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Mixpeekvs
Voxel51
Mixpeek vs Voxel51
A detailed look at how Mixpeek compares to Voxel51.
MixpeekVoxel51You can run this comparison on your own content. Index a sample and put the same query through it.
Search your own files →Key Differentiators
Where Mixpeek Fits Better
- Indexes the bucket you already have: 20 storage connectors, including S3, GCS, Azure Blob, Box, SharePoint, iconik, Mux and RTSP.
- One index for video, audio, images, documents and text, served by API to apps, services and agents.
- Retrievers are multi-stage pipelines (filter, rerank, moment grouping, dedupe, clustering, taxonomy and agentic enrichment) with built-in IR evaluations.
- Human decisions recorded through the Annotations API, linked to the retriever execution and stage that surfaced each document, with version history.
- Exports a curated set to your own bucket as Parquet or WebDataset shards, with lineage back to every source object.
- Usage-based pricing with no seats; also runs customer-hosted on your own Kubernetes.
Where Voxel51 Fits Better
- FiftyOne is open source (Apache-2.0) with a large community of computer vision engineers.
- Labeling tools for 2D, 3D, video and multimodal data, with automated QA and annotator metrics.
- Agentic Labeling: vision-language models draft labels from instructions and examples, then people review them.
- Several sensor streams on one timeline, with segment-level embeddings for moments in long robot or vehicle recordings.
- Dataset versioning, sample edit history and activity logs inside the curation workflow.
- Free tier with no credit card: 3 datasets, 10,000 samples each, 20 GB of hosted storage.
TL;DR: Voxel51 is a workbench where an ML team looks at samples, labels them, curates a training set and evaluates a model, now aimed at physical AI (robotics, autonomous vehicles, sensor data). Mixpeek is retrieval infrastructure: it indexes video, images, audio and documents where they already sit in object storage and serves search, classification and review to production applications and agents. The two can work together: Mixpeek filters and searches the whole bucket, and the selected set is exported to a labeling and training workflow.
Mixpeek vs. Voxel51
Positioning
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Core job | Search, classify and review multimodal content across a whole bucket, from an API | Curate, label and evaluate datasets for model training |
| Primary users | Engineering, ML and data science teams shipping search or review features | ML and computer vision engineers improving perception models |
| Data focus | Video, audio, images, PDFs and text | Images, video, 3D and time-series sensor data for physical AI |
| Where results go | Retriever API, public retrievers, MCP tools, exports to your bucket | Curated datasets and labels inside Voxel51 |
Search and Retrieval
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Moments inside long video | Scene-level indexing; the moment_group stage merges matching intervals into start and end times per video | Segment-level embeddings and moment search across sensor timelines |
| Hybrid search | Dense, sparse and BM25 vectors with metadata filters, fused in a retriever | Semantic, lexical and structured search on a LanceDB-backed index |
| Pipeline stages | Filter, rerank, late-interaction rerank, dedupe, cluster, taxonomy and agentic enrichment, chained per retriever | Operators and plugins inside the app |
| Quality measurement | Retriever evaluations with standard IR metrics against ground-truth datasets | Model evaluation against labeled datasets |
Labels, Review and Versioning
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Model-assisted labels | agentic_enrich, llm_enrich and taxonomy_enrich stages classify documents at query time | Agentic Labeling drafts labels with vision-language models for review |
| Human review | Annotations API: label, confidence, reasoning and payload per document, with who decided and which retriever run surfaced it | Annotation workflows with QA and annotator metrics |
| Pixel, mask and 3D labeling | Not offered | Core feature |
| Versioning | Taxonomy versions, namespace snapshots with restore, per-document lineage, annotation versions | Dataset versions, sample edit history, activity logs |
Deployment and Pricing
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Pricing model | Usage-based: content processed and vectors stored, no seats | Free tier, then seat-based Team, Growth and Custom plans |
| Deployment | Mixpeek Cloud, dedicated cloud, or customer-hosted on your Kubernetes | Cloud, hybrid, on-premise and air-gapped |
| Agents | MCP server with 66 tools for ingestion, retrieval and admin | Voxel51 Agent with 45+ MCP tools, 80+ operators and 18 open-source skills |
| Open source | Proprietary platform with public SDKs | FiftyOne core is open source (Apache-2.0) |
TL;DR: Mixpeek vs. Voxel51
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Choose it when | You need search, classification or review over a large media library in object storage, served to apps or agents | You need to label, curate and evaluate training data for perception models |
| Pricing | Pay for content processed and stored | Pay per seat above the free tier |
| Using both | Filter and search the full bucket, export the selected set with lineage | Load the selected set to label and train on it |
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