LanceDB
Circuit mix
Where LanceDB shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Modern data stack & practitioner29%9 events
- AI engineering & MLOps26%6 events
- Open-source data infrastructure20%7 events
- Python & R data science14%4 events
- Vendor flagships & roadshows11%4 events
The year around now
Conferences where LanceDB sponsored or had speakers in the past 12 months, and the announced events it is already listed for. One row per conference, January to December; hover a mark for the event and the company's role.
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
Activity by year
Three measures on one year axis; each panel has its own scale.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Silver
- Bronze
- Other / unlabeled
Where they've grown or pulled back
UpSponsored 3 conferences in 2026, up from 1 in 2023.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. Tiers use the best tier each year; swatches show the path, oldest first. A conference counts as pulled out only when a later event published a complete sponsor list, since most events don’t say whether their list is complete.
New 4
First sponsored in 2025 or later.
- AI Engineer (World's Fair, Summit and regional editions)first sponsored 2026, Bronze
- Data Councilfirst sponsored 2026, Bronze
- New York R Conference (now The New York Data Science & AI Conference)first sponsored 2025, Silver
- Ray Summitfirst sponsored 2026, Silver
Once or on and off 1
No clear direction, or a gap we can’t confirm because later sponsor lists are incomplete.
- MLOps World (Machine Learning in Production)once, 2023, Startup tier
Lookalikes
Companies with the most similar conference footprint: they sponsor and speak at the same conferences. Bars are cosine similarity, from 0 (nothing shared) to 1 (identical).
- Braintrust0.82
- Riot Games0.77
- Arcee AI0.77
- Instacart0.77
- Arize AI0.75
- Vectara0.73
- Tigris Data0.72
- NEA0.71
11 Cluster: AI Engineer · QCon circuit →
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 5 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| Datadog | 3 | 60% |
| Microsoft | 3 | 60% |
| Airbyte | 2 | 40% |
| Amazon Web Services | 2 | 40% |
| bem | 2 | 40% |
| Braintrust | 2 | 40% |
| ClickHouse | 2 | 40% |
| Deepgram | 2 | 40% |
| DigitalOcean | 2 | 40% |
| Elastic | 2 | 40% |
| Encord | 2 | 40% |
| 2 | 40% | |
| Nebius | 2 | 40% |
| NVIDIA | 2 | 40% |
| Prior Labs | 2 | 40% |
Neighborhood: companies with nearby conference footprints
LanceDB (ringed) and the 30 companies closest to it on the company map. Close together means they sponsor and speak at many of the same conferences. Distances are only meaningful locally; the axes have no meaning. Dot size: conferences present at. Color: cluster.
Sponsorships 5
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| Ray Summit 2026 | Aug 24–26, 2026 | San Francisco, United States | Silver | Silver |
| AI Engineer World's Fair 2026 | Jun 29, 2026 – Jul 2, 2026 | San Francisco, United States | Bronze | Bronze |
| AI Council 2026 | May 12–14, 2026 | San Francisco, United States | Bronze | Bronze |
| The New York Data Science & AI Conference 2025 | Aug 26–27, 2025 | New York, United States | Silver | Silver |
| 4th Annual MLOps World Conference & Expo 2023 | Oct 25–26, 2023 | Austin, United States | Start-Up Corner | Startup |
Speakers 37
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Chang She | Reverie 2026 | moderator | Physical AI Researchers (panel) | — |
| Chang She | Reverie 2026 | keynote | Welcome: What Will AI Dream Next? | — |
| Chang She | Reverie 2026 | speaker | Closing Remarks: From What AI Dreams to What We Build Next | — |
| Ding Hao (Xuanwo) | AI Engineer Shanghai 2026 | speaker | Robot Data Needs a Build System | — |
| Fei Xue | Reverie 2026 | moderator | Building the Autonomous Driving Data Flywheel (panel) | — |
| Lei Xu | Reverie 2026 | panelist | Building the Autonomous Driving Data Flywheel (panel) | — |
| Justin Miller | Data Con LA 2026 | speaker | Embeddings at Scale: Lessons from LanceDB and the Lance Format | Video |
| Chang She | Data + AI Summit 2026 | speaker | Managing Data at Exabyte Scale for AI Model Training | — |
| Jack Ye | Data + AI Summit 2026 | speaker | Managing Data at Exabyte Scale for AI Model Training | — |
| Jack Ye | Data + AI Summit 2026 | speaker | From Streaming to Search: How Exa Uses Lance and Apache Spark for High-Throughput AI Workloads | — |
| Lei Xu | Snowflake Summit 26 | speaker | Apache Polaris in Practice: Open Catalogs, Open Formats, Open Community | — |
| Chang She | AI Council 2026 | talk | Trillion is the New Billion: Managing Really Large Multimodal Datasets for AI | — |
| Lei Xu | AI Council 2026 | talk | Trillion is the New Billion: Managing Really Large Multimodal Datasets for AI | — |
| Prashant Rao | San Francisco Apache DataFusion Meetup (May 2026) | speaker | Distributed query execution with DataFusion and Lance | — |
| Chang She | OpenXData 2026 (April) | speaker | Managing Data at Exabyte Scale for AI Model Training | Video |
| Weston Pace | Seattle/Bellevue Apache DataFusion Meetup | speaker | DataFusion without Row Groups | Video |
| Jack Ye | Data Engineering Open Forum 2026 | speaker | Powering Netflix's Multimodal Feature Engineering at Scale | Video |
| Prashanth Rao | Data Day Texas + AI 2026 | speaker | — | — |
| Prashanth Rao | AI By the Bay 2025 | speaker | Agents Need More than Vector Search | Video |
| Chang She | PyData Seattle 2025 | keynote | Keynote: Chang She - Never Send a Human to do an Agent's Search | Video |
| Jack Ye | PyData Seattle 2025 | speaker | Supercharging Multimodal Feature Engineering with Lance and Ray | Video |
| Weston Pace | PyData Seattle 2025 | speaker | Data Loading for Data Engineers | Video |
| Chang She | TiDB SCaiLE 2025 | speaker | — | — |
| Chang She | PyData Berlin 2025 | tutorial | AI-Ready Data in Action: Powering Smarter Agents | Video |
| Chang She | Data + AI Summit 2025 | speaker | LanceDB: A Complete Search and Analytical Store for Serving Production-scale AI Applications | — |
| Jonathan Hsieh | Data + AI Summit 2025 | speaker | DevConnect Meetup | — |
| LU QIU | Data + AI Summit 2025 | speaker | Bridging Big Data and AI: Empowering PySpark With Lance Format for Multi-Modal AI Data Pipelines | — |
| Chang She | AI Engineer World's Fair 2025 | speaker | Scaling Enterprise-Grade RAG Systems: Lessons from the Legal Frontier | Video |
| Weston Pace | Iceberg Summit 2025 | speaker | Turbocharge Queries on Iceberg with Next-Gen File Formats | Video |
| Chang She | AI Engineer World's Fair 2024 | speaker | The Hierarchy of Needs for Training Dataset Development | Video |
| Lei Xu | PrestoCon Day 2024 | speaker | Bridging the Divide: Running Presto SQL on a Vector Data Lake powered by Lance | — |
| Chang She | San Francisco Bay Area Apache DataFusion Meetup (June 2024) | speaker | — | — |
| Lei Xu | Trino Fest 2024 | speaker | Bridging the divide: running Trino SQL on a vector data lake powered by Lance | Slides · Video |
| Chang She | New York R Conference 2024 (10th anniversary) | talk | Building Data Tooling in Rust for Multimodal AI | Video |
| Chang She | Data Council Austin 2024 | talk | Foundations for a Multi-Modal Lakehouse for AI | — |
| Chang She | DuckCon #3 | lightning talk | Bringing AI to DuckDB with Lance columnar format for multi-modal AI | — |
| Chang She | Data Council Austin 2022 | talk | Rikai: a new data format for analytics on unstructured data at scale | — |