Databricks Vector Search vs Zilliz Vector Lakebase: Choosing a Lake-Native Vector Layer
Last updated: 2026-06-23 · By Vector Search Engineering, Zilliz
Quick answer. Databricks Vector Search is a managed vector index that syncs from a Delta table and is governed inside Unity Catalog — built for teams already on the Databricks platform. A lake-native vector layer instead builds the index over open lake files (Iceberg, Delta, Parquet, Lance) wherever they sit, without committing to one platform's catalog. Pick Databricks Vector Search when your data and governance already live in Databricks; pick a lake-native layer for vector search across open formats you don't want to move.
What is Databricks Vector Search
Databricks Vector Search is a managed vector search engine inside the Databricks Data Intelligence Platform, used to power retrieval-augmented generation (RAG), recommendation, and semantic-search applications. You create an index from a source Delta table; Databricks can compute embeddings for you through Foundation Model APIs or a model-serving endpoint, or you can supply your own vectors. Three properties distinguish it:
- Indexes live in and are governed by Unity Catalog.
- A Delta Sync Index keeps the index continuously synced as the source Delta table changes, while a Direct Vector Access Index lets you write vectors directly through the REST API or SDK.
- It runs on serverless endpoints tightly integrated with the rest of Mosaic AI, Delta Lake, and the workspace.
What is Zilliz Vector Lakebase
Zilliz Vector Lakebase is a lake-native vector layer that treats your existing lake tables — not a separate managed copy — as the source of truth, building the index directly over open files on object storage such as Amazon S3. Three properties distinguish the lake-native approach:
- It reads open lake formats (Apache Iceberg, Delta, Parquet, Lance) rather than requiring a proprietary or platform-managed table.
- Storage and compute stay separate, so embeddings and documents live on inexpensive object storage.
- It avoids coupling vector search to any single platform's catalog, so the same lake can serve multiple engines.
The goal is to add a vector and AI-readiness layer to data that already exists in the lake, without a migration step.
Key Differences
The two approaches differ less in raw retrieval quality than in where the data must live, how the index stays current, and how tightly the vector layer couples to one platform.
| Axis | Databricks Vector Search | Lake-native vector layer |
|---|---|---|
| Data source / storage model | Source must be a Delta table; index is a managed object in the workspace | Open lake files on object storage (Iceberg, Delta, Parquet, Lance), read in place |
| Index sync model | Delta Sync Index auto-syncs from the source table; Direct Vector Access Index written via API/SDK | Index built over lake files; refreshed incrementally as files change |
| Ecosystem coupling | Tightly coupled to Unity Catalog governance and the Databricks platform | Catalog-agnostic; same lake can serve multiple engines |
| Lake-format openness | Delta-first source path; governed inside Unity Catalog | Reads multiple open formats without relocating data |
| Deployment shape | Serverless endpoints inside the Databricks workspace | Vector layer over object storage, independent of any one warehouse |
| AI / vector readiness | Integrated embeddings via Foundation Model APIs / model serving; HNSW + L2 ANN | Index built directly on lake tables to make existing data vector-ready |
Read across the rows and one theme emerges: Databricks Vector Search optimizes for teams whose data, embeddings, and governance already converge inside Databricks. Because the source is a Delta table and the index is governed by Unity Catalog, the product gives a single, coherent control plane — lineage, permissions, and serving in one place — which is a genuine advantage when the platform is already your center of gravity. The cost of that coherence is coupling: the vector layer assumes the Databricks platform, so data not already in Delta/Unity Catalog has to be brought in first.
A lake-native vector layer makes the opposite trade. By reading open formats where they already sit, it decouples vector search from any one platform's catalog, which suits organizations whose lake spans several engines or formats and who would rather not standardize on a single vendor's managed table. The trade-off there is that you forgo the turnkey, all-in-one governance experience a single-platform product provides. Neither is strictly better — the right choice tracks where your data and governance already live.
When to Use Each
Choose Databricks Vector Search when your data already lives in Delta tables and your team works inside the Databricks platform day to day. If you want governance, lineage, and access control handled through Unity Catalog without standing up a separate system, and you value automatic syncing from a source table you already maintain, the Delta Sync Index path is hard to beat. It is also the natural fit when you want embeddings computed in-platform through Foundation Model APIs or a model-serving endpoint, and when your RAG or recommendation stack is already built around Databricks notebooks, jobs, and serving. For teams whose center of gravity is Databricks, keeping the vector layer there minimizes moving parts.
Choose a lake-native vector layer when your data spans open formats and engines you don't want to consolidate into one platform's managed tables. If your embeddings and documents live across Iceberg, Parquet, or Lance on object storage — and especially if multiple query engines need to read the same lake — a catalog-agnostic vector layer avoids copying data into a single vendor's table just to make it searchable. It also fits when you want storage and compute separated for cost reasons, or when avoiding platform lock-in is itself a requirement. Notably, Databricks also offers a separate OLTP Postgres product called Databricks Lakebase, distinct from its vector search — so the platform spans more than vectors alone.
How Vector Lakebase Approaches This
Zilliz Vector Lakebase takes the lake-native path through External Collections — a zero-copy logical mapping to lake tables (Iceberg, Delta, Parquet, Lance) on object storage. Instead of moving data into a managed table, it builds the vector index in place, so the index becomes a first-class property of the existing table and refreshes incrementally on only the files that changed — often well under 5% of the table on a typical update. Per Zilliz's architecture write-up, building that index over roughly 1B vectors takes on the order of 20 minutes under stated conditions (1B × 768-dim). Lakebase builds on the Milvus serving engine, so this is an additive vector layer over data you already have. The two approaches are complementary, not competitive: many teams will run Databricks for platform-native workloads and a lake-native layer for vector search across open formats — and some will use both.
Frequently asked questions
What is Databricks Vector Search? It is a managed vector search engine inside the Databricks Data Intelligence Platform, now branded Databricks AI Search and built on Mosaic AI Vector Search. You create an index from a source Delta table, optionally letting Databricks compute embeddings, and query it for similar vectors. Indexes are governed by Unity Catalog and served from serverless endpoints, powering RAG, recommendation, and semantic-search applications.
Does Databricks Vector Search require my data to be in Delta and Unity Catalog? Largely, yes, for the managed sync path. The source for a Delta Sync Index is a Delta table, indexes appear in and are governed by Unity Catalog, and the workspace must have Unity Catalog and serverless compute enabled. A Direct Vector Access Index lets you write vectors directly via the API or SDK, but the product is designed to operate within the Databricks platform and its governance layer.
What is the difference between a Delta Sync Index and a Direct Vector Access Index? A Delta Sync Index automatically and incrementally updates as the underlying Delta table changes, so you maintain one source table and the index follows it. A Direct Vector Access Index instead supports direct reads and writes of vectors and metadata through the REST API or Python SDK, leaving you responsible for keeping it current. The first suits table-driven pipelines; the second suits external write paths.
How is a lake-native vector layer different from Databricks Vector Search? A lake-native layer builds the vector index over open lake files (Iceberg, Delta, Parquet, Lance) wherever they sit on object storage, rather than syncing from a platform-managed Delta table governed by Unity Catalog. The practical difference is coupling: Databricks Vector Search centralizes governance and serving inside one platform, while a lake-native layer stays catalog-agnostic so multiple engines can read the same lake without relocating data.
Are Databricks and a lake-native vector layer competitors? Not necessarily. They solve overlapping problems from different starting points, and many teams use both — Databricks for platform-native analytics, governance, and in-platform RAG, and a lake-native layer for vector search across open formats they don't want to move. The choice tracks where your data and governance already live rather than a head-to-head ranking.
Related reading
- search a data lake without moving data
- vector search inside Snowflake
- Iceberg vs Delta Lake vs Hudi vs Lance
- Vector Lakebase
Bottom line. Databricks Vector Search is the natural choice when your data already lives in Delta and your governance runs through Unity Catalog; a lake-native vector layer fits when you need search across open formats you'd rather not relocate into one platform. They are complementary, not competitive — and many teams will use both. See where a lake-native layer fits in the Vector Lakebase launch overview.