Multimodal lakehouses: The architecture AI teams are migrating to
When the “lakehouse” was first introduced in 2020, the goal was to reconcile data warehouses and data lakes in a single architecture: open formats on cheap object storage, with ACID transactions, schema enforcement, governance, BI support, and streaming built in. The promise was simple: one system for SQL analytics, data science, and machine learning, instead of shuttling data between lakes, warehouses, and specialized stores. That design assumed that most workloads were still fundamentally tabular, even when dealing with logs or semi-structured JSON — and that analytics, not AI model serving, was the primary consumer.
Multimodal AI challenges those assumptions. Modern systems combine text, images, video, audio, sensor streams, and very large embeddings. Rows are no longer kilobytes; they can be megabytes or more. Access patterns are not “scan a table once a day”; they are “fetch thousands of random clips or vectors per second, update labels continuously, and feed GPUs without ever starving them.” Traditional formats such as Parquet and table layers like Iceberg struggle under these conditions: they were built for batch analytics, not low-latency, mutable, vector-heavy workloads. To prevent a regression into siloed architectures — where teams maintain separate vector databases, search engines, and blob stores — teams are revisiting the lakehouse concept.
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From Batch Analytics to Random Access: Inside the Lance Format
Lance is a columnar data format engineered specifically for AI workloads rather than traditional business intelligence. While legacy formats like Apache Parquet are optimized for sequential scans — ideal for aggregating sales figures — they create significant I/O bottlenecks in modern machine learning pipelines. Deep learning tasks, such as training shuffles or retrieval-augmented generation (RAG), demand high-performance random access to specific rows, a pattern that traditional formats struggle to support efficiently.
Lance addresses this by unifying embeddings, media references (pointers to video, audio, or images), and structured metadata within a single schema. It integrates search capabilities directly into the storage layer to minimize data transfer. By filtering on lightweight attributes first, it ensures that large files — such as images or videos — are retrieved only when absolutely necessary. This architecture delivers substantial performance gains; benchmarks indicate Lance achieves 3x to 35x faster random reads and up to 10x faster vector queries compared to Parquet, often eliminating the need for external indexing services.
This unification also shows up in the storage stack. In a conventional lakehouse setup, teams typically combine three separate pieces: a columnar file format such as Parquet, a table format like Iceberg or Delta Lake, and an external catalog definition to keep track of tables. Lance covers all three of these specification layers at once: it defines how data is stored in individual files, how those files are organized into a logical table, and a lightweight namespace mechanism for locating tables. Because that structure is captured in a single, coherent format, the same Lance tables can be registered with different catalog services and accessed by multiple compute engines without changing the underlying data.
Critically, Lance solves the operational challenge of data mutability. Whereas modifying datasets in Parquet often requires expensive rewrites, Lance utilizes a Delta-style log and layered on-disk layout. This supports streaming inserts, column appends, and deletions with automatic compaction, ensuring high read performance while enabling snapshot-based versioning and “time-travel” queries essential for model reproducibility.
LanceDB elevates the Lance format into a multimodal lakehouse, an architectural paradigm designed to consolidate the storage and retrieval of complex data types — including video, audio, 3D models, and embeddings — alongside traditional tabular records. This approach diverges from the standard data lake model by treating media and vector embeddings as core components rather than auxiliary attachments. By tightly coupling vector search with the storage engine, the system eliminates the need for external indexing services, enabling efficient hybrid queries that filter semantic results by metadata constraints (e.g., “retrieve similar video clips created within the last hour”).
While retaining the scalability and object-storage economics of traditional lakehouses, the system is re-engineered for AI-native workflows such as Retrieval-Augmented Generation (RAG) and model training. Key differentiators include the co-location of raw assets and embeddings to minimize latency, “zero-copy” pipelines that allow training and serving to access the same storage without duplication, and support for streaming updates — a necessity for dynamic production environments.
Multimodal lakehouses are already being used in production, and adoption highlights two primary drivers: performance optimization and infrastructure consolidation.
Performance:Netflix migrated from Parquet to LanceDB to unify video frames and metadata, resolving bottlenecks in the high-frequency access patterns required for media analytics. Similarly, generative video startup Runway adopted the platform to prevent GPU starvation during training; the format’s random access capabilities allow for efficient streaming and on-the-fly data augmentation without the need to rewrite massive datasets.
Consolidation: For CodeRabbit, an AI code review tool, the shift was operational. They replaced a fragmented stack of Pinecone (for vectors) and PostgreSQL (for metadata) with a single LanceDB instance, reducing complexity while enabling hybrid search over code snippets. Likewise, Geneva and TwelveLabs utilized the platform to aggregate embeddings from different modalities, creating a unified semantic search engine for audio and video content.
Multimodal AI has broken the assumptions of the traditional data lake: rows are huge, access is random, and GPUs can’t afford to wait on batch-era storage.
Other Multimodal Data Management Systems
LanceDB is not operating in a vacuum. The race to define data infrastructure for multimodal AI has spawned other architectures, each addressing specific bottlenecks in latency, throughput, or workflow orchestration.
Magnus (ByteDance): Designed for the exabyte-scale requirements of large recommendation models, Magnus integrates vector and inverted indexes directly into the data lake. It introduces a specialized “Blob format” to handle media files and optimizes for the “wide tables” common in machine learning, allowing complex retrieval via SQL without reliance on external vector databases.
Deep Lake 4.0 (Activeloop): This platform targets the friction between storage and training frameworks. By offering zero-copy dataloaders for PyTorch and TensorFlow, Deep Lake allows models to train directly on remote datasets. Its philosophy treats “data as code,” incorporating version control and CI/CD integration to ensure reproducibility in end-to-end pipelines.
Pixeltable: Pixeltable reimagines the AI pipeline as a database problem. It employs a declarative interface where transformations — such as video transcription or embedding generation — are defined as “computed columns” within a schema. Built on PostgreSQL and pgvector, it automates dependency tracking and incremental updates, significantly reducing the need for bespoke orchestration code.
Daft + Unity Catalog: Daft is a Rust-based distributed query engine designed for multimodal workloads. When paired with Databricks’ Unity Catalog, it addresses the challenge of “data inflation” — such as decoding millions of images from URLs — using dynamic streaming execution to prevent memory overloads, while also managing GPU resource scheduling.
These systems point in the same direction as LanceDB: unified handling of multimodal content, vector search, and tabular metadata. The differences are in packaging and emphasis — internal versus open-source, focus on training versus serving, or stronger orientation toward orchestration versus storage format.
Synergy with the PARK Stack
Taken together, Lance (the format) and LanceDB (the multimodal lakehouse) are a maturing and proven platform for teams that want a general-purpose data layer for multimodal AI. The core is open source, governed via a community model, and already deployed in demanding environments across media, developer tools, and generative AI products. For most teams, the main architectural question is “How does a multimodal lakehouse fit into the rest of my AI platform?”
One useful lens is the PARK stack: PyTorch, AI frontier models, Ray, and Kubernetes. The role of Lance and LanceDB is as the multimodal storage and retrieval substrate under that compute stack. PyTorch and your foundation models handle learning and inference. Ray orchestrates heterogeneous workloads — CPUs for metadata transforms, GPUs for embedding computation or decoding video — and Kubernetes provides cluster-level resource management. LanceDB sits underneath as the place where raw media, embeddings, and features live, with Ray Data or similar libraries reading from and writing to Lance tables as part of training and serving pipelines. This is already reflected in practice: LanceDB has native integrations with Ray to distribute queries and vector workloads across a cluster, and its design assumes object storage and decoupled compute.
For AI teams, the practical takeaway is that you can combine open-source compute (PARK) with an open multimodal storage layer (Lance + LanceDB) and specialized engines where necessary. As Netflix recently demonstrated, this modular approach allows teams to leverage Ray for elastic batch inference and LanceDB for zero-copy data evolution, enabling the efficient curation and querying of petabyte-scale multimodal datasets without the overhead of traditional data lakes.
