Softwr

Databases · head to head

Apache Spark vs Nile

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
Nile logo

Nile

Databases

PostgreSQL database platform built for multi-tenant SaaS applications

From
Free
Free plan
Yes
Visit NileFull review

The short version

  • Apache Spark is free; Nile starts at $15/mo.
  • Only Apache Spark lists unified engine; only Nile lists multi-tenant virtualization.
  • Where Nile falls short: limited to B2B SaaS use cases, not optimized for single-tenant applications.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

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

Attributes where Apache Spark and Nile differ
AttributeApache SparkNile
Pricing modelOpen-sourcePay-per-use with committed pricing
PlatformsWebWeb, API

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 Nile

  • Multi-tenant virtualization
  • Serverless compute
  • Vector embeddings
  • Tenant branching
  • Schema migration
  • Tenant dashboards
  • Global deployment
  • PostgreSQL compatible

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

Nile

  • Multi-tenant SaaS applications with per-customer isolationnot Apache Spark
  • Serverless workloads needing cost-efficient auto-scalingnot Apache Spark
  • RAG applications leveraging vector embeddingsnot Apache Spark
  • Applications requiring per-tenant analytics dashboardsnot Apache Spark
  • Cross-customer analytics using shared data tablesnot 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

Nile

  • Limited to B2B SaaS use cases, not optimized for single-tenant applications
  • Query token abstraction makes pricing less transparent than traditional compute pricing
  • Free plan limited to 50M query tokens and 1GB storage for development
  • Serverless startup latency may impact performance-sensitive applications
  • Per-million token overages require careful monitoring to avoid surprise costs

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Nile

Free
  • FreeFree
    • 50M query tokens included
    • 1 GB storage
    • 500 connections
  • Pro$15/mo
    • 150M query tokens included
    • 5 GB storage
    • 10,000 connections
  • Scale$350/mo
    • 500M query tokens included
    • 50 GB storage
    • 100,000 connections
  • EnterpriseOn request
    • Custom resource allocation
    • Large-scale workloads supporting millions of tenants
    • Designated support

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 Nile if

  • You need multi-tenant virtualization.
  • You want to start without paying.
  • You work on Web, API.
  • You also want serverless compute.
Nile review

Questions people ask

Is Apache Spark or Nile better?
Neither clearly leads. Apache Spark starts at Free and Nile at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Nile?
Apache Spark starts at Free and Nile at Free.
Does Apache Spark or Nile run on more platforms?
Apache Spark runs on Web. Nile runs on Web, API.
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 Nile is typically brought in for.
What can Apache Spark do that Nile cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Nile covers Multi-tenant virtualization, Serverless compute, Vector embeddings, Tenant branching.

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.

Nile: What are query tokens and how are they calculated?

Query tokens are abstract units of CPU and memory used when queries execute on Nile's serverless compute. The platform uses this abstraction to enable fair, predictable pay-per-use pricing.

Source
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.

Nile: Do all Nile plans include multi-tenant isolation?

Yes. All plans including the free tier include unlimited databases, tenants, and vector embeddings with built-in data isolation.

Source
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.

Nile: What is included in the Pro plan?

The Pro plan ($15/month) includes 150M query tokens, 5GB storage, 10,000 connections, and a 99.95% SLA. SAML and MFA authentication features are available on Pro and above.

Source
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.

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.

Share

Related pages

More on Apache Spark

More on Nile

Keep looking

Other head to heads