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Databases · head to head

Apache Spark vs Tinybird

Apache Spark logo

Apache Spark

Databases

A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.

From
Free
Free plan
Yes
Visit Apache SparkFull review
Tinybird logo

Tinybird

Databases

Managed ClickHouse with a workflow that turns SQL queries into hosted HTTP APIs

From
Free
Free plan
Yes
Visit TinybirdFull review

The short version

  • Apache Spark is free; Tinybird starts at $25/mo.
  • Only Apache Spark lists unified engine; only Tinybird lists managed ClickHouse.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which Apache Spark and Tinybird actually diverge.

Attributes where Apache Spark and Tinybird differ
AttributeApache SparkTinybird
Pricing modelOpen-sourcePer month by compute and storage
PlatformsWebWeb, Linux, macOS

Identical on both: starting price (Free), free tier (Yes), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Spark

  • Unified engine
  • Catalyst optimiser
  • DataFrame and SQL APIs
  • Structured Streaming
  • Spark Connect
  • Kubernetes and YARN support
  • Table format integration
  • MLlib

Only in Tinybird

  • Managed ClickHouse
  • Pipes as APIs
  • Events HTTP endpoint
  • Streaming connectors
  • Materialized views
  • Git-based workflow
  • Token-scoped auth
  • Observability

What people use each for

The jobs each tool is most often brought in to do.

Apache Spark

  • Nightly ETL over terabytes in object storage, where a single machine would take longer than the batch window allowsnot Tinybird
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Tinybird
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Tinybird
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Tinybird

Tinybird

  • A SaaS product adding a per-customer usage dashboard that must render in under a second across billions of eventsnot Apache Spark
  • A team building rate limiting or fraud checks that need an aggregate over the last few minutes returned inside a request cyclenot Apache Spark
  • A data team offloading interactive operational dashboards from Snowflake, where per-query warehouse cost makes constant refresh untenablenot Apache Spark
  • A game or ad-tech company ingesting a high-volume event stream and exposing live counters back to customers through an APInot Apache Spark

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Spark

  • Running it well is JVM operations work: executor sizing, shuffle partition counts, off-heap memory and serialisation all have to be tuned, and the failures you actually get are out-of-memory errors and skewed shuffles rather than wrong answers, so you need somebody who can read the Spark UI or you will scale the cluster instead of fixing the query
  • The fastest Spark is not open source. Databricks' Photon engine and comparable vendor accelerations are proprietary, so benchmark numbers quoted for Spark frequently describe a fork you can only rent, and moving off that vendor loses the performance you sized your pipelines around
  • It is a distributed system with distributed overheads, and modern single-node tools such as DuckDB and Polars finish faster on datasets up to hundreds of gigabytes with no cluster to start, so a Spark job below that threshold is paying coordination cost for nothing
  • Structured Streaming is micro-batch, which puts an end-to-end latency floor in the range of hundreds of milliseconds to seconds; workloads that need genuine per-event latency go to Flink instead, and discovering this after building on Spark means a rewrite
  • Major upgrades deliberately break jobs: Spark 4.0 turns ANSI SQL mode on by default, so silent overflow and invalid casts that previously produced nulls now raise runtime errors, and a pipeline that worked for years can start failing purely on upgrade
  • PySpark hides a process boundary, and Python UDFs serialise every row between the JVM and a Python worker; a direct translation of pandas code into PySpark UDFs can run an order of magnitude slower than the equivalent built-in expressions

Tinybird

  • It is ClickHouse underneath, so it inherits ClickHouse limits: multi-table joins degrade badly at scale, updates and deletes are expensive mutations rather than cheap operations, and a poorly chosen sorting key at table creation cannot be fixed without rebuilding the data
  • Compute is metered per vCPU-second with overage at 0.0002 USD per second, so an inefficient query shipped to production shows up directly on the invoice rather than merely running slowly
  • Only the Enterprise tier gets horizontal scaling and dedicated infrastructure; Free, Developer and SaaS all run on shared infrastructure with vertical scaling only, which caps both isolation and headroom for anyone not on a custom contract
  • Storage is billed at 0.058 USD per gigabyte on top of compute, and egress is charged separately at 0.01 USD per gigabyte intra-cloud and 0.10 USD inter-cloud, so a high-fanout API serving many small responses accrues costs in three places at once
  • You are building on a proprietary workflow around an open database: the pipes, tokens and API layer are Tinybird specific, so leaving means keeping your data but rewriting the entire serving layer you adopted Tinybird to avoid writing

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Tinybird

Free
  • FreeFree
    • 0.25 vCPU on shared infrastructure
    • 10 GB storage included
    • 1,000 requests per day
  • Developer$25/mo
    • 0.5 vCPU scaling to 8 vCPU
    • 25 GB storage included
    • Two replicas
  • SaaSOn request
    • Up to 32 vCPU
    • 500 GB storage included
    • Four to sixteen threads per request
  • EnterpriseOn request
    • Unlimited vCPU and bottomless storage
    • Dedicated infrastructure and private regions
    • Vertical and horizontal scaling

Which should you pick?

Choose Apache Spark if

  • You need unified engine.
  • You want to start without paying.
  • You also want catalyst optimiser.
Apache Spark review

Choose Tinybird if

  • You need managed clickhouse.
  • You want to start without paying.
  • You work on Web, Linux, macOS.
  • You also want pipes as apis.
Tinybird review

Questions people ask

Is Apache Spark or Tinybird better?
Neither clearly leads. Apache Spark starts at Free and Tinybird at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Tinybird?
Apache Spark starts at Free and Tinybird at Free.
Does Apache Spark or Tinybird run on more platforms?
Apache Spark runs on Web. Tinybird runs on Web, Linux, macOS.
Can I use Apache Spark for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark best used for?
Apache Spark is most often used for nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows, building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction, feature engineering and model training across datasets too large to fit in pandas on one node, migrating legacy mapreduce or hive workloads onto an engine that is still actively developed and widely supported by cloud vendors. Of those, nightly etl over terabytes in object storage, where a single machine would take longer than the batch window allows and building and maintaining a lakehouse on iceberg or delta lake, where spark handles both the writes and the compaction are not what Tinybird is typically brought in for.
What can Apache Spark do that Tinybird cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Tinybird covers Managed ClickHouse, Pipes as APIs, Events HTTP endpoint, Streaming connectors.

Answered from the vendors’ own pages

Apache Spark: When is Spark the wrong choice?

When your data fits comfortably on one machine. DuckDB or Polars will process hundreds of gigabytes on a single large node faster than a Spark cluster, without a scheduler, a driver or a shuffle. Spark earns its overhead when the data genuinely does not fit.

Tinybird: Is Tinybird just hosted ClickHouse?

No. The database is ClickHouse, but the product is the layer above it: publishing parameterised SQL as authenticated, rate-limited REST endpoints without writing an API server.

Apache Spark: Is Spark the same on Databricks as the open source version?

No. Databricks runs its own runtime including the proprietary Photon engine and its own optimisations, so performance figures and some behaviours do not carry over to open source Spark on EMR, Dataproc or your own Kubernetes cluster.

Tinybird: What does it actually cost?

Free tier at 1,000 requests a day, Developer from 25 USD a month, then compute at 0.0002 USD per vCPU-second and storage at 0.058 USD per gigabyte. Higher tiers are quoted.

Apache Spark: Can I use Spark for real-time processing?

For near-real-time, yes, with Structured Streaming's micro-batch model, which lands in the sub-second to seconds range. For true per-event latency in the low milliseconds, Flink is the usual choice.

Tinybird: Can I run it on my own infrastructure?

Only on Enterprise, which offers dedicated infrastructure and private regions. Lower tiers are shared multi-tenant cloud.

Apache Spark: Does upgrading between major versions break things?

Yes, by design in some cases. Spark 4.0 makes ANSI SQL mode the default, which converts previously silent overflow and cast failures into runtime errors. Upgrades need a testing pass over production pipelines rather than a version bump.

Tinybird: Does it handle updates and deletes?

Poorly, as ClickHouse does. Design for append-only event data; frequent mutation is the wrong workload for this engine.

Apache Spark: Do I need to know Scala?

No. Python covers the vast majority of work and PySpark is the most common interface. Scala still helps when reading the source, writing custom data sources or diagnosing errors that surface as JVM stack traces.

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