Native Vector Search. No OpenSearch Overhead.

If you’re running OpenSearch today and pushing it into RAG or recommendation workloads with OpenSearch Vector Search, you’re still dealing with the same things: JVM nodes to tune, shards to juggle, re-indexing when mappings change, and clusters sized for peak load. Whether you picked opensearch vs elasticsearch, the core is a Lucene-based text engine with vectors added on.

LanceDB is an AI-native, serverless vector database, optimized for random access, shuffling, and column pruning. It stores vectors and metadata in Lance, a columnar lakehouse format, on your data lake or object store and uses stateless query services that scale with traffic instead of long-lived, fixed OpenSearch clusters. The engine and Lance file format are open source (Apache-2.0), so the same stack you can run yourself is what powers any managed LanceDB service.

Why OpenSearch Vector Search Falls Short

OpenSearch was originally designed to be good at logs and full-text search. For vector workloads, its Lucene roots become apparent:

• You still provision and tune JVM nodes, set heap sizes, and monitor cluster health.
• You micro-manage shards and replicas to stay within latency and durability targets.
• You re-index large datasets when schemas or embedding models change.

LanceDB starts from vectors and multimodal data:

• Built on the Lance columnar format, which collocates embeddings, metadata, and large large binary blob data
• Storage is decoupled from compute: data lives in your lake or object store; query services are stateless.
• The same tables can serve online vector search, training pipelines, and evaluation runs.
• Because the core engine is open source, you can adopt LanceDB incrementally (embedded, self-hosted, or managed) without worries of being “locked-in”.

Instead of stretching OpenSearch Vector Search into an AI database, you use a system built for AI workloads first.

OpenSearch Analytics without the Cluster

Many teams adopt OpenSearch Analytics because they like being able to slice and query data where it’s indexed. The trade-off is that analytics, search, and vector workloads all sit on the same cluster:

• Every dashboard and analytics query runs on nodes sized for peak search traffic.
• You often keep another copy of the data in S3 or your lake for other tools, effectively paying twice for storage.
• Scaling analytics means scaling the entire OpenSearch deployment.

With LanceDB:

• Data is stored as columnar files on your lake or object storage, so your existing analytics and data tools can read it directly.
• Vector search capacity is just another stateless service layer; you scale it separately from storage.
• You avoid maintaining both an OpenSearch cluster and a separate lake copy just to keep data queryable.

You keep the ability to analyze and inspect your data, without tying every query to a heavyweight search cluster.

OpenSearch SQL on Vectors and Metadata

OpenSearch SQL is attractive because it gives you familiar syntax over your indices, but it’s still bound to the same JVM cluster and index structures.

With LanceDB:

• In LanceDB Enterprise, Vectors and metadata are first-class columns and can be exposed to SQL-style querying, so you can inspect datasets and debug retrieval over the same tables you serve from.
• Because storage lives in your lake or object store, SQL and analytics can also run in the engines you already use there, not only through an OpenSearch SQL endpoint.
• You’re not forced to route all queries through a single OpenSearch SQL front-end just to keep a unified view.

You keep the “queryable database” experience that drew you to OpenSearch SQL, but with a storage and execution model aligned to modern AI stacks.

Beyond the OpenSearch Dashboard

OpenSearch Dashboard works well enough for logs and simple charts. For AI workloads, it’s limiting:

• Visuals are index- and document-centric, not embedding- or model-centric.
• It’s awkward to explore nearest neighbours, multimodal records, or RAG behavior over time.
• Integrations lean toward the OpenSearch ecosystem, not the wider BI and notebook tools you use for ML.

With LanceDB:

• Embeddings, metadata, and the actual raw multimodal data (including blobs) live in one table, so the same dataset can be queried, audited, and visualized in standard BI and data science tools.
• You can inspect neighbors, filters, and score distributions in the same environment you use for evaluation and model metrics.
• Keep logs in OpenSearch; move AI retrieval data to a storage engine tuned for large multimodal datasets and high-throughput random access without being locked to what OpenSearch Dashboards exposes.

You’re not constrained by what the OpenSearch Dashboard happens to expose about your vector workloads.

OpenSearch Plugin vs Native Architecture

Adding vectors via the k-NN opensearch plugin keeps everything in one system, but it doesn’t change the fundamentals:

• Vectors still inherit shard, replica, and re-index behavior from a text/log engine.
• You manage plugin versions and compatibility alongside cluster upgrades.
• Storage stays index-centric and cluster-bound instead of lake-centric.

LanceDB is a native vector architecture:

• Embeddings, metadata, and multimodal fields are first-class, columnar data, not plugin-defined extras.
• Vector similarity, filtering, and (in Enterprise) SQL-style access are part of the engine, not layered in via an OpenSearch plugin.
• Storage remains in your lake or object store; query capacity can be embedded, self-hosted, or managed and scaled separately.
• The open source core means the same engine underlies all those deployment options; you’re not tied to a single vendor cluster.

You move from patching a text engine into a vector database to using a system where vectors and AI workloads are the main design target.

Trusted by AI Teams at Scale

“LanceDB has been an incredibly useful tool in Character.ai’s petabyte-scale data lake… we can iterate quickly and efficiently.”
- Ryan Vilim, Member of Technical Staff, Character.ai

Teams shifting from log- and text-centric stacks to AI-centric data platforms choose LanceDB when they want the storage engine and retrieval layer to match how they actually build and train models.

Upgrade Your Search Stack

If you’re using OpenSearch Vector Search to stretch a log and text engine into an AI database, you’re carrying more cluster work and cost than you need.

LanceDB gives you:

• Native vector search and multimodal storage on Lance—an open-source lakehouse format for multimodal AI
• A serverless, stateless query layer that scales with traffic instead of fixed OpenSearch nodes
• One set of tables for training, evaluation, and retrieval (no separate “offline” and “online” copies)
• Built for datasets bigger than RAM—scaling from multi-TB up to petabyte lake scale on object storage