Mixedbread AI
Unified multimodal retrieval API for agentic search across text, images, audio, and video.
Mixedbread is the leading retrieval API for deep-research agents, with top benchmark accuracy and a unique multimodal pipeline. For simple full-text search, however, lighter alternatives like Algolia are more cost-effective. Best for teams building agentic search across diverse media types.
- Building deep-research agents that search across text, images, audio, and video
- Multimodal RAG applications requiring high accuracy on complex QA evals
- Coding assistants that need context from code files, docs, and media
- Enterprise knowledge retrieval with SOC 2 / ISO 27001 compliance
- Simple full-text search on small datasets—Algolia or Meilisearch are lighter
- Cost-sensitive startups without clear usage transparency
- Teams wanting to bring their own embedding models
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Skip Mixedbread if you only need basic full-text search on a small dataset—Algolia or Meilisearch are simpler and cheaper.
Going past the $20 monthly credits on Scale incurs pay-as-you-go rates: $1.50/1M tokens for fast indexing, $3/1M for high-quality.
Mixedbread's freemium model suits startups prototyping multimodal search, but heavy usage scales costs via pay-as-you-go. Scale at $20/mo includes $20 in credits—effective if usage stays within that. Compared to Algolia ($1/1K searches) or Meilisearch (self-hosted free), Mixedbread is pricier for simple text search but cheaper for multimodal retrieval when factoring in embedding infrastructure savings.
In short
Mixedbread AI — Unified multimodal retrieval API for agentic search across text, images, audio, and video. Best for Building deep-research agents that search across text, images, audio, and video, Multimodal RAG applications requiring high accuracy on complex QA evals, Coding assistants that need context from code files, docs, and media. Free to start; paid plans from $20/mo.
What's new in Mixedbread AI
Checked 11 days agoAcross the latest 8 updates: 6 feature updates, 1 launch and 1 pricing change.
Asymmetric Quantization: Near-Lossless Late interaction Retrieval with 97% Storage Reduction
Asymmetric quantization reduces corpus storage 32x while holding 89.65 vs 90.26 NDCG@10 for late-interaction retrieval.
Dense Retrievers Know More Than They Can Express
Dense retrieval models contain an indexable sparse vocabulary plug-and-play with BM25, beyond their scoring mechanism.
Bring Your Own Bucket
Enterprise organizations can keep content in their own AWS S3 bucket; Mixedbread indexes with ephemeral compute.
Store Chunk Grep and Listing
Added stores.grep for regex matching and stores.list_chunks for metadata-filtered chunk retrieval with sorting.
Ranking Beyond Binary Relevance: mxbai-rerank-v3-listwise
New listwise reranker codesigned with Wholembed v3 improves results on every benchmark with instruction following.
Agentic Search Observability
Dashboard now shows agent runs for inspecting and tuning retrieval behavior in agentic search.
Startup Accelerator Credits
Mixedbread partners with Startup Accelerator to offer search credits for eligible early-stage startups.
Mixedbread Search Skill
Agent skill that gives coding agents context to integrate Mixedbread, reducing development time.
Viability Score
How likely is Mixedbread AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Semantic + lexical search (multi-vector embedding + keyword)
- Multimodal ingestion: text, PDF, images, audio, video
- 100+ language support
- Sub-200ms query latency
- Wholembed v3 late-interaction retrieval model
- mxbai-rerank-v3-listwise listwise reranker
- Agentic search with multi-step reasoning
- Agentic search observability dashboard
- stores.grep exact regex chunk matching
- stores.list_chunks with metadata filters and numeric sorting
- File upload to stores (PDF, code, images)
- CLI for bulk file operations
- MCP server integration for LLMs
- Client libraries: Python, TypeScript
- BYOB (Bring Your Own Bucket) for S3 indexing
About Mixedbread AI
Mixedbread is a full-stack search and retrieval API designed for AI agents that need to search across text, PDFs, images, audio, and video in 100+ languages. It automatically ingests and processes data with proprietary models and a custom search engine, returning results in sub-200ms. The platform achieves top accuracy on agentic search benchmarks like BrowseComp-Plus, MADQA, and OfficeQA-Pro while cutting tool-call volume by half—critical for latency-sensitive and cost-conscious AI workflows. Key features include semantic and lexical search (with stores.grep for exact regex matching), the mxbai-rerank-v3-listwise listwise reranker, and Wholembed v3 late-interaction model for native audio/video support. The dashboard recently added agentic search observability, letting you trace and tune retrieval behavior. Mixedbread also supports file uploads, chunk listing with metadata filters, a CLI for bulk operations, and MCP server integration for direct use with LLMs like Claude Code and Cursor. Security is enterprise-ready: SOC 2 Type II and ISO 27001 certified, with optional BYOB (Bring Your Own Bucket) for indexing AWS S3 data ephemerally. Pricing starts with a free Starter tier (trial with $5 credits), then a Scale tier at $20/month including $20 in credits, plus pay-as-you-go rates for indexing ($1.50-$3/1M tokens), search ($0.10-$20/1K queries), and storage ($0.50/1M tokens/month). Enterprise plans offer volume discounts, dedicated infrastructure, BYOC, and custom SLAs. Compared to alternatives like Algolia or Meilisearch, Mixedbread is purpose-built for multimodal, agentic retrieval rather than simple full-text search. It integrates deeper into LLM workflows via MCP, LangChain, and LlamaIndex, and excels on complex retrieval evals that require reasoning across modalities. For teams building deep-research agents or multi-modal RAG, Mixedbread delivers a unified pipeline that would otherwise require assembling multiple point solutions.
Behind the Verdict
Mixedbread is an excellent choice if you're building AI agents that need to search across text, images, audio, and video simultaneously. The unified pipeline—ingest → index → search → rerank—handles complex queries that would require multiple tools otherwise. Benchmarks like BrowseComp-Plus show it outperforms alternatives in multi-step retrieval while using fewer tool calls, which directly reduces latency and cost in agent loops. Where it bites: the pricing model is usage-based and can surprise startups. Search queries, especially agentic ones at ~$20/1K queries, add up fast if you don't set spending limits (available via the dashboard). The free tier gives only $5 in one-time credits, which won't go far in production. Smaller teams with straightforward text-only search will find Algolia or Meilisearch simpler and cheaper. The closest alternative is probably Cohere's Rerank or Jina's multimodal search, but Mixedbread's end-to-end approach (ingestion to reranking) avoids the need to stitch together separate embeddings and search services. Our view: if your use case is deep research, coding agent context retrieval, or multimodal RAG, Mixedbread is worth the investment. Start with the Scale tier, monitor usage via the observability dashboard, and set spending caps—it's a genuinely differentiated tool for complex agentic search.
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Real-world workflow fit
Concrete scenarios for the personas Mixedbread AI actually fits — and what changes day-one when you adopt it.
You're building a research agent that needs to retrieve from PDFs, images, and web pages simultaneously.
Outcome: You upload documents to a store, configure agentic search via the dashboard, and get sub-200ms multimodal results with traceable agent reasoning, reducing tool calls by half.
You want to add semantic search to your SaaS product across user-uploaded PDFs and images in multiple languages.
Outcome: You integrate Mixedbread API in a day using the Python SDK, set up a store with automatic chunking and embedding, and deploy with spending limits to control costs.
Use Cases
- Build a multimodal search engine that retrieves text, images, audio, and video simultaneously.
- Create a conversational AI agent with persistent memory using Mixedbread's agentic search.
- Implement semantic search over PDFs and documents in 100+ languages.
- Add web search capabilities to your application via the store-compatible API.
- Use open-source reranker models to improve retrieval accuracy in your RAG pipeline.
- Integrate search into coding agents (Claude Code, Cursor) with the Mixedbread Search skill.
Models Under the Hood
as of 2026-07-06
Limitations
- Free tier limited to 1000 store files and 100 requests/minute.
- Advanced features (agentic search, high-quality indexing, priority support) require Scale ($20/mo) or Enterprise.
- On-premise deployment only via Enterprise contract.
- API-first design means no visual search UI for non-developers.
as of 2026-06-30
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 Mixedbread AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$0/mo
Ideal for
Developers prototyping multimodal search with minimal usage—up to 10 stores and 100 req/min.
What this tier adds
Free entry point with $5 one-time credits, 3 workspace users, and community Slack support.
Scale
$20/mo
Ideal for
Growing teams needing higher throughput and priority support—unlimited users and up to 10,000 stores.
What this tier adds
Adds $20/month in credits, automatic backups, same-day Slack support, and 1,200 queries/min.
Enterprise
Custom
Ideal for
Organizations with large volumes, custom compliance, or on-premise needs—unlimited stores and custom SLAs.
What this tier adds
Adds volume discounts, BYOB, BYOC, point-in-time recovery, dedicated support, and custom rate limits.
Where the pricing makes sense
The company stage and team size where Mixedbread AI's pricing actually pencils out — and where peers do it cheaper.
Mixedbread's freemium model suits startups prototyping multimodal search, but heavy usage scales costs via pay-as-you-go. Scale at $20/mo includes $20 in credits—effective if usage stays within that. Compared to Algolia ($1/1K searches) or Meilisearch (self-hosted free), Mixedbread is pricier for simple text search but cheaper for multimodal retrieval when factoring in embedding infrastructure savings.
Setup time & first value
How long it actually takes to get something useful out of Mixedbread AI — broken out by persona, not the marketing-page minute.
For developers: create a store and start searching in under 10 minutes using the quickstart guide in the docs. CLI setup takes 5 minutes; integrating the Python or TypeScript SDK into an existing app takes about an hour. The Mixedbread Search skill for coding agents can be installed in seconds via `npx skills add mixedbread-ai/skills`.
Switching to or from Mixedbread AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Algolia: upload your documents via the Mixedbread CLI or SDK; retrain no embeddings—Mixedbread handles multimodal ingestion automatically.
- →From Pinecone: replace your embedding pipeline with Mixedbread's automatic indexing; use the compatible API to switch clients.
- →From Meilisearch: switch to Mixedbread's store-based search, but note the change from simple keyword to semantic+mixed retrieval.
Integrations
Resources & Guides
- Documentationmixedbread.com
Overview
The Search API that makes all your unstructured data understandable and usable for AI. Build intelligent search experiences with Stores.
- API Referencemixedbread.com
Introduction
The mxbai CLI provides a powerful command-line interface for managing Mixedbread stores and files directly from your terminal.
- Documentationmixedbread.com
Introduction
The Mixedbread MCP Server connects AI assistants to the Mixedbread Search API.
- Resourcemixedbread.com
Blog
The baked bytes of AI research & development.
Official links
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