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Databases · head to head

Apache Spark vs Turso

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
Turso logo

Turso

Databases

SQLite-lineage database platform for running very large numbers of small per-tenant databases, with an MIT-licensed engine.

From
Free
Free plan
Yes
Visit TursoFull review

The short version

  • Apache Spark is free; Turso starts at $4.99/mo.
  • Only Apache Spark lists catalyst optimiser; only Turso lists per-tenant databases.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and Turso differ
AttributeApache SparkTurso
Pricing modelOpen-sourceFreemium

Identical on both: starting price (Free), free tier (Yes), platforms (Web), 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 Turso

  • Per-tenant databases
  • MIT licence
  • Embedded replicas
  • SQLite compatibility
  • HTTP access
  • Branching
  • Point-in-time restore
  • In-process engine

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 Turso
  • Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Turso
  • Feature engineering and model training across datasets too large to fit in pandas on one nodenot Turso
  • Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Turso

Turso

  • Multi-tenant SaaS where each customer gets their own database for real isolation, per-tenant restore and clean deletionnot Apache Spark
  • Agent or session infrastructure that creates a throwaway database per task and destroys it afterwardsnot Apache Spark
  • Local-first and offline-capable applications where an embedded replica serves reads at file speed and syncs when connectivity returnsnot Apache Spark
  • Embedding a SQLite-compatible engine in a product where the public domain original's closed contribution model is a problemnot 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

Turso

  • SQLite allows one writer per database, so a single tenant's write throughput cannot be scaled horizontally and a busy tenant serialises against itself with no sharding option
  • Per-tenant databases mean per-tenant migrations, so every schema change becomes a fan-out job that must be idempotent and resumable, and multi-database schemas, the feature meant to solve this, is deprecated
  • There are no cross-database queries; ATTACH is deprecated on the cloud, so any report or analytic spanning tenants must be assembled in your application or in a separate warehouse you also operate
  • Data Edge, the multi-region edge replication that was Turso's original positioning, is deprecated, so a large share of the tutorials and articles describing Turso as an edge database describe behaviour you can no longer rely on
  • Three engine lineages share the name, SQLite, the libSQL fork and the Rust rewrite, and the Rust engine is still maturing, so SQLite compatibility needs verifying against your specific pragmas, extensions and query patterns; the cloud already restricts journal_mode and busy_timeout and makes user_version read-only

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Turso

Free
  • FreeFree
    • 100 databases
    • 5 GB storage
    • 500M monthly rows read
  • Developer$4.99/mo
    • Unlimited databases
    • 9 GB storage
    • 2.5B monthly rows read
  • Scaler$24.92/mo
    • Unlimited databases
    • 24 GB storage
    • 100B monthly rows read
  • Pro$416.58/mo
    • Unlimited databases
    • 50 GB storage
    • 250B monthly rows read

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 Turso if

  • You need per-tenant databases.
  • You want to start without paying.
  • You also want mit licence.
Turso review

Questions people ask

Is Apache Spark or Turso better?
Neither clearly leads. Apache Spark starts at Free and Turso at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Turso?
Apache Spark starts at Free and Turso at Free.
Does Apache Spark or Turso run on more platforms?
Both run on Web, so platform support will not decide this one for you.
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 Turso is typically brought in for.
What can Apache Spark do that Turso cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Turso covers Per-tenant databases, MIT licence, Embedded replicas, SQLite compatibility.

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.

Turso: Is Turso just hosted SQLite?

Not exactly. It hosts databases built on the SQLite lineage: libSQL, an MIT fork of SQLite, and Turso Database, a newer Rust rewrite. The platform adds branching, backups, an HTTP API and programmatic database creation.

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.

Turso: Can I really run a million databases?

That is the design goal and the platform API exists to make provisioning and deletion programmatic. The practical constraints are the ones that come with it: migrations fan out, and nothing can query across databases.

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.

Turso: How do embedded replicas handle read-after-write?

Reads come from the local replica and writes go to the primary, so a read immediately after a write can return stale data unless you wait for a sync. Application code has to account for that rather than assume it.

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.

Turso: What licence is it under?

libSQL and Turso Database are both MIT. Turso Cloud is a commercial managed service built on them, so the engine is genuinely open even though the platform is not.

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.

Turso: Is Turso still an edge database?

No. Data Edge, the multi-region edge replication feature, is deprecated. The current positioning is per-tenant and embedded databases, and older material describing edge replication is out of date.

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