Mixedbread AI
Multimodal retrieval API that gives AI agents evidence, not raw search hits
Mixedbread is the pick when your agent's bottleneck has moved from reasoning to context. The benchmarks it publishes under its own name make the case better than any pitch page: the same task score on 3.5x fewer tokens, and 70% correctness on OfficeQA Pro V2 at about $1.15 versus $4.68 for Codex alone. You are renting the whole pipeline — parsing, OCR, transcription, embedding, reranking, and the Toast 1 loop — so pass if you need your own embedding models, per-stage control, or anything
Verified 16h ago · liveness 86/100 · cite: rightaichoice.com/tools/mixedbread-ai
- Knowledge agents answering from contracts, filings, or internal doc sets where citations matter
- Teams whose agents burn tokens hunting for context and need turn counts down
- Multimodal search over video, podcast, slide or diagram archives including SVG and GIF
- Coding agents that need to search a monorepo and docs instead of grepping blind
- Small corpora under a few thousand docs where Meilisearch or Algolia is lighter and cheaper
- Teams that need to bring their own embedding models or control each pipeline stage
- Air-gapped or on-prem requirements — BYOB and BYOC are Enterprise contract items
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Skip Mixedbread if you need to run your own embedding models, control every pipeline stage, or deploy fully offline — it is a hosted API that owns the whole stack, and there is no air-gapped option.
Storage bills at $0.50 per million content tokens per month whether you query the corpus or not, so a large idle knowledge base is a standing cost.
At $20/mo with $20 coming back as monthly credits, Scale is priced for a small team shipping an agent rather than a solo prototype; Starter's $5 one-time credit is enough to test but not to run production traffic. Above it, Enterprise is a custom contract with volume discounts and BYOB, so the honest benchmark is object storage plus an open-source reranker, which is cheaper on paper but leaves you owning parsing, chunking, and model selection. Against managed web-search APIs like Exa or
In short
Mixedbread AI — Multimodal retrieval API that gives AI agents evidence, not raw search hits. Best for Knowledge agents answering from contracts, filings, or internal doc sets where citations matter, Teams whose agents burn tokens hunting for context and need turn counts down, Multimodal search over video, podcast, slide or diagram archives including SVG and GIF. Free to start; paid plans from $20/mo.
No head-to-head comparison for Mixedbread AI yet — the alternatives below are the closest matches we list.
What's new in Mixedbread AI
Checked 8 days agoAcross the latest 9 updates: 7 feature updates, 1 pricing change and 1 news mention.
Download links added to search results
Chunks from search, grep and list chunks now include a file_url download link valid for one hour, removing the need for a second call to fetch the source document.
Spending limits become monthly budgets
Spending limits are now monthly budgets applied after included credits. On reaching the budget, choose to pause API requests or keep serving; both send an alert to the billing email.
API keys can be scoped by Store tag
API keys can now be scoped to every Store carrying a given tag, such as dev or prod, including Stores tagged later. Keeps environment permissions separate.
Zero Data Retention attestation for BYOB
Bring your own bucket deployments run under Mixedbread's Zero Data Retention architecture. The attestation is downloadable from Settings and shareable with security and legal teams.
Toast 1 available in Agent Playground
The agent playground now runs Toast 1 deep search over Stores, the web, MCP servers and custom tools, with a code view showing how to build it into an app.
Stores accept SVG and GIF files
Stores now accept .svg and .gif files, with SVGs rendered to an image first so logos, diagrams and vector artwork are searchable like other images.
Tag Stores for tracking and filtering
Stores can be assigned tags to track and filter them by environment, source or tenant. A copied Store inherits the tags of its source.
Copy Store for testing and branching
Copy all files from one Store into a new one in a single call without reprocessing, for snapshots or second environments. Neither Store accepts file changes until the copy finishes.
Structured output for Toast 1
The Responses and Chat Completions APIs now support OpenAI structured outputs, letting Toast 1 deep search and extract information into structured formats.
What people actually say about Mixedbread AI — 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.
8 mentions across 2 sources (Hacker News, Bluesky) · researched Jul 6, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +True multimodal search across text, images, audio, and video in one API.
- +Sub-200ms latency with state-of-the-art accuracy on retrieval benchmarks.
- +Small, efficient models like mxbai-edge-colbert-v0 outperform larger alternatives.
- +Novel dimensionality pruning compresses embeddings 50:1 without quality loss.
- +Open-sourced reranker and embed models available on Hugging Face.
- −Very limited real-world production feedback — mostly research demos.
- −Advanced features require machine learning expertise to fully exploit.
- −Pricing lacks transparent cost examples at scale, risking bill shock.
- −No native no-code or Zapier integrations for less technical teams.
- −Vendor lock-in on proprietary retrieval models and APIs.
- • Indexing and storage costs scale with data volume — no fixed caps on free or Scale tiers.
- • Pay-as-you-go rates may surprise heavy users without spending limits configured.
Viability Score
How well maintained and how widely used is Mixedbread AI? 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: October 2026
How we score →Key Features
- Multimodal ingestion of text, PDFs, DOCX, images, audio, video, code, SVG and GIF in 100+ languages
- Four search modes behind one flag: semantic search, exact grep, reranking, and agentic search
- Wholembed V3 late-interaction omnimodal embedding model, reported #1 on ViDoRe V3
- mxbai-rerank listwise reranker trained on the same distribution as the embedder
- Toast 1 specialized search agent running inspect, search, grep and rank loops
- OpenAI structured outputs for Toast 1 extraction via Responses and Chat Completions APIs
- Evidence results carrying source file, page, passage, bounding box and chunk
- Download links valid for one hour attached to search and grep results, no second fetch
- Streaming server-sent trace events for live agentic search debugging
- OpenTelemetry span export to Langfuse, Braintrust, or any OTLP collector
- Monthly budgets that pause API requests or keep serving once included credits run out
- API keys scoped by Store tag for dev/prod permission separation
- Store tags for filtering by environment, source or tenant, inherited on copy
- Copy Store to snapshot or branch a corpus in one call without reprocessing
- Fast and High Quality indexing modes, the latter adding layout-preserving OCR, transcription and image descriptions
About Mixedbread AI
Mixedbread is a hosted retrieval API for agents that need to search your own data before they answer or act. You upload PDFs, decks, video, audio, code, SVG and GIF files, or connect Slack and Google Drive; Mixedbread parses, OCRs, transcribes, chunks, embeds, and indexes the lot across 100+ languages. Your agent then queries it through one flag with four modes available: semantic search, exact grep, reranking, and agentic search. The stack is built in-house rather than assembled from parts that never met. Wholembed V3, a late-interaction omnimodal embedder the team reports at #1 on ViDoRe V3 ahead of Voyage 4 and Cohere Embed 4, feeds mxbai-rerank listwise, a reranker trained on the same distribution. Underneath sits Silo, an S3-native multi-vector engine that stores multiple vectors per item instead of pooling them down. Toast 1 is the search agent on top, running the inspect, search, grep, rank loop and coming back with file, page, bounding box, and passage attached to every hit. The commercial pitch is token economics, and Mixedbread publishes its own numbers rather than talking around them. On the Harvey LAB firm-knowledge benchmark, swapping in Mixedbread Search took a vanilla agent from 80.6M to 47M tokens, and Toast 1 as a subagent took it to 23M, all three configurations landing on the same task score of 55. On OfficeQA Pro V2, Codex with Toast 1 reached 70% answer correctness at roughly $1.15 per task against Codex alone at 33% for $4.68. Integration runs through Python and TypeScript SDKs, a CLI, an MCP server for Claude Code, Cursor and Codex, and agent skills. Every search, grep, and read is traced, with spans exportable via OpenTelemetry to Langfuse, Braintrust, or any OTLP collector. The closest alternatives are vector databases you assemble yourself and public-web search APIs like Exa or Parallel; Mixedbread sits between them, covering owned data the web will never see.
Behind the Verdict
There's a specific moment when Mixedbread becomes the obvious call: your agent is good at reasoning and bad at finding. Token spend is climbing, turn counts are climbing, and the answers are still wrong because the right passage never made it into context. That is the problem this stack is aimed at, and the Harvey LAB numbers are the clearest statement of it — 80.6M tokens down to 23M and the same score of 55 at the end. Pick it if you'd rather rent the entire retrieval pipeline than staff one. Parsing, OCR with layout preservation, transcription, embedding, reranking, and an agent that runs the multi-step search loop itself all arrive in one API. Toast 1 is the part worth a demo: point it at a question, and it decides when to inspect metadata, when to grep, when to search the corpus, and hands back evidence with page and bounding box so your app can cite what it used. The tracing story matters more than vendors usually admit. Waterfall traces of agentic search, streaming trace events, and OpenTelemetry export to Langfuse or Braintrust mean you can see the sub-query or grep pattern that produced a bad result instead of guessing. Recent changelog work pushed in the same direction: monthly budgets that pause or keep serving once credits run out, API keys scoped by Store tag so dev and prod don't bleed, and a Zero Data Retention attestation you can hand to legal for bring-your-own-bucket deployments. Where it bites. Everything here is hosted, and there is no path to running Wholembed or Silo inside your own network on the Starter or Scale plans — BYOB and BYOC are Enterprise contract items. Pricing is metered and the meter is real: $4 per thousand semantic queries, $7.50 with reranking, plus $1.50 or $3 per million content tokens to index and $0.50 to store. A chatty
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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.
They create a Store, upload a folder of PDFs and slide decks through the Python SDK, then call stores.search with search_options agentic and rerank set to true.
Outcome: The agent gets back evidence with file, page, and passage attached instead of raw chunks, so it cites what it acted on and the team can audit the answer.
They install the MCP server and the mixedbread-ai/skills package, then point the coding agent at a Store holding the monorepo and internal docs.
Outcome: The agent searches and greps the codebase for real context instead of guessing at file paths, with every read traced back to a specific chunk.
They route search through Toast 1 as a subagent and wire OpenTelemetry export to Langfuse under Settings then Tracing.
Outcome: Total tokens on the benchmark-style task drop by roughly 3.5x at identical task score, and each search and tool call shows up as a span they can inspect for latency.
Use Cases
- Build a multimodal search engine that retrieves text, images, audio, and video from one index.
- Give a knowledge agent evidence from contracts, filings, or internal docs instead of stuffing context.
- Add persistent agent memory by treating the store as what the agent remembers across sessions.
- Implement cross-lingual semantic search over PDFs and documents in 100+ languages.
- Run agentic deep search inside Claude Code or Codex via the MCP server and skills.sh skills.
- Cut token spend in agent loops by replacing blind grepping with Toast 1 evidence returns.
- Add listwise reranking to an existing RAG pipeline without owning the reranker.
- Snapshot and branch a production store for testing before a risky corpus change.
Models Under the Hood
as of 2026-10-16
Limitations
- Usage pricing is uniform across plans; Starter, Scale and Enterprise differ in included credits, seat/store limits and support.
- Starter is capped at 3 workspace users, 3 stores, 100 queries/min and 6,000 uploads/min, and Scale at 10,000 stores with 2,400 queries/min and 12,000 uploads/min, with custom limits and SLA only on Enterprise.
- Chunks per store are capped at 100M on Starter and Scale, and automatic backups are not included on Starter.
- It is API/SDK/CLI/MCP-first, with no native desktop or mobile app indicated in the evidence.
as of 2026-09-30
Verification history
We have re-verified Mixedbread AI 20 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-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
- — 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 20 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 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
Solo developer or two-person team testing whether Mixedbread's retrieval quality beats the vector database they already run.
What this tier adds
Free entry point: $5 in one-time credits with no card required, 3 workspace users, 3 stores, and 100 requests per minute.
Scale
$20/mo
Ideal for
A funded startup or platform team running an agent in production with more than a handful of stores and users.
What this tier adds
Adds $20 of credits back every month, unlimited workspace users, 10,000 stores, 1,200 queries/min, 360 ingestions/min, and automatic backups.
Enterprise
Custom
Ideal for
Regulated or high-volume organizations that need custom rate limits, BYOB or BYOC deployment, and a signed SLA.
What this tier adds
Adds volume-based discounts, unlimited stores and chunks, custom rate limits and SLA, point-in-time recovery, and dedicated support.
Mixedbread for Startups
$250 in credits
Ideal for
Early-stage startups with institutional venture backing or existing paying usage that want to offset their first months of indexing and search.
What this tier adds
One-time credit grant of $250 on top of a normal plan, with more available case by case for teams that qualify.
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.
At $20/mo with $20 coming back as monthly credits, Scale is priced for a small team shipping an agent rather than a solo prototype; Starter's $5 one-time credit is enough to test but not to run production traffic. Above it, Enterprise is a custom contract with volume discounts and BYOB, so the honest benchmark is object storage plus an open-source reranker, which is cheaper on paper but leaves you owning parsing, chunking, and model selection. Against managed web-search APIs like Exa or
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.
A developer with an API key can create a Store and get first search results in under ten minutes using the Python or TypeScript SDK with the quickstart — ingestion of a small corpus finishes in minutes on Fast indexing. Wiring the MCP server into Claude Code or Cursor adds another fifteen minutes. Teams indexing a large High Quality corpus with OCR and transcription should budget hours of
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 a raw vector database (Pinecone, Weaviate): move stored files into a Mixedbread Store and drop the chunking, embedding, and reranking code you maintained.
- →From Algolia or Meilisearch: re-upload source documents to gain PDF, image, audio, and video understanding instead of text-only indexing.
- →From a self-hosted RAG stack: replace your parser, embedding model, and reranker with one Store and select a search mode per request.
- →From Exa or Parallel: keep web search for public pages, and move owned corpora into a Mixedbread Store where evidence provenance is required.
- ↗To a self-hosted stack: export source files from your Store and rebuild parsing, embedding, and reranking on open-source components if you need full pipeline control.
- ↗To Exa or Firecrawl: switch to a public-web index if your answers depend on pages you do not own.
- ↗To object storage plus an open-source reranker: keep files in S3 and run retrieval yourself when storage and query volume make the hosted rates uneconomic.
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.
Tutorials & Learning
YouTube returned 6 videos for “Mixedbread AI”, and we withheld 6: 6 could not be judged, because “Mixedbread AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Mixedbread AI.
Official links
Tools that pair well with Mixedbread AI
Common stack mates teams adopt alongside Mixedbread AI, with the specific reason each pairing earns its keep.
Tavily
Tavily is a real-time web search API that grounds AI agents in fresh, structured web data for reliable retrieval-augmented generation
Kindly Web Search Mcp Server
Open-source MCP server that gives AI coding agents live web search plus noise-resistant full-page extraction.
Ragatouille
RAGatouille brings ColBERT late-interaction retrieval into any Python RAG pipeline — train, index, and search in a few lines.
Alternatives to Mixedbread AI
View allTavily
Tavily is a real-time web search API that grounds AI agents in fresh, structured web data for reliable retrieval-augmented generation
Kindly Web Search Mcp Server
Open-source MCP server that gives AI coding agents live web search plus noise-resistant full-page extraction.
Ragatouille
RAGatouille brings ColBERT late-interaction retrieval into any Python RAG pipeline — train, index, and search in a few lines.
Frequently Asked Questions
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