Mixpeek vs Voxel51
A detailed look at how Mixpeek compares to Voxel51.
Mixpeek
Voxel51Key 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 features, embeddings and lineage to your own bucket as Parquet, so the team can query them with SQL in DuckDB, Spark or a warehouse.
- 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.
- Reads robotics formats natively: their own comparison lists MCAP and LeRobot support, which Mixpeek does not offer.
- Free tier with no credit card: 3 datasets, 10,000 samples each, 20 GB of hosted storage and 30 compute hours a month.
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 |
Physical AI Data
| Feature / Dimension | Mixpeek | Voxel51 |
|---|---|---|
| Search inside recordings | Indexes the camera video and files from each episode or drive at the scene level; one query returns start and end times in every matching recording | Segment-level embeddings and event search across synchronized sensor timelines |
| Robotics formats | No native MCAP, ROS bag or LeRobot reader; index the exported video, images and metadata | MCAP and LeRobot support, plus 3D and LiDAR views |
| How much data | Processes the whole bucket where it sits; vectors are stored on object storage in every deployment model | Curated datasets loaded into Voxel51; free tier caps at 3 datasets of 10,000 samples |
| Where it runs | Mixpeek Cloud, or customer-hosted on your own Kubernetes in your cloud account | Hosted workspace; self-hosted on Team; on-premise and air-gapped on Enterprise |
| SQL over features | Export a collection as Parquet with embeddings and lineage, then query it with DuckDB, Spark or a warehouse; the SQL lookup stage joins Postgres rows into search results | Python SDK and query language over FiftyOne datasets |
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 Team (from 8 user seats) and Enterprise (from 25), priced through sales |
| 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 |
Frequently Asked Questions: FiftyOne alternatives for physical AI
Is Mixpeek a FiftyOne alternative for physical AI data?
For search and curation over large recording archives, yes. Mixpeek indexes the video, images and files from robot episodes or vehicle drives where they already sit in S3, GCS or Azure, and a single query returns the matching moments with start and end times across every recording. FiftyOne is the better fit for pixel, mask and 3D labeling, synchronized multi-sensor timelines and native MCAP or LeRobot files, none of which Mixpeek offers. Many teams use Mixpeek to search and filter the full archive, then load the selected episodes into a labeling tool.
Can I search inside robot episodes and driving logs with Mixpeek?
Yes, through their video. Mixpeek splits each camera stream into scenes, embeds them, and the moment_group retriever stage merges matching scenes into time ranges per recording, so a query such as "gripper drops the part" returns the episode and the seconds where it happens. Mixpeek does not read MCAP, ROS bag or LeRobot files directly; export the camera video, frames and episode metadata to your bucket and index those.
Can Mixpeek curate data in my own cloud account?
Yes. Mixpeek processes the bucket in place through storage connectors for S3, GCS, Azure Blob and others, and the customer-hosted deployment runs the processing and the vector store in a Kubernetes namespace you grant in your own cloud account. Vectors are stored on object storage, so the archive does not have to fit into a hosted dataset. Voxel51 offers self-hosting on its Team plan and on-premise or air-gapped deployment on Enterprise.
Can I query extracted features with SQL?
Yes, by exporting them. The collection export endpoint writes Parquet by default to a storage connection you own, with optional embeddings and lineage back to each source object and processing step. DuckDB, Spark, BigQuery or Snowflake can then query the features directly. Inside a retriever, the SQL lookup stage joins rows from a PostgreSQL database into search results.
How does Mixpeek pricing compare with Voxel51?
Mixpeek charges for content processed and vectors stored, with no seats. Voxel51 has a free tier (3 datasets of up to 10,000 samples, 20 GB of hosted storage, 30,000 inference requests and 30 compute hours a month), then Team plans from 8 user seats and Enterprise plans from 25, both priced through sales. Voxel51 facts on this page come from voxel51.com/pricing and voxel51.com/fiftyone-alternatives, checked on 5 November 2026; Mixpeek facts come from the Mixpeek docs and API reference.
Ready to See Mixpeek in Action?
Discover how Mixpeek's multimodal AI platform can transform your data workflows and unlock new insights. Let us show you how we compare and why leading teams choose Mixpeek.
Explore Other Comparisons
VS
Mixpeek vs DIY Solution
Compare the multimodal warehouse approach with cobbling together vector databases, embedding APIs, processing pipelines, and glue code. The total cost of a Frankenstack is 10-20x higher than you think.
View DetailsVS
Mixpeek vs Coactive AI
See how Mixpeek's developer-first, API-driven multimodal AI platform compares against Coactive AI's UI-centric media management.
View Details