ClickHouse Alternatives

StarRocks is the closest open-source replacement when ClickHouse struggles with joins. It is an MPP analytical database: a query is split across many nodes, each reading compressed columns in parallel, and a cost-based planner decides how to join large tables instead of asking you to flatten everything into one wide table first. The project describes itself as delivering sub-second latency for complex multi-table queries, and it also handles second-level updates and deletes, which is the second big pain on ClickHouse, where a classic mutation rewrites whole data parts. It speaks the MySQL protocol, so most BI tools and drivers connect without a new connector, and it can query lakehouse tables in place as well as data loaded into its own storage.

The code is under the Apache License 2.0 and the project sits with the Linux Foundation (StarRocks Project, a Series of LF Projects), so self-hosting costs you only servers and engineer time. The commercial company behind it now sells its managed product as PhoenixAI: a BYOC cloud run in your own account, or a self-managed edition. It publishes no price grid; pricing depends on query volume, data scale, region and support tier, and a 30-day trial needs no card. For a self-hosted cluster, plan the same kind of servers you run for ClickHouse today.

Tinybird is the odd one on this list: it runs on managed ClickHouse, so your SQL skills and most of your table design carry over. What changes is everything around the database. You ingest events over HTTP or through Kafka, S3 and GCS connectors, write SQL in files that live in git, and publish each query as a low-latency API endpoint with a token. There is no cluster to size, no ZooKeeper or Keeper to babysit, no ingestion service to write. For a SaaS team that only used ClickHouse to serve charts to its own customers, that removes most of the work. Tinybird states it is not affiliated with ClickHouse, Inc.

Pricing is by plan and vCPU. The Free plan includes 0.25 vCPU, 1,000 requests per day and 10 GB of storage. Developer starts at $25 a month (the plan card shows $49 a month for 0.5 vCPU), with 0.25 to 8 vCPUs, 25 GB of storage and 2 replicas; storage beyond the plan costs $0.058 per GB. SaaS and Enterprise plans are custom. Queries are not throttled when they exceed your base vCPUs: they burst, and the extra compute is billed at $0.0002 per second, which is something to watch on a spiky month.

Apache Pinot was built at LinkedIn to power interactive real-time analytics inside a product, the kind of page where every user sees their own numbers. Its design goal is concurrency: the project talks about query volumes in the hundreds of thousands per second for user-facing apps, and about data from Kafka being queryable in under one second. It gets there with several indexing and pre-aggregation techniques you choose per column, plus upserts for streams where the same record changes over time. Where ClickHouse needs careful design to serve many small queries at once, Pinot starts from that use case.

The code is Apache 2.0. StarTree, founded by Pinot's creators, sells the managed version. StarTree Cloud costs $0.21 per hour per production vCPU, with no separate ingest or storage charge, autoscaling and backups included, in a standard three-zone deployment. The BYOC option, managed inside your own AWS, GCP or Azure account, costs $0.11 per hour per production vCPU plus the infrastructure your cloud provider bills you. Non-production vCPUs get lower rates, and annual and volume pricing is available. An 8-vCPU production cluster on StarTree Cloud running all month comes to $1,226.40 at list price.

DuckDB is an in-process analytical database: it runs inside your Python script, your notebook or your app, with no server at all, and reads Parquet, CSV and its own files with a columnar, vectorized engine. Many teams that picked ClickHouse for a few hundred gigabytes find that one DuckDB process on a big machine answers the same queries without a cluster. The license is MIT. MotherDuck is the managed cloud built on DuckDB: shared databases, a web UI, service accounts for dbt or BI tools, and compute instances billed per second.

MotherDuck Lite starts at $0 with 10 GB of storage, 10 hours of Pulse compute a month and up to 3 active users. Business costs $250 per organization per month plus usage: storage at $0.04 per GB a month, Pulse at $0.60 per compute-unit hour metered per query, and dedicated instances from $2.40 an hour (Standard) up to $24 an hour (Giga), billed per second. The limit is scale: a single-node engine is not designed for many petabytes or thousands of concurrent users, and MotherDuck has no on-premises version.

Apache Druid is the older sibling of the real-time OLAP family. It ingests from Kafka and Amazon Kinesis natively, so an event is queryable as it arrives, and it indexes every row by time. Data lives in a deep storage layer such as object storage, with query nodes caching what they need, which makes scaling out and recovery simpler than on a shared-nothing cluster. The project claims sub-second queries on streaming and batch data under load and 100s to 100,000s of queries per second. It shines on time-sliced aggregates (events per minute, per country, per campaign) and is weaker on large joins, where StarRocks does better.

The code is Apache 2.0, but self-hosting Druid means running several node types, which is real work. Imply, founded by Druid's creators, sells it managed as Imply Polaris. Starter begins at $100 a month for projects up to 25 GB, Standard at $600 a month for up to 9.6 TB with a 99.9% uptime SLA, and Custom is quoted. Your bill adds the tier's hourly price for every hour of the month (you can pause a project to stop it), ingestion from $0.35 per GB for the first terabyte, and storage at $0.06 per GB-month. New accounts get $500 in credits for 30 days.

BigQuery is Google Cloud's serverless warehouse. There is no cluster and no warehouse to size: you load data, write SQL and Google allocates compute, called slots, for each query. That removes almost all the operations work that pushes teams off self-hosted ClickHouse, and it plugs into the rest of Google Cloud, from Pub/Sub streaming to Looker Studio, and into workflow tools (see our n8n and BigQuery integration). Storage and compute are billed separately, so data you keep but rarely query is cheap, and data not modified for 90 consecutive days moves to the lower long-term storage rate with no change in performance.

Pricing has two models. On demand, you pay $6.25 per TiB of data your queries process, and the first 1 TiB each month is free. With capacity pricing you buy slots by the hour (pay as you go or with one- and three-year commitments), billed per second with a one-minute minimum unless you opt in to fluid scaling. The catch for ClickHouse users: BigQuery is built for analysts, not for an API that answers thousands of small queries a second, and on demand every dashboard refresh is billed by the bytes it reads. Set a maximum bytes billed limit per query from day one.

Snowflake is the default enterprise data warehouse. Storage and compute are separate: data sits once in compressed columnar storage, and each team gets its own virtual warehouse, a compute cluster you start, size and suspend independently, so finance reports never slow down the data science team. It adds the things large companies ask for: role-based access, data sharing across accounts, time travel, a marketplace, and native Python through Snowpark. For workloads that moved to ClickHouse for raw speed, Snowflake is slower on sub-second, high-concurrency queries, but it is much easier to run for a broad set of users.

You pay in credits. On demand on AWS, a credit costs from $2.00 on Standard, $3.00 on Enterprise and $4.00 on Business Critical, with higher rates in some regions, and storage costs $23 per TB per month. An X-Small warehouse burns 1 credit an hour, a Small 2 and a Medium 4, billed per second with a 60-second minimum each time it starts. Cloud services are charged only when they exceed 10% of the day's warehouse usage. A new account gets a 30-day trial; to get a team up to speed, see our Snowflake training ranking. Already on Snowflake and unhappy? Our Snowflake switching guide covers the other direction.