NEWMVS for embeddings. Managed for files. Both on object storage.Vectors or files. Pick a path.Start →

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Vector Store (MVS)

Bring your own vectors. Dense, sparse, and BM25 search on object storage.

Managed Indexing

Connect a bucket and auto-extract scenes, faces, OCR, transcripts, and embeddings.

Build

Compose multi-stage search in <100ms: filter, join, rerank.

Feature Extractors

Typed pipelines for faces, scenes, transcripts, OCR, fingerprints.

S3, GCS, R2, Mux, LangChain, MCP, and more. Connect your stack.

Generate and store embeddings from 50+ models, then search them.

By Industry

Map, search, and reuse the moments that perform. Plugs into iconik & Mux.

Talent search, brand safety, creative analytics.

Scene search, recommendation, archive access.

Visual search, PDP enrichment, catalog QA.

Lecture search, transcript Q&A, content safety.

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By Use Case

Face & Person Search

Find anyone across video libraries in milliseconds.

IP & Copyright Detection

Logos, songs, faces: one pipeline, one report.

Visual Taste & Recs

Scene-similarity ranked recommendations with RL.

Brand & Ad Safety

Pre-publish content screening at bid-time speeds.

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Build

API reference, SDKs, recipes, and architecture guides.

Launches, deep dives, and field notes from our engineers.

Browse supported HuggingFace models by task and modality.

See what teams are building with Mixpeek.

Education

Vendor-neutral deep dives on perception, retrieval, and embeddings.

Multimodal University

Fundamentals of multimodal retrieval, modules + certs.

Every term you need: embeddings to re-rankers.

Talks, demos, and customer sessions on demand.

Mixpeek vs. Pinecone, Weaviate, Twelve Labs, more.

Mission, team, and the multimodal vision.

We're hiring across research, infra, and design.

Talk to sales, support, or press.

White-glove 30-day production pilot for new customers.

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Back to Glossary

What is JPEG, JPG, JSON, JSONL

JPEG, JPG, JSON, JSONL - Common data formats

Common data formats in multimodal data systems. JPEG/JPG for image storage, JSON/JSONL for metadata and annotations.

How It Works

JPEG and JPG are widely used formats for storing images, offering compression and quality balance. JSON and JSONL are text-based formats for representing structured data, commonly used for metadata and annotations in multimodal systems.

Technical Details

JPEG/JPG use lossy compression to reduce file size while maintaining visual quality. JSON is a lightweight data-interchange format, while JSONL (JSON Lines) is a newline-delimited variant for streaming data.

Best Practices

Choose appropriate formats for your data
Consider compression and quality trade-offs
Implement efficient data pipelines
Regularly update format strategies
Monitor format performance

Common Pitfalls

Using inappropriate formats
Ignoring compression and quality trade-offs
Inefficient data pipelines
Lack of regular updates
Poor performance monitoring

Advanced Tips

Use hybrid format techniques
Implement format optimization
Consider domain-specific format strategies
Optimize for specific use cases
Regularly review format performance

Managed Mixpeek

Put multimodal search to work

Connect a bucket and Mixpeek runs the whole multimodal search pipeline for you: extraction, indexing, and search over your own objects. No models to wire up, nothing to host.

Start with Managed

MVS · bring your own

Already have vectors?

Keep your embeddings on your own cloud and run dense, sparse, and BM25 search directly on object storage. From $25/mo.

Building an agent? Connect Mixpeek over MCP

Related Terms

ACID API Blob Storage CLIP Embedding