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Apache Spark vs Close

Apache Spark logo

Apache Spark

Technology

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

From
Free
Rated
-
Full reviewWebsite
Close logo

Close

Technology

The inside sales CRM of choice for startups & SMBs

From
$9/month
Rated
-
Full reviewWebsite

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: 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.; Close hidden costs beyond subscription: calling charges (~$0.02/minute), SMS usage fees, phone numbers ($1-5/month), AI call assistant ($50/month + $0.02/min transcription)
  • They diverge on capability: Apache Spark covers Unified engine, Close covers Built-in calling.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Close differ
AttributeApache SparkClose
Starting priceFree$9/month
Pricing modelopen-sourceUnknown
Free tierYesNo
PlatformsWebWeb, iOS, Android
FoundedUnknown2013

Identical on both: user rating (Not yet rated), category (Technology).

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 Close

  • Built-in calling
  • Email automation
  • SMS messaging
  • Pipeline management
  • Lead management
  • Activity tracking
  • Reporting
  • Mobile app

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

Close

  • Inside salesnot Apache Spark
  • Outbound salesnot Apache Spark
  • Lead managementnot Apache Spark
  • Sales engagementnot Apache Spark
  • Pipeline trackingnot 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.

Close

  • Hidden costs beyond subscription: calling charges (~$0.02/minute), SMS usage fees, phone numbers ($1-5/month), AI call assistant ($50/month + $0.02/min transcription)
  • No built-in lead lists, company intelligence, or contact enrichment; must source leads separately
  • Limited marketing automation compared to competitors like HubSpot; lacks lead scoring and nurturing campaigns
  • Calling cost at scale can double effective monthly cost for high-volume SDR teams making 50+ calls per day
  • Limited customization of data model compared to Salesforce; cannot create fully custom objects or complex relationships

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Close

$9/month
  • Solo$9/month (annual)
    • 1 user only
    • 10,000 leads max
    • Calling, email, SMS
  • Essentials$35/month (annual)
    • Unlimited contacts
    • Team collaboration
    • 1,000 AI credits/month
  • Growth$99/month (annual)
    • Automation workflows
    • Power dialer
    • Bulk email
  • Scale$139/month (annual)
    • Role-based permissions
    • Predictive dialer
    • Unlimited recording

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 Close if

  • You need built-in calling.
  • You work on Web, iOS, Android.
  • You also want email automation.
Close review

Questions people ask

Is Apache Spark or Close better?
Neither clearly leads. Apache Spark starts at Free and Close at $9/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Close?
Apache Spark has a free tier; the other does not. Paid plans start at Free for Apache Spark and $9/month for Close.
Does Apache Spark or Close run on more platforms?
Apache Spark runs on Web. Close runs on Web, iOS, Android.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Close starts at $9/month.
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 Close is typically brought in for.
What can Apache Spark do that Close cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Close covers Built-in calling, Email automation, SMS messaging, Pipeline management.

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.

Close: What are Close's pricing plans and what do they include?

Close offers four plans: Solo ($9-19/user/month), Essentials ($35-49), Growth ($99-109), and Scale ($139-149). All include calling, email, SMS, and Chloe AI agent access. Essentials adds unlimited contacts and team collaboration. Growth adds automation, power dialer, and custom activities. Scale adds role-based permissions and unlimited recording. Annual billing saves up to 50%.

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.

Close: What are the hidden costs in Close's pricing beyond the subscription?

Close charges usage-based fees for calling (~$0.02 per minute), SMS charges per message, and phone number rentals ($1-5/month). The AI Call Assistant add-on costs $50/month plus $0.02 per minute for transcription. Heavy outbound calling teams can see their total costs increase 30-50% beyond the base seat price.

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.

Close: What is Chloe, Close's AI sales agent, and what can it do?

Chloe is an AI sales agent built into Close that automatically calls leads, qualifies prospects through real conversations, and can book meetings while updating the CRM automatically. Chloe features AI-generated call summaries, transcripts, and action items automatically logged to contacts.

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.

Close: Does Close include lead lists, data enrichment, or contact information?

No. Close does not provide built-in lead lists, company intelligence, or contact enrichment features. Organizations must source leads separately or use third-party data providers. This differs from platforms like HubSpot which include integrated lead databases.

Source
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.

Close: What communication features does Close include?

Close includes built-in calling with automatic call logging, email templates and bulk email capabilities, SMS messaging with tracking and automation, email syncing with Gmail, and automated follow-up workflows. All communication happens within the CRM platform.

Source
Close: Does Close have marketing automation capabilities?

Close's marketing automation features are limited compared to HubSpot. It includes basic email sequences, task reminders, and workflow automation, but lacks advanced lead scoring, nurturing campaigns, and detailed marketing analytics. Organizations requiring extensive marketing automation need separate tools.

Source
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