Sharding: up to 256TB in one index

Linear indexes more documents with turbopuffer

Linear replaced Elasticsearch & pgvector to cut down ops and unify their search infrastructure. Cost wasn't the focus, but 70% savings has Linear thinking about indexing far more data.

70%

cost reduction

2B+

documents

13ms

p50 latency

4M+

namespaces

Their responsiveness and shipping velocity make us feel like we are their only customer.

Tom Moor

Tom Moor, Head of Engineering

Why turbopuffer?

Linear was drawn to turbopuffer for its ability to ingest 100s of millions of full-text search documents & vectors without having to think about machine types. Instead, Linear can focus on shipping features.

Linear does hybrid search (FTS + vector) on multiple (org_id, table) namespaces, rank fuse, and re-rank:

┌──────────────────────────────┐
│ ┌──────┐  ┌────────────────┐ │
│ │Linear│─▶│ Cohere         │ │
│ │Query │  │ Embedding      │ │
│ └──────┘  └──────┬─────────┘ │
│                  ▼           │
│ ┌─turbopuffer queries────┐   │
│ │ Issues Vector          │   │
│ ├────────────────────────┤   │
│ │ Issues FTS             │   │
│ ├────────────────────────┤   │
│ │ Document Vector        │   │
│ ├────────────────────────┤   │
│ │ Document FTS           │   │
│ ├────────────────────────┤   │
│ │ Project Vector         │   │
│ ├────────────────────────┤   │
│ │ Project FTS            │   │
│ └───────────┬────────────┘   │
│             ▼                │
│     ┌────────────┐           │
│     │ Rank Fuse  │           │
│     └─────┬──────┘           │
│           ▼                  │
│    ┌──────────────┐          │
│    │ Cohere Rerank│          │
│    └──────────────┘          │
└──────────────────────────────┘

Customer data security is critical to Linear. The ability to use customer managed encryption keys (CMEK) on a per namespace basis stood out.

turbopuffer in Linear

When a user submits a search, Linear will issue parallel queries across multiple namespaces (documents, issues, projects, comments, attachments, issues, and initiatives) using vector + FTS to return a list of results that then get passed into a reranker:

Linear search GIF showing issue-search flow

Linear also leverages turbopuffer for:

  1. Finding similar issues when a user opens a new issue
  2. Offering suggestions during issue creation, like which user should be assigned or which project the issue should belong to
  3. 🔜 Providing context to agentic workflows that are seamlessly integrated into the Linear experience

Linear plans to leverage turbopuffer to power all of their AI features.

Log

Linear migrated search off Elasticsearch and pgvector onto turbopuffer and experienced:

  1. Single system for vector and full-text search
  2. Zero-ops search for terabytes of data
  3. 70% reduction in cost, unlocking indexing far more data

It's been fun to work with @turbopuffer on this. Admittedly, keyword-based search never really worked great for issues, and the results from embedding+FTS are now actually useful. We'll also use this new infrastructure to power more of Linear's AI functionality

Jori Lallo

Jori Lallo, Co-founder

Linear migrated from running its own embedding pipeline to turbopuffer's native embeddings. They no longer have to deal with juggling rate limits and SDK complexity across multiple embedding providers, and they also sped up their embedding pipeline:

  • ~150ms lower embedding latency
  • 200M+ documents, 30B+ tokens embedded

We love working with turbopuffer. I've never felt sold to and I've always felt I know exactly what I'm buying. Which when you're buying critical infrastructure is absolutely crucial.

Peter Travers

Peter Travers, Software Engineer

We will continue to update this log as Linear's usage of turbopuffer evolves.