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Alkami vs Apache Spark

Alkami logo

Alkami

Technology

Digital banking platform for United States banks and credit unions

From
On request
Rated
-
Full reviewWebsite
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

The short version

  • Only Apache Spark has a free tier, so it costs nothing to try first.
  • Each has a real cost: Alkami pricing is per registered user with contractual minimums, so institutions with many enrolled but dormant users pay for accounts that never log in.; 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.
  • They diverge on capability: Alkami covers Retail digital banking, Apache Spark covers Unified engine.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Alkami and Apache Spark differ
AttributeAlkamiApache Spark
Starting priceOn requestFree
Pricing modelquoteopen-source
Free tierNoYes
PlatformsWeb, iOS, AndroidWeb

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 Alkami

  • Retail digital banking
  • Business banking
  • Digital account opening
  • Payments and money movement
  • Data and marketing
  • Extensibility framework
  • Card controls
  • Multi core integration

Only in Apache Spark

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

What people use each for

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

Alkami

  • A credit union replacing the weak digital banking bundled with its core contractnot Apache Spark
  • A community bank that needs commercial entitlements and approvals its core vendor does not providenot Apache Spark
  • An institution wanting digital account opening and digital banking from one vendor after the MANTL acquisitionnot Apache Spark
  • A bank competing for deposits online and needing an onboarding funnel that works on mobilenot Apache Spark

Apache Spark

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

Where each one falls short

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

Alkami

  • Pricing is per registered user with contractual minimums, so institutions with many enrolled but dormant users pay for accounts that never log in.
  • Alkami is a channel layer, not a core, so functionality is bounded by what the core exposes, and a poor core integration produces a slow product no amount of channel work fixes.
  • Implementation typically runs six to twelve months and requires institution staff time, which small credit unions consistently underestimate.
  • The MANTL acquisition adds account opening but also another integration programme, and consolidating on one vendor removes leverage to swap a weak module for a best of breed alternative.
  • Buying digital banking separately from the core vendor means you own the integration risk between two suppliers who will each blame the other when something breaks.

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.

Pricing, plan by plan

Alkami

On request
  • Alkami Digital Banking Platform$undefined/year
    • Priced per registered user per month with multi year terms
    • Minimum user commitments typical, so enrolled inactive users are still billed
    • Implementation and core integration charged separately

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Which should you pick?

Choose Alkami if

  • You need retail digital banking.
  • You work on Web, iOS, Android.
  • You also want business banking.
Alkami review

Choose Apache Spark if

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

Questions people ask

Is Alkami or Apache Spark better?
Neither clearly leads. Alkami starts at On request and Apache Spark at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Alkami or Apache Spark?
Apache Spark has a free tier; the other does not. Paid plans start at On request for Alkami and Free for Apache Spark.
Does Alkami or Apache Spark run on more platforms?
Alkami runs on Web, iOS, Android. Apache Spark runs on Web.
Can I use Apache Spark for free?
Yes. Apache Spark has a free tier, so you can try it without paying. Alkami starts at On request.
What is Alkami best used for?
Alkami is most often used for a credit union replacing the weak digital banking bundled with its core contract, a community bank that needs commercial entitlements and approvals its core vendor does not provide, an institution wanting digital account opening and digital banking from one vendor after the mantl acquisition, a bank competing for deposits online and needing an onboarding funnel that works on mobile. Of those, a credit union replacing the weak digital banking bundled with its core contract and a community bank that needs commercial entitlements and approvals its core vendor does not provide are not what Apache Spark is typically brought in for.
What can Alkami do that Apache Spark cannot?
Alkami covers Retail digital banking, Business banking, Digital account opening, Payments and money movement. Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming.

Answered from the vendors’ own pages

Alkami: Does Alkami replace our core?

No. It is the digital channel and account opening layer on top of a core such as Fiserv, Jack Henry or FIS.

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.

Alkami: How is it priced?

Per registered user per month on multi year contracts with minimums, so model your enrolled user count rather than your active user count.

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.

Alkami: What did the MANTL acquisition change?

It brought deposit account opening in house, announced February 2025 at $400 million enterprise value, so account opening and digital banking can come from one vendor.

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.

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.

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