Mixpeek
Multimodal video search API: find any scene by description in your object storage.
Mixpeek is a solid choice for teams that need to search video and documents by meaning, not just keywords. Its managed extraction and cross-modal joins save you from building your own pipeline, and the $25/month entry point is reasonable. But if you only need basic vector search on text, a simpler vector DB like Pinecone will be cheaper and easier to manage.
Verified 4d ago · liveness 89/100 · cite: rightaichoice.com/tools/mixpeek
- Advertising agencies needing talent search and brand safety across video libraries
- Entertainment companies requiring scene search and archive access
- E-commerce teams for visual search and product discovery page enrichment
- Educational institutions for lecture search and transcript Q&A
- Pure text or tabular data search – simpler vector DBs are more cost-effective
- Teams needing full control over feature extraction pipelines
- Organizations without object storage infrastructure (S3/GCS/R2)
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Skip Mixpeek if you only need basic vector search on text documents — a simpler vector DB like Pinecone will be cheaper and easier to manage.
The Multimodal Extractor charges $0.05 per minute of video, so indexing a large archive can rack up a hefty one-time bill.
Mixpeek's pricing fits teams with video-heavy libraries who need multimodal search without building their own pipeline. The $25/mo entry point is competitive against managed alternatives like Twelve Labs, but cheaper options like Pinecone start ~$70/mo for pure vector search — though you'd need to add perception separately.
In short
Mixpeek — Multimodal video search API: find any scene by description in your object storage. Best for Advertising agencies needing talent search and brand safety across video libraries, Entertainment companies requiring scene search and archive access, E-commerce teams for visual search and product discovery page enrichment. Free to start; paid plans from $25/mo.
What's new in Mixpeek
Checked 4 days agoAcross the latest 2 updates: 1 feature update and 1 changelog entry.
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 naming the field and suggesting alternatives.
Moment grouping collapsed whole video when time field was in seconds
Fixed bug where moment_group on a seconds-based time field collapsed entire videos into one result; now respects the correct 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.
- +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: September 2026
How we score →Key Features
- Multimodal search across video, images, audio, documents
- Auto-extract scenes, faces, OCR, transcripts, embeddings
- Multi-stage retrieval pipelines: filter, join, rerank in <100ms
- Managed Vector Store (MVS): dense, sparse, BM25 search
- Multimodal Extractor v2 with Gemini Embedding 2 (3072D)
- Face & person search across video libraries
- IP & copyright detection for logos, songs, faces
- Brand & ad safety pre-publish screening
- Speaker diarization aligned to transcript timeline
- Layout extraction from PDFs (header, body, charts, signature)
- Plugin Marketplace for custom extractors
- Cluster visualization with color-by-field, similarity slider
- Cross-modal joins at same object and timestamp
- Bring your own vectors (BYO) with vector store
- Evaluation scoring API (precision, recall, F1, F2)
About Mixpeek
Mixpeek is a multimodal retrieval platform that indexes video, images, audio, and documents directly from your object storage (S3, GCS, R2) and makes them searchable by natural language. Built for developers and data teams, it automatically extracts scenes, faces, OCR, transcripts, and embeddings at upload time, so you never have to build a custom perception pipeline. You can then compose multi-stage search pipelines in under 100ms — filter, join, and rerank across modalities — and query everything through a single API. The platform offers two main paths: a managed indexing tier where Mixpeek handles extraction and embedding of raw files, and a standalone Vector Store (MVS) for teams that already have their own vectors. MVS supports dense, sparse, and BM25 search directly on object storage, with pricing starting at $25/month. Both paths operate on a usage-based model: $0.023 per GB-month of vector storage and $2 per million queries, with a $25/month minimum. Recent updates include the new evaluation scoring endpoint (POST /v1/evaluations/score) that computes precision, recall, F1, and F2 without a retriever, and Studio enhancements that render geospatial features as maps instead of raw JSON. Typed extractors produce structured features — faces, speakers, layouts, captions — that let you join across modalities at the same timestamp. For example, you can search for 'find when our CEO said guidance while the slide read Q4 outlook,' combining transcript and OCR output. Compared to plain vector databases like Pinecone or Weaviate, Mixpeek adds a perception layer out of the box, making it especially strong for video-heavy use cases such as talent search, brand safety, and scene retrieval. It also integrates with DAM systems like iconik and Mux, and supports source adapters for webhooks, email, and Supabase. If your data lives in object storage and you need scene-level search without the glue code, Mixpeek is worth a close look.
Behind the Verdict
Mixpeek stands out because it handles the messy part of multimodal search: turning raw video and documents into semantic features you can query. Instead of stitching together separate services for transcription, embedding, face detection, and object recognition, you point Mixpeek at a bucket and it does the extraction automatically. The typed extractors produce structured features like faces, speakers, layouts, and captions, which enables cross-modal joins at the same timestamp. That capability is the core reason video-heavy teams should consider it. You can search for a moment where a person said something while a specific slide was on screen — something a plain vector database can't do. The managed path suits teams without the engineering bandwidth to build their own perception pipeline; the MVS path suits teams that already have embeddings and just need a vector store that lives on object storage. On the downside, the free tier is limited — 1 GB storage and 1K credits — so you'll likely need to pay for any real project. The usage-based pricing includes an indexing bill that can surprise you: the Multimodal Extractor costs $0.05 per minute of video, which adds up on large libraries. Also, there's no sub-10ms latency guarantee; the platform optimizes for multi-stage retrieval in under 100ms. If all you need is simple text search, a generic vector DB like Pinecone is more cost-effective. Mixpeek's real value shows up when you need scene-level search across video libraries — think talent search, brand safety, or archive access.
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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.
Indexing a library of past ad creatives to find scenes matching a client's new brief.
Outcome: Connect an S3 bucket, configure the face and scene extractors, and within hours the team can search by description like 'woman smiling with product' and get timestamped, ranked clips.
Using Mixpeek's vector store to rank scenes by visual similarity.
Outcome: Upload pre-computed embeddings, run dense+BM25 hybrid search, and integrate the retriever as a LangChain tool — reaching a working recommendation endpoint in under a day.
Screening user-uploaded videos for copyrighted logos or audio before publication.
Outcome: Set up a collection with IP detection extractors, process uploads automatically, and get flagged content reports in near-real-time, avoiding takedown risks.
Use Cases
- Search for a specific face across thousands of hours of video in milliseconds.
- Detect copyrighted logos or songs in user-generated content before publication.
- Build a visual recommendation system that ranks scenes by similarity using reinforcement learning.
- Automatically transcribe and index lecture videos for natural language Q&A.
- Screen ad creatives against brand safety blacklists at bid-time speeds.
- Find the exact frame in a video where a person says a specific phrase.
- Auto-label datasets using taxonomy-driven classification and feedback loops.
- Reverse image & video search using an image or clip as the query.
Models Under the Hood
as of 2026-08-30
Limitations
- The free tier is limited to 1 GB storage and 1K credits/month, insufficient for moderate-scale projects.
- Pricing scales with storage and extractor usage — the Multimodal Extractor costs $0.05/min of video, which can become expensive for large libraries.
- Real-time streaming ingestion is not advertised.
- There's no sub-10ms latency guarantee for simple vector search; the platform is optimized for multi-stage retrieval in under 100ms.
- You must bring your own object storage (S3/GCS/R2) to use Mixpeek.
as of 2026-08-30
Verification history
We have re-verified Mixpeek 17 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-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
- — 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
Showing the 6 most recent of 17 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.
Free
$0/mo
Ideal for
Solo developers evaluating Mixpeek with small datasets (up to 1 GB storage) and limited extraction needs.
What this tier adds
Free entry point with limited objects and basic extractors; no commitment needed to test the API.
Build
$25/mo minimum
Ideal for
Startups and small teams processing up to 100K objects/month who need a mix of basic and advanced extractors plus webhooks and RBAC.
What this tier adds
Adds 25 collections, 5 namespaces, batch processing, and email support over Free; starts at $25/month minimum.
Scale
$250/mo minimum
Ideal for
Growing teams with up to 1M objects/month requiring all extractors, 200 collections, and 25 namespaces at a higher volume.
What this tier adds
Raises the object limit to 1M/month, expands collections to 200 and namespaces to 25, and unlocks all extractors vs. Build's basic set.
Enterprise
Custom
Ideal for
Large organizations with compliance requirements needing dedicated deployment, SSO, audit logs, and custom extractors.
What this tier adds
Adds unlimited collections/namespaces, dedicated support, and advanced security features beyond Scale.
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 pricing fits teams with video-heavy libraries who need multimodal search without building their own pipeline. The $25/mo entry point is competitive against managed alternatives like Twelve Labs, but cheaper options like Pinecone start ~$70/mo for pure vector search — though you'd need to add perception separately.
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 the managed path, you can connect a bucket and run your first semantic search within minutes — the quickstart promises under 10 minutes. For the MVS path, you can upsert vectors and query in about 60 seconds. But full production setup, including tuning extractors and building multi-stage pipelines, typically takes a few days.
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: Export your vectors in a compatible format (e.g., JSON or Parquet) and upsert them into Mixpeek's MVS — it supports dense, sparse, and BM25, so you can maintain search quality while moving.
- →From a custom pipeline (e.g., separate Whisper + CLIP + OCR): Point Mixpeek's managed indexing at your raw files and it replaces the whole extraction stack, so you can retire your glue code.
- ↗To Pinecone: Export your vectors and metadata from MVS and bulk-import them into a Pinecone index — the API is standard, so this is straightforward.
- ↗To Weaviate: Use their bulk import tools to load your vectors and metadata, then recreate your retriever logic as Weaviate queries.
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
Official links
Tools that pair well with Mixpeek
Common stack mates teams adopt alongside Mixpeek, with the specific reason each pairing earns its keep.
Alternatives to Mixpeek
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