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docs: add an Introduction nav section with a dedicated Embedded vs Enterprise comparison (#4829)
## Summary - Adds a new top-level "Introduction" nav section before "Getting Started": LanceDB, LanceDB Embedded, LanceDB Enterprise, Embedded vs Enterprise. - Trims the landing page's combined Architecture/Embedded/Enterprise/Compare block down to a short pointer section (keeping its `#architecture` anchor), moving that content into two new pages: `basics/lancedb-embedded.mdx` and `basics/embedded-vs-enterprise.mdx`. - `basics/embedded-vs-enterprise.mdx` consolidates the feature-comparison table and the Enterprise overview's own comparison content (previously duplicated across multiple pages and, within this page, repeated across a dimension table, several narrative subsections, and a usage-differences list) into one comparison page with each section covering a distinct angle. - Repoints the `/enterprise` and `/enterprise/deployment` redirects at `/basics/embedded-vs-enterprise`, since the previous destination only resolves once the Enterprise overlay is applied and fails `rust/lancedb/src/docs.rs`'s link-resolution test against this repo's own `docs/web` tree. ## Companion change lancedb/sophon#9718 trims the Enterprise overview page to drop the content that moved here, and relocates its nav insertion into the new Introduction group. Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
Jack Ye
co-authored by
Claude Sonnet 5
Claude Sonnet 5
parent
d0b0efcd47
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---
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title: "Embedded vs Enterprise"
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sidebarTitle: "Embedded vs Enterprise"
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description: "A feature-by-feature comparison between LanceDB Embedded and LanceDB Enterprise."
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icon: "scale-balanced"
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keywords: ["embedded vs enterprise", "comparison", "oss vs enterprise"]
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---
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LanceDB Embedded runs in your application's process; your data can live on local disk or in object storage.
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[LanceDB Enterprise](/enterprise/index) distributes query execution, indexing, maintenance and refresh work across
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a cluster of many machines, outside your application. Both are built on the same [Lance](/basics/lance) columnar
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file format, so moving data from one edition to the other does not require a data conversion step. See
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[Architecture](/index#architecture) for how each one's components fit together.
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## How they differ {#how-they-differ}
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| Dimension | LanceDB Embedded | LanceDB Enterprise | What the difference means |
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|:----------|:------------|:-------------------|:-------------------------|
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| **Mode** | Single process | Distributed fleet | LanceDB Embedded runs on one host, so scale is limited to what it can provide. Enterprise spreads work across nodes, adds capacity by adding nodes, and keeps serving even if one node fails. |
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| **Query serving** | Uses the resources of your application process | Adds independently scaled query compute | Size the Enterprise cluster for your workload and latency target. See the expected-performance sections on [vector search](/search/vector-search#expected-performance) and [full-text search](/search/full-text-search#expected-performance) for representative measurements. |
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| **Cache** | In-process index and metadata caches | Adds a distributed NVMe data cache | LanceDB Embedded caches indexes and metadata in your application's memory. Enterprise also keeps hot table data near compute on NVMe and avoids repeated S3 calls -- see the [Data Page Cache](/enterprise/data-page-cache). |
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| **Indexing & compaction** | Manual | Platform-managed workflows | LanceDB Embedded requires application-managed maintenance; Enterprise schedules it in the background. See [Indexing in LanceDB](/indexing/). |
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| **Data format** | Lance | Lance | Both use the same open table format. Plan catalog registration, access and transfer when moving existing data. |
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| **Deployment** | Embedded in your code | BYOC or Managed | Enterprise adds deployment operations and support for shared workloads. |
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| **SQL analytics** | Not available | Arrow Flight SQL | Enterprise runs joins, grouping, sampling and [curation](/curation) over SQL. See [SQL reference](/sql). |
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| **Native vector dedup** | Not available | `dedupe()` / `duplicate_pairs()` | Index-partition deduplication as a SQL table function, with no full pairwise scan. See [DEDUPE](/sql/dql/dedupe). |
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| **Materialized views** | Not available | Server-side, distributed compute | Enterprise derives and refreshes a table from a query as a background job. See [Materialized views](/feature-engineering/materialized-views). |
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## Compare features {#compare}
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| Area | Feature | Embedded | Enterprise |
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| --- | --- | :-: | :-: |
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| **Data Ingestion** | [Insert data](/ingestion/insert) | ✅ | ✅ |
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| | [Merge](/ingestion/merge-insert) | ✅ | ✅ |
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| | [RTE Write](/ingestion/realtime-write) | ✅ | ✅ |
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| | [Distributed RTE Write](/enterprise/distributed-real-time-write) | ❌ | ✅ |
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| | [Attach Database](/ingestion/migrate-from-embedded) | ❌ | ✅ |
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| | [Import Datasets](/ingestion/import) | ❌ | ✅ |
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| | [Distributed Write](/enterprise/distributed-write) | ❌ | ✅ |
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| **Semantic Search** | [Vector search](/search/vector-search) | ✅ | ✅ |
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| | [Full-text search](/search/full-text-search) | ✅ | ✅ |
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| | [Hybrid search](/search/hybrid-search) | ✅ | ✅ |
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| | [Filtering](/search/filtering) | ✅ | ✅ |
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| | [Reranking](/reranking) | ✅ | ✅ |
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| | [Distributed search](/enterprise/distributed-search) | ❌ | ✅ |
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| | [RTE Read](/search/realtime-read) | ✅ | ✅ |
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| | [Distributed RTE Read](/enterprise/distributed-real-time-read) | ❌ | ✅ |
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| | [Cache Prewarm](/enterprise/cache-prewarm) | ❌ | ✅ |
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| **Data Curation** | [Inspect Datasets](/curation/inspect-datasets) | ❌ | ✅ |
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| | [Statistical Sampling](/curation/statistical-sampling) | ❌ | ✅ |
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| | [Selective Sampling](/curation/selective-sampling) | ✅ | ✅ |
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| | [Aggregation and profiling](/curation/aggregate) | ❌ | ✅ |
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| | [Joins](/curation/join) | ❌ | ✅ |
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| | [Blob Late Materialization](/curation/blob-late-materialization) | ❌ | ✅ |
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| | [Ranking and deduplication](/curation/windowing) | ❌ | ✅ |
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| | [Near-duplicate detection](/curation/dedupe) | ❌ | ✅ |
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| | [Chunking](/curation/chunking) | ❌ | ✅ |
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| | [Changes since a version](/curation/changes) | ❌ | ✅ |
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| | [GPU Function Execution](/curation/gpu-function-execution) | ❌ | ✅ |
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| | [Slurm Scheduling](/curation/slurm-scheduling) | ❌ | ✅ |
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| **Feature Engineering** | [Computed columns](/feature-engineering/add-computed-column) | ❌ | ✅ |
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| | [Incremental Refresh](/enterprise/incremental-refresh) | ❌ | ✅ |
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| | [Auto Refresh](/enterprise/auto-refresh) | ❌ | ✅ |
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| | [Checkpointing](/enterprise/checkpointing) | ❌ | ✅ |
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| | [Adaptive Execution](/enterprise/adaptive-execution) | ❌ | ✅ |
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| | [GPU Refresh Execution](/enterprise/gpu-refresh-execution) | ❌ | ✅ |
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| | [Slurm Scheduling](/enterprise/slurm-refresh-scheduling) | ❌ | ✅ |
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| | [Refresh Materialized View](/feature-engineering/refresh-materialized-view) | ❌ | ✅ |
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| | [Embedding registry](/embedding) | ✅ | ✅ |
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| **Training** | [PyTorch data loader](/training/data-loading) | ✅ | ✅ |
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| | [Training Cache](/training/training-cache) | ❌ | ✅ |
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| | [Permutations](/training/permutation) | ✅ | ✅ |
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| | [Distributed training](/training/distributed-training) | ✅ | ✅ |
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| | [Observability](/training/observability) | ✅ | ✅ |
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| | [Checkpointing](/training/checkpointing) | ✅ | ✅ |
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| | [Resuming](/training/resuming) | ✅ | ✅ |
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| **Management** | [Catalog](/management/catalog) | ❌ | ✅ |
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| | [Database](/management/database) | ✅ | ✅ |
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| | [Namespace](/management/namespace) | ✅ | ✅ |
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| | [Table](/management/table) | ✅ | ✅ |
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| | [Branches](/tables/branching) | ✅ | ✅ |
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| | [View](/management/view) | ❌ | ✅ |
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| | [Materialized view](/management/materialized-view) | ❌ | ✅ |
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| | [Secret](/management/secret) | ❌ | ✅ |
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| | [Function](/management/function) | ❌ | ✅ |
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| | [Storage integration](/management/storage-integration) | ❌ | ✅ |
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| **Maintenance** | [Intelligent Auto-Maintenance](/enterprise/intelligent-auto-maintenance) | ❌ | ✅ |
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| | [Vector Indexing](/indexing/vector-index) | ✅ | ✅ |
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| | [FTS Indexing](/indexing/fts-index) | ✅ | ✅ |
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| | [Scalar Indexing](/indexing/scalar-index) | ✅ | ✅ |
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| | [Incremental Indexing](/indexing/incremental-indexing) | ✅ | ✅ |
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| | [Distributed Indexing](/enterprise/distributed-indexing) | ❌ | ✅ |
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| | [GPU Indexing](/indexing/gpu-indexing) | ✅ | ✅ |
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| | [Compaction](/maintenance/compaction) | ✅ | ✅ |
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| | [RTE Compaction](/enterprise/realtime-engine-compaction) | ❌ | ✅ |
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| | [Garbage Collection](/maintenance/cleanup) | ✅ | ✅ |
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| | [Distributed compaction](/enterprise/distributed-compaction) | ❌ | ✅ |
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| | [Advanced Conflict Resolution](/enterprise/advanced-conflict-resolution) | ❌ | ✅ |
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| **Security** | [Authentication](/enterprise/authentication) | ❌ | ✅ |
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| | [Authorization](/governance/authorization) | ❌ | ✅ |
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| | [Audit Log](/governance/audit-log) | ❌ | ✅ |
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| **Deployment** | [Horizontal Scaling](/enterprise/horizontal-scaling) | ❌ | ✅ |
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| | [OpenTelemetry](/deployment/opentelemetry) | ✅ | ✅ |
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| | [Kubernetes](/enterprise/deployment/kubernetes) | ❌ | ✅ |
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## Usage differences between Enterprise and Embedded {#usage-differences-between-enterprise-and-oss}
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The [quickstart](/quickstart) guide shows both LanceDB Embedded connections and Enterprise `db://...`
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connections. Once connected to LanceDB, the table API is largely the same: create a table, search,
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filter, evolve the schema, and store multimodal records. However, there are some semantic differences
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worth understanding when your code is talking to LanceDB Enterprise.
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### 1\. Connection model {#1-connection-model}
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In LanceDB Enterprise, your app connects via a `db://...` URI and sends requests to the cluster API.
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The cluster executes table operations on your behalf. Your code is coupled to a **managed service endpoint**,
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whereas LanceDB Embedded is directly coupled to local or object-storage paths.
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### 2\. Returned table type {#2-returned-table-type}
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Connecting to an Enterprise table via `open_table(...)` returns a `RemoteTable`, unlike LanceDB Embedded,
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which returns a `LanceTable`. `RemoteTable` is a catalog-backed table accessed through a server/cluster,
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and does not support all the same methods as `LanceTable` (see below).
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### 3\. Materialization APIs {#3-materialization-apis}
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For Python users working with LanceDB Enterprise, `RemoteTable` does not support table-level
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materialization methods like `table.to_arrow()` or `table.to_pandas()`. This protects users from
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accidentally materializing tables that are too large to fit in memory.
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Instead, materialize results through query/search builders, for example
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`table.search(...).limit(...).to_pandas()` for a vector or full-text search, or
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`table.search().where(...).limit(...).to_arrow()` for a filtered scan. For quick previews, use
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`table.head()`.
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### 4\. Maintenance lifecycle {#4-maintenance-lifecycle}
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In LanceDB Embedded, you run maintenance yourself: `optimize()` compacts data files, adds new rows to
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existing indexes, and removes old table versions. In Enterprise, the cluster runs this work -- along with
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async write and reindexing workflows -- as background jobs, and the SDK's maintenance calls do not trigger
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it. On an Enterprise table, the synchronous Python `optimize()`, `compact_files()`, and
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`cleanup_old_versions()` only emit a warning, and the asynchronous Python, TypeScript, and Rust clients
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reject `optimize()` as not supported.
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That means maintenance is managed by platform behavior and cluster configuration, not by explicit
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per-table maintenance calls in your application code.
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### 5\. Guardrails and limits {#5-guardrails-and-limits}
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Enterprise can enforce platform-level guardrails, such as index/table limits and safety checks around
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operations like `merge_insert` when too many rows are unindexed. LanceDB Embedded mostly exposes
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storage/format-level behavior, and you tune many lifecycle tasks yourself.
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This means an operation in LanceDB Enterprise can fail due to service-level policy, not just because
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of local table shape or schema mismatch.
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<Info>
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As a rule of thumb, all you need to remember is this: treat `db://...` as a network service boundary,
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use query builders to fetch results, and otherwise interact with your tables as you would in LanceDB
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Embedded.
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</Info>
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## Which one should I use? {#which-one-should-i-use}
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[It's very simple to get started with LanceDB Embedded](/quickstart/): Get started with `pip install lancedb` and begin ingesting
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your data and vectors into LanceDB. LanceDB Embedded (open source) makes sense when your dataset fits on one machine, traffic is still
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modest, and your team is comfortable handling maintenance tasks such as compaction or reindexing itself.
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Move to LanceDB Enterprise when retrieval becomes shared infrastructure for your business: your data or traffic has
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outgrown a single machine, you need private deployment options, or you want platform support for monitoring, security,
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and large-scale feature workflows.
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If these sound like your use cases, [reach out to us](https://www.lancedb.com/contact) and we can help you scope your workload and arrange an Enterprise proof of concept.
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@@ -0,0 +1,46 @@
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---
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title: "LanceDB Embedded"
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sidebarTitle: "LanceDB Embedded"
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description: "The open-source, embedded retrieval library that runs inside your application process."
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icon: cube
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keywords: ["embedded", "open source", "oss", "local", "sdk", "lancedb embedded"]
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---
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**LanceDB Embedded** is an open-source, embedded retrieval library, with client SDKs in Python, TypeScript and
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Rust -- the application itself plans queries, builds indexes, and runs background work, with no separate database
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server to deploy. It reads and writes tables on local disk or in object storage from inside your application
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process, on the same [Lance](/basics/lance) format and table model that [LanceDB Enterprise](/enterprise/index)
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builds on.
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<Frame caption="LanceDB Embedded: the LanceDB SDK and the Lance format run in your application, reading and writing tables on local disk or in object storage.">
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<img src="/static/assets/images/overview/lancedb-embedded.png" alt="LanceDB Embedded architecture" width="440" />
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</Frame>
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## Why use LanceDB Embedded? {#why-use-lancedb-embedded}
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LanceDB Embedded fits local retrieval applications, notebooks, development, and single-node data pipelines, where
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your application controls its own compute resources and [table maintenance](/maintenance/compaction).
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- **No infrastructure to run** -- the library runs inside your process. There is no server to deploy, configure,
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or keep available.
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- **Free and open source** -- `pip install lancedb` (or the TypeScript or Rust equivalent) and start reading and
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writing tables, with no account or service to set up first.
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- **Full local control** -- your application decides where table data lives, when maintenance such as compaction
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and indexing runs, and how much compute each operation gets.
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- **The same table format as Enterprise** -- Embedded and [Enterprise](/enterprise/index) share the same Lance
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format and table model, so moving to a distributed deployment later does not require a data conversion step.
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## Get started {#get-started}
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<CardGroup cols={2}>
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<Card title="Quickstart" icon="rocket" href="/quickstart">
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Create a table, add data, and run your first search in a few lines of code.
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</Card>
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<Card title="Choose an SDK" icon="code-compare" href="/basics/sdk">
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Pick between the Python, TypeScript, and Rust SDKs.
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</Card>
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</CardGroup>
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When your data or traffic outgrows a single machine, or you need managed infrastructure, private deployment, or
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platform support, see [LanceDB Enterprise](/enterprise/index) and the
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[feature-by-feature comparison](/basics/embedded-vs-enterprise).
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+10
-3
@@ -41,7 +41,7 @@
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},
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{
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"source": "/enterprise",
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"destination": "/#architecture"
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"destination": "/basics/embedded-vs-enterprise"
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},
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{
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"source": "/enterprise/intelligent-scheduling",
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@@ -89,7 +89,7 @@
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},
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{
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"source": "/enterprise/deployment",
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"destination": "/#architecture"
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"destination": "/basics/embedded-vs-enterprise"
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},
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{
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"source": "/feature-engineering/data-evolution",
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@@ -370,9 +370,16 @@
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"tab": "Documentation",
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"groups": [
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{
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"group": "Getting Started",
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"group": "Introduction",
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"pages": [
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"index",
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"basics/lancedb-embedded",
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"basics/embedded-vs-enterprise"
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]
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},
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{
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"group": "Getting Started",
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"pages": [
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"quickstart",
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"basics/sdk",
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"basics/sql",
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+13
-127
@@ -63,137 +63,23 @@ through the same table model, whether a workflow uses one table or several relat
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LanceDB ships as two offerings on the same [Lance](/basics/lance) format and table model, which stores table data,
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versions, and indexes in open, columnar files. What changes between them is how storage and compute are organized
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around that format. Start locally with Embedded, then move to Enterprise when your team needs distributed scale,
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around that format: start locally with Embedded, then move to Enterprise when your team needs distributed scale,
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managed infrastructure, private deployment, or higher-throughput curation, feature engineering, search and
|
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retrieval, and training workflows.
|
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|
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<Columns cols={2}>
|
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<Frame caption="LanceDB Embedded: the LanceDB SDK and the Lance format run in your application, reading and writing tables on local disk or in object storage.">
|
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<img src="/static/assets/images/overview/lancedb-embedded.png" alt="LanceDB Embedded architecture" />
|
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</Frame>
|
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<Frame caption="LanceDB Enterprise: the LanceDB SDK connects to the Enterprise service, where a query fleet, executor and cache fleets, the realtime engine, and a jobs fleet run on top of the Lance format and object storage.">
|
||||
<img src="/static/assets/images/overview/lancedb-distributed.png" alt="LanceDB Enterprise architecture" />
|
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</Frame>
|
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</Columns>
|
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<CardGroup cols={2}>
|
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<Card title="LanceDB Embedded" icon="cube" href="/basics/lancedb-embedded">
|
||||
An open-source, embedded retrieval library that runs inside your application process, with no separate
|
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database server to deploy.
|
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</Card>
|
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<Card title="LanceDB Enterprise" icon="server" href="/enterprise/index">
|
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A distributed multimodal lakehouse service for production workloads that need managed infrastructure and
|
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higher-throughput workflows.
|
||||
</Card>
|
||||
</CardGroup>
|
||||
|
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### 1\. LanceDB Embedded {#1-lancedb-oss}
|
||||
|
||||
**LanceDB Embedded** is an open-source, embedded retrieval library, with client SDKs in Python, TypeScript and
|
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Rust -- the application itself plans queries, builds indexes, and runs background work, with no separate database
|
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server to deploy. It reads and writes tables on local disk or in object storage from inside your application
|
||||
process. This fits local retrieval applications, notebooks, development, and single-node data pipelines, where
|
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your application controls its own compute resources and [table maintenance](/maintenance/compaction).
|
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|
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### 2\. LanceDB Enterprise {#2-lancedb-enterprise}
|
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|
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**LanceDB Enterprise** is an exabyte-scale, distributed **multimodal lakehouse** service built on the
|
||||
same table abstraction as Embedded, for search, curation, feature engineering, and high-throughput training data
|
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access. Applications connect through a LanceDB SDK while the service plans and runs work across a fleet: query
|
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serving, read execution and caching, indexing, and background maintenance scale as separate components, with
|
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durable table data and index artifacts kept in object storage, independently of the machines serving requests.
|
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Caches accelerate reads; they are never the durable source of truth. With multiple serving replicas, Enterprise
|
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routes traffic to surviving nodes when one fails; losing a cache node temporarily raises latency while its
|
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replacement warms, rather than causing an outage. See [Distributed search](/enterprise/distributed-search),
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[distributed indexing](/enterprise/distributed-indexing), and the other architecture guides linked from the
|
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comparison table below for how work divides across machines.
|
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|
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Teams choose Enterprise to:
|
||||
|
||||
- **Get new capabilities first** -- SQL, Functions, materialized views, and most other features documented
|
||||
throughout these docs land in Enterprise first, often well before they reach Embedded, if they ever do.
|
||||
- **Performance** -- cache frequently read table data near compute with the [Data Page Cache](/enterprise/data-page-cache),
|
||||
and let [adaptive execution](/enterprise/adaptive-execution) size refresh and curation workers to the job instead
|
||||
of guessing once.
|
||||
- **Scale** -- add query-serving capacity by spreading a table across more [replica groups](/enterprise/horizontal-scaling)
|
||||
as traffic grows, instead of upgrading one machine.
|
||||
- **Fault tolerance** -- [replication](/enterprise/horizontal-scaling) keeps a table serving when a node fails, so
|
||||
you lose latency to a warm-up, not an outage.
|
||||
- **Multicloud deployment** -- bring LanceDB into your own cloud account, in the region your
|
||||
[GPU capacity](/curation/gpu-function-execution) already lives, instead of moving data to it.
|
||||
- **Simplify operations** -- [Intelligent Auto-Maintenance](/enterprise/intelligent-auto-maintenance) runs
|
||||
compaction, cleanup, and indexing in the background, with [OpenTelemetry](/deployment/opentelemetry) metrics and
|
||||
traces instead of guesswork.
|
||||
|
||||
[Reach out to us](https://www.lancedb.com/contact) to set up LanceDB Enterprise in your organization. Sharing a
|
||||
format does not mean an Enterprise deployment automatically opens an existing Embedded database -- plan how to
|
||||
move or connect your data.
|
||||
|
||||
### Compare features {#compare}
|
||||
|
||||
| Area | Feature | Embedded | Enterprise |
|
||||
| --- | --- | :-: | :-: |
|
||||
| **Data Ingestion** | [Insert data](/ingestion/insert) | ✅ | ✅ |
|
||||
| | [Merge](/ingestion/merge-insert) | ✅ | ✅ |
|
||||
| | [RTE Write](/ingestion/realtime-write) | ✅ | ✅ |
|
||||
| | [Distributed RTE Write](/enterprise/distributed-real-time-write) | ❌ | ✅ |
|
||||
| | [Attach Database](/ingestion/migrate-from-embedded) | ❌ | ✅ |
|
||||
| | [Import Datasets](/ingestion/import) | ❌ | ✅ |
|
||||
| | [Distributed Write](/enterprise/distributed-write) | ❌ | ✅ |
|
||||
| **Semantic Search** | [Vector search](/search/vector-search) | ✅ | ✅ |
|
||||
| | [Full-text search](/search/full-text-search) | ✅ | ✅ |
|
||||
| | [Hybrid search](/search/hybrid-search) | ✅ | ✅ |
|
||||
| | [Filtering](/search/filtering) | ✅ | ✅ |
|
||||
| | [Reranking](/reranking) | ✅ | ✅ |
|
||||
| | [Distributed search](/enterprise/distributed-search) | ❌ | ✅ |
|
||||
| | [RTE Read](/search/realtime-read) | ✅ | ✅ |
|
||||
| | [Distributed RTE Read](/enterprise/distributed-real-time-read) | ❌ | ✅ |
|
||||
| | [Cache Prewarm](/enterprise/cache-prewarm) | ❌ | ✅ |
|
||||
| **Data Curation** | [Inspect Datasets](/curation/inspect-datasets) | ❌ | ✅ |
|
||||
| | [Statistical Sampling](/curation/statistical-sampling) | ❌ | ✅ |
|
||||
| | [Selective Sampling](/curation/selective-sampling) | ✅ | ✅ |
|
||||
| | [Aggregation and profiling](/curation/aggregate) | ❌ | ✅ |
|
||||
| | [Joins](/curation/join) | ❌ | ✅ |
|
||||
| | [Blob Late Materialization](/curation/blob-late-materialization) | ❌ | ✅ |
|
||||
| | [Ranking and deduplication](/curation/windowing) | ❌ | ✅ |
|
||||
| | [Near-duplicate detection](/curation/dedupe) | ❌ | ✅ |
|
||||
| | [Chunking](/curation/chunking) | ❌ | ✅ |
|
||||
| | [Changes since a version](/curation/changes) | ❌ | ✅ |
|
||||
| | [GPU Function Execution](/curation/gpu-function-execution) | ❌ | ✅ |
|
||||
| | [Slurm Scheduling](/curation/slurm-scheduling) | ❌ | ✅ |
|
||||
| **Feature Engineering** | [Computed columns](/feature-engineering/add-computed-column) | ❌ | ✅ |
|
||||
| | [Incremental Refresh](/enterprise/incremental-refresh) | ❌ | ✅ |
|
||||
| | [Auto Refresh](/enterprise/auto-refresh) | ❌ | ✅ |
|
||||
| | [Checkpointing](/enterprise/checkpointing) | ❌ | ✅ |
|
||||
| | [Adaptive Execution](/enterprise/adaptive-execution) | ❌ | ✅ |
|
||||
| | [GPU Refresh Execution](/enterprise/gpu-refresh-execution) | ❌ | ✅ |
|
||||
| | [Slurm Scheduling](/enterprise/slurm-refresh-scheduling) | ❌ | ✅ |
|
||||
| | [Refresh Materialized View](/feature-engineering/refresh-materialized-view) | ❌ | ✅ |
|
||||
| | [Embedding registry](/embedding) | ✅ | ✅ |
|
||||
| **Training** | [PyTorch data loader](/training/data-loading) | ✅ | ✅ |
|
||||
| | [Training Cache](/training/training-cache) | ❌ | ✅ |
|
||||
| | [Permutations](/training/permutation) | ✅ | ✅ |
|
||||
| | [Distributed training](/training/distributed-training) | ✅ | ✅ |
|
||||
| | [Observability](/training/observability) | ✅ | ✅ |
|
||||
| | [Checkpointing](/training/checkpointing) | ✅ | ✅ |
|
||||
| | [Resuming](/training/resuming) | ✅ | ✅ |
|
||||
| **Management** | [Catalog](/management/catalog) | ❌ | ✅ |
|
||||
| | [Database](/management/database) | ✅ | ✅ |
|
||||
| | [Namespace](/management/namespace) | ✅ | ✅ |
|
||||
| | [Table](/management/table) | ✅ | ✅ |
|
||||
| | [Branches](/tables/branching) | ✅ | ✅ |
|
||||
| | [View](/management/view) | ❌ | ✅ |
|
||||
| | [Materialized view](/management/materialized-view) | ❌ | ✅ |
|
||||
| | [Secret](/management/secret) | ❌ | ✅ |
|
||||
| | [Function](/management/function) | ❌ | ✅ |
|
||||
| | [Storage integration](/management/storage-integration) | ❌ | ✅ |
|
||||
| **Maintenance** | [Intelligent Auto-Maintenance](/enterprise/intelligent-auto-maintenance) | ❌ | ✅ |
|
||||
| | [Vector Indexing](/indexing/vector-index) | ✅ | ✅ |
|
||||
| | [FTS Indexing](/indexing/fts-index) | ✅ | ✅ |
|
||||
| | [Scalar Indexing](/indexing/scalar-index) | ✅ | ✅ |
|
||||
| | [Incremental Indexing](/indexing/incremental-indexing) | ✅ | ✅ |
|
||||
| | [Distributed Indexing](/enterprise/distributed-indexing) | ❌ | ✅ |
|
||||
| | [GPU Indexing](/indexing/gpu-indexing) | ✅ | ✅ |
|
||||
| | [Compaction](/maintenance/compaction) | ✅ | ✅ |
|
||||
| | [RTE Compaction](/enterprise/realtime-engine-compaction) | ❌ | ✅ |
|
||||
| | [Garbage Collection](/maintenance/cleanup) | ✅ | ✅ |
|
||||
| | [Distributed compaction](/enterprise/distributed-compaction) | ❌ | ✅ |
|
||||
| | [Advanced Conflict Resolution](/enterprise/advanced-conflict-resolution) | ❌ | ✅ |
|
||||
| **Security** | [Authentication](/enterprise/authentication) | ❌ | ✅ |
|
||||
| | [Authorization](/governance/authorization) | ❌ | ✅ |
|
||||
| | [Audit Log](/governance/audit-log) | ❌ | ✅ |
|
||||
| **Deployment** | [Horizontal Scaling](/enterprise/horizontal-scaling) | ❌ | ✅ |
|
||||
| | [OpenTelemetry](/deployment/opentelemetry) | ✅ | ✅ |
|
||||
| | [Kubernetes](/enterprise/deployment/kubernetes) | ❌ | ✅ |
|
||||
See [Embedded vs Enterprise](/basics/embedded-vs-enterprise) for a feature-by-feature comparison, including
|
||||
architecture, latency, throughput, and day-to-day usage differences.
|
||||
|
||||
## Projects using LanceDB {#projects-using-lancedb}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user