LakeHold
Comparison

How LakeHold compares

LakeHold is not the right choice for everyone. This page says plainly where each alternative wins, because a comparison that only flatters us is marketing, not engineering — and you would find out the truth in week three anyway.

Colour key Strength on this axis Limitation on this axis Depends what you wantJudged for the reader, not for us — the same rule applies to every column.
Feature comparison across LakeHold, MotherDuck, ClickHouse, and cloud warehouses
 LakeHoldMotherDuckClickHouseSnowflake / Databricks
DeploymentSelf-hosted anywhere, incl. air-gappedHosted catalog; local or cloud computeSelf-hosted or ClickHouse CloudHosted service only
Where your data livesYour disk or object store, under your controlManaged storage or your own bucketYour disks, or their cloudTheir account, or external tables
Accounts, SSO, permissionsOIDC browser login, scoped API tokens, three roles, and client-token administration; no row policiesAccounts, SSO, org rolesUsers, roles, row policiesMature RBAC, SSO, lineage
Table formatDuckLake — plain Parquet + SQL catalogDuckLake — same open formatMergeTree, proprietary on diskDelta / Iceberg, now genuinely open
Read data without the productYes — tested, see exit pathYes, DuckLake is openExport requiredYes, via Iceberg / Delta readers
Other engines read it liveEject or export today; Iceberg REST plannedDirect with BYO compute; catalog remains hostedIts own protocols; export for othersYes — via their catalog endpoints
Time travelYes — query your data from an earlier point in timeYesNo first-class equivalentYes, mature
Verified, signed exportOne call — row-count attested and signedManual export; nothing attests itManual exportManual unload; nothing attests it
Change data captureBuilt in — typed feed + signed webhooksLimited; not exposed directlyKafka engine or external toolingYes — CDF / streams, mature
AI / MCPAuthenticated MCP; read tools + operator-gated writesManaged MCP with sandboxed computeOpen-source and managed remote MCPManaged AI and agent platforms with MCP
BI tools (Power BI, Tableau)Postgres wire protocol; Power BI blocked on type loadingPostgres endpoint; connector for older toolsNative connectors and JDBC/ODBCFirst-class connectors everywhere
Maintenance controlExplicit, dry-run by defaultAutomatic, not exposedExplicit merges and TTLsAutomatic, partly exposed
.NET / EF CoreOne model for app and lake; client package pendingCommunity drivers; Python/JS firstSolid ADO.NET client, no ORM storyJDBC/ODBC; .NET is second-class
Scale ceilingScale out workers; each query stays on one nodeElastic, scales past a nodeClustered, petabyte-scaleEffectively unlimited
Concurrent writersPostgreSQL-backed DuckLake metadata; worker-local executionManagedHigh concurrencyHigh concurrency
Operational burdenYou run itNoneHigh if self-hostedLow
LicenceApache-2.0ProprietaryApache-2.0; Cloud proprietaryProprietary
Cost shapeA VM and a bucketFree Lite; Business $250/org/mo + usageFree self-hosted; Cloud pay-as-you-useUsage / credit based; enterprise spend varies

Pricing and tiers checked July 2026 and they move constantly — treat the cost row as a shape, not a quote. Verify against each vendor before you budget.

vs MotherDuck

The closest comparison: the same engine and the same table format, with a different control model. MotherDuck hosts the catalog and can manage storage and compute; LakeHold puts the whole service under your control. Query semantics are otherwise close — a query that runs on one generally runs on the other.

Choose LakeHold when

  • Data residency, a security review, or an air-gapped network rules out a hosted service.
  • You want the metadata catalog under your control rather than hosted by another service.
  • Your stack is .NET and you want EF Core and analytics sharing one model.
  • You want the compaction and retention knobs exposed rather than managed for you.
  • A predictable VM bill beats per-second billing for your workload.

Choose MotherDuck when

  • You need per-user accounts and row- or column-level permissions with a console to administer them — LakeHold authenticates with tenant-scoped API tokens, OIDC, and three roles, but per-user administration is not a product surface yet.
  • You need a shared multi-tenant production service today — LakeHold still has same-name catalog isolation work to complete.
  • You want zero operations and nothing to run.
  • You need to scale past a single node without re-architecting.
  • Hybrid local-and-cloud dual execution is valuable to you — it is genuinely clever and we have not replicated it.
  • You want managed ingestion and a more mature web UI today.
  • Your team is Python-first.

vs ClickHouse

The strongest alternative if self-hosting is the requirement, and on raw scale and concurrency it beats us outright. The real difference is storage philosophy: ClickHouse owns its on-disk format, LakeHold leaves plain Parquet in your bucket.

Choose LakeHold when

  • You want an open Parquet and DuckLake storage contract with a verified eject path.
  • You need transactions, snapshots, and time travel over your tables.
  • Your data fits comfortably on one node and you would rather not run a cluster.
  • You are a .NET shop and want an ORM story, not just a driver.

Choose ClickHouse when

  • You are past a few terabytes, or heading there quickly.
  • You need high write concurrency or many simultaneous readers.
  • You want sub-second dashboards over very large tables.
  • You have the DevOps capacity to run a cluster properly — and it is a real cluster.
  • You need a mature ecosystem and a long operational track record.

vs Snowflake / Databricks

Grouped because by 2026 they have converged: both do lakehouse workloads, both support open table formats, and both are excellent. They are also a different category of spend, and the honest comparison is scope, not features.

Choose LakeHold when

  • Your workload is far smaller than their pricing assumes — a single node genuinely covers it.
  • You want the whole platform to be inspectable and Apache-2.0.
  • You need to run somewhere they do not, including on a laptop or offline.
  • Cost predictability matters more than elasticity.

Choose Snowflake / Databricks when

  • You are operating at real scale, with many teams and governed sharing across them.
  • You need mature governance, lineage, and enterprise compliance out of the box.
  • ML, Spark, and agentic AI workloads sit alongside your SQL.
  • You want an ecosystem of connectors and consultants that already exists.
  • Nobody on your team should be thinking about compaction schedules.

“Why not just use DuckDB?”

Often you should. DuckDB in a script, reading Parquet from a bucket, is an excellent answer and costs nothing to run. LakeHold only starts earning its place when you need things a library cannot give you:

  • More than one person. DuckDB is a library, not a service — there is no shared endpoint, no auth, no query history.
  • Tenant-scoped access. Credentials and control-plane records carry tenant identity. Shared multi-tenant production use still waits on complete same-name catalog isolation.
  • Transactions and time travel. That is DuckLake, and someone has to own the catalog, the snapshots, and the compaction schedule.
  • An operator. Flush, compact, expire, and cleanup are real jobs with real consequences. LakeHold makes them buttons with dry-run defaults instead of folklore.

If none of those apply, use DuckDB directly and come back when they do. LakeHold is built on it either way, so nothing you write is wasted.

See it for yourself

The development stack ships with a seeded catalog — 250,000 events, queryable immediately.

Open the workbench