How to choose a real-time OLAP database

A real-time OLAP database ingests streaming and batch data continuously and answers aggregate queries in sub-second time against data that may be seconds old, not last night's load. That combination — fresh data, fast aggregate queries — is the whole point of the category, and it's expensive to build well, which is why every option here asks for real operational investment or a real subscription. Teams that need it: anyone building a user-facing analytics feature, an operational dashboard on live data, or an observability backend. Teams that don't: if daily or hourly freshness is genuinely fine, a conventional warehouse and BI tool will be cheaper to run and easier to hire for.

Architecture: single binary versus separated tiers

ClickHouse, Apache Doris and StarRocks favor a simpler, more unified architecture — fewer distinct process types to operate. Apache Druid and Apache Pinot deliberately separate ingestion, storage and query into distinct node types (and, for Druid, a ZooKeeper dependency and deep storage layer), which buys finer-grained tuning at the cost of more moving parts to run well. Neither approach is objectively better; the separated model tends to reward teams with the operational depth to tune it, while the unified model gets a smaller team to a working system faster.

Who is actually running the query

This is the sharpest dividing line in the category. Apache Pinot was built at LinkedIn specifically for very high query concurrency — thousands of queries per second at predictable millisecond latency — aimed at analytics embedded directly inside a customer-facing product. Apache Druid's indexing and query model is built more for internal, operational dashboards where query shapes vary and ad hoc exploration matters more than raw concurrency. Tinybird takes a related but distinct approach: it turns SQL transformations into versioned REST API endpoints on top of ClickHouse, aimed squarely at product engineers embedding real-time features rather than analysts running dashboards.

Query the lakehouse in place, or load data in natively

Apache Doris and StarRocks — which share common origins — can query data already sitting in Iceberg, Hudi, Delta Lake or Hive tables directly, without a separate ingestion step, in addition to serving natively loaded data. That matters if a meaningful share of your data already lives in lake tables you'd rather not duplicate. ClickHouse and Druid/Pinot are generally used with data loaded natively into their own storage for maximum query speed, treating federation as secondary. If your organization is already committed to a lakehouse table format, that commitment should weigh heavily on this choice — see the lakehouse platform guide.

Managed by the project's own creators

A pattern repeats across this category: the commercial, managed version of an open-source project is frequently built by the same people who created it. Imply (Druid), StarTree (Pinot) and ClickHouse Cloud (ClickHouse) all fit this shape. Buying the managed version means someone else runs compaction, scaling, upgrades and on-call — a real cost saving for teams without the operational depth the open-source project otherwise demands, at the price of a usage-based bill instead of a fixed infrastructure cost.

How pricing scales

Self-hosted open-source deployment costs only your own infrastructure and operations time. Managed offerings are usage-based, but metered differently across vendors: Imply prices by project-size tier plus consumption; StarTree meters per production vCPU-hour, with separate rates for its SaaS, bring-your-own-cloud and bring-your-own-Kubernetes models; ClickHouse Cloud bills by compute-unit-hour and storage; Tinybird combines a base plan fee with usage-based compute and storage charges. None of the open-source projects themselves carry a license fee — see the profiles for verified current figures rather than relying on this guide for prices.

A shortlist by situation

• Want the simplest, most widely adopted single-binary engine for logs, events or product analytics: ClickHouse, managed as ClickHouse Cloud.
• Building a customer-facing product that needs thousands of queries per second at millisecond latency: Apache Pinot, managed as StarTree.
• Internal or operational dashboards over continuously streaming data with varied, ad hoc query shapes: Apache Druid, managed as Imply.
• Want one engine that queries both freshly loaded data and existing lakehouse tables: Apache Doris or StarRocks.
• Want to turn event streams directly into versioned API endpoints without managing infrastructure: Tinybird.

Questions to ask vendors or in a trial

1. What is the actual ingestion-to-queryable latency under our real data volume, not the vendor's benchmark?
2. What does it cost to run at our expected concurrency and cardinality — not the entry-tier numbers?
3. Do we need to query lakehouse tables directly, or is native ingestion into the database's own storage enough?
4. What operational burden does the open-source version carry that the managed price is buying us out of?

Common mistakes

Choosing on a raw-speed benchmark without matching it to your actual concurrency and cardinality profile is the most common misstep — a database tuned to win a single-query benchmark can still struggle under your real thousands-of-QPS load, or the reverse. Underestimating the multi-node operational burden of Druid or Pinot is a close second; both genuinely need dedicated attention to run well self-hosted. The third is committing to a managed service before checking whether the open-source core, self-hosted, already covers the actual requirement — the managed price is buying operational relief, not new capability.

Where to go next

Apache Druid vs Apache Pinot and ClickHouse vs StarRocks cover the two most-asked comparisons in this category. See every tool in this category for the full list.