Native Vector Search. No Elasticsearch Overhead.

If you already run an Elasticsearch cluster and you’re trying to layer vector search on top, you know the trade-offs: JVM tuning, shard strategy, index templates, re-indexing, and an operational footprint that grows with every new use case.

LanceDB is an AI-native storage engine with native vector search built in. Instead of bolting vectors onto a text-first system, you get a retrieval layer designed for embeddings, multimodal data, and RAG from day one—with stateless query services that scale independently on top of your lake or object storage, so you’re not managing another always-on cluster or paying to store the same data twice. Under the hood, LanceDB is built on the open Lance format, optimized for fast random access and multimodal tables—so retrieval stays predictable as data grows.

Elastic AI Search vs an AI-Native Storage Engine

Elastic AI Search sits on top of Elasticsearch (Lucene). It’s excellent for text search over logs and JSON documents, but vector search typically inherits the same operational model: shard layouts, lifecycle policies, and costly re-index cycles as your embeddings and schema evolve.

LanceDB starts from a different place: a storage + retrieval layer built for modern AI access patterns (random access + multimodal + evolving schemas), without Elasticsearch’s cluster overhead.

What that usually looks like:

• Vectors are just another field type inside a text-first index.
• You still own Java heaps, shard layouts, index lifecycles, and re-index operations.
• Rolling out new embeddings or schema changes often means rewriting large indexes and rebalancing shards.

LanceDB starts from a different place:

• Built on the Lance columnar format, designed for vectors, features, and multimodal data from day one.
• Vector search, metadata, and filtering are first-class concepts, not add-ons to Lucene.
• The same Lance tables can back training runs, evaluation jobs, and online retrieval, instead of keeping a separate “search copy” of your data.

LanceDB is designed for random access and schema evolution on top of object storage, so you don’t inherit Elasticsearch’s always-on cluster model just to serve vector retrieval.

Elastic Pricing vs LanceDB TCO

When teams dig into Elastic Pricing, they find the main cost isn’t just the line item, it’s the shape of the deployment.

With a typical Elasticsearch setup:

• You provision clusters sized for peak load and high availability.
• You pay for those nodes 24/7, even when traffic is low.
• Each index is stored on disk with one or more replicas, so the same data is kept multiple times inside the cluster.
• In many stacks, you also keep a full copy in your data lake or object store, so you’re effectively paying to store the same payload twice in different formats.
• Upgrades, re-indexing, and shard rebalancing are your responsibility, whether you’re self-managed or paying against elastic.co pricing in a managed tier.

LanceDB is designed to change that cost profile:

• Serverless compute – capacity scales with query volume, not renting idle nodes just in case.
• Storage on your lake or object store – embeddings and metadata live in compact columnar files alongside the rest of your data, not in a separate proprietary storage tier.
• No extra shard replicas inside a closed cluster – durability comes from the underlying object storage; you’re not tuning replica counts for safety.
• Less re-indexing churn – schema and embedding updates don’t require rewriting large indices, which reduces both storage churn and engineering time.

In short: Elasticsearch assumes the cluster is the database. LanceDB assumes your lake or object store is the database, and the vector service sits on top of it. Total cost of ownership follows from that design choice.

Store More Than Text

Elasticsearch is a strong choice when your main job is indexing JSON logs and documents. But once you move into multimodal AI (images, video frames, audio segments, and structured features) you’re forcing a text-first system to behave like an AI storage layer.

LanceDB is built for multimodal retrieval:

• Store the actual data (including blobs), embeddings, and metadata together in the same Lance table
• Multiple vector columns per row (e.g., text + image embeddings) as your models and modalities expand
• Metadata and structured features alongside vectors for filtering, ranking, and analysis

That gives you concrete advantages in production:

• One system for storing and retrieving the data your AI workloads actually use, not vectors plus pointers to “real data” elsewhere
• A single table layout that supports multimodal RAG, recommendation, and similarity search plus offline jobs like re-embedding and feature engineering
• Built on an open format, so the same data works in embedded mode, in your own infrastructure, or in a managed LanceDB deployment, without rewriting or exporting

Because LanceDB can sit directly on your lake or object storage, you keep a single canonical copy of your data and avoid unnecessary duplication across separate indexing stacks.

What Engineers See in Practice

“Lance has been a significant enabler for our multimodal data workflows. Its performance and feature set offer a dramatic step up from legacy formats like WebDataset and Parquet. Using Lance has freed up considerable time and energy for our team, allowing us to iterate faster and focus more on research.”
- Keunhong Park, Member of Technical Staff, World Labs

Upgrade Your Search Stack

If you’ve pushed Elasticsearch as far as it reasonably goes for vector and AI use cases, the next step isn’t more shards or a bigger heap. It’s moving to an engine that was built for embeddings and multimodal data from the start.

LanceDB gives you:

• Native vector search and retrieval instead of an add-on to a text index.
• Stateless, managed query services instead of long-lived clusters and shard-level operations.
• A cost structure tied to your stored data and actual traffic, not fixed node counts and duplicated storage.

Use the Get a Demo form on this page to walk through what a move from your current Elastic setup to LanceDB would look like: data model, migration approach, performance expectations, and total cost over time.