut-over path for switching embedding models with no downtime.
- ↗To a plain vector database: if the workload turns text-only, embeddings can be read back out and loaded into Pinecone or Weaviate, though you lose the perception layer.
- ↗To a self-managed stack: the Customer-Hosted Mixpeek option in the docs keeps the platform but moves the deployment into your environment rather than off it.
Integrations
Resources & Guides
- Documentationmixpeek.com
Introduction
Mixpeek gives AI agents the ability to see, hear, and understand multimodal content
- Tutorialmixpeek.com
Tutorials
Step-by-step guides to build with Mixpeek
- Learnmixpeek.com
Multimodal Data Infrastructure
Multimodal data infrastructure for AI applications.
- Resourcemixpeek.com
Release Notes
Stay up to date with the latest Mixpeek features and performance improvements. Deep links to docs and examples for every release.
- Resourcemixpeek.com
Blog - Insights on Multimodal AI
Multimodal data infrastructure for AI applications.
- Documentationmixpeek.com
Llms
Full product docs from mixpeek.com
Tutorials & Learning
YouTube returned 6 videos for “Mixpeek”, and we withheld 6: 6 could not be judged, because “Mixpeek” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Mixpeek.
Official links
Tools that pair well with Mixpeek
Common stack mates teams adopt alongside Mixpeek, with the specific reason each pairing earns its keep.
Google Cloud Vision AI
Google Cloud Vision AI: computer vision APIs for image labeling, OCR, and video analysis
Pinecone
Pinecone is a managed vector database and knowledge engine for RAG, semantic search, and agent retrieval.
Lumana
Lumana turns existing IP cameras into self-learning AI video security agents powered by the VIA-1 model.
Alternatives to Mixpeek
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Google Cloud Vision AI
Google Cloud Vision AI: computer vision APIs for image labeling, OCR, and video analysis
Frequently Asked Questions
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