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AI · head to head

Helicone vs Apache Spark MLlib

Helicone logo

Helicone

AI

Open-source LLM observability and gateway platform for AI applications

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Helicone the free Hobby plan is capped at 10,000 requests per month, which teams with production traffic can exceed quickly.; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: Helicone covers Request dashboard and tracking, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Helicone and Apache Spark MLlib actually diverge.

Attributes where Helicone and Apache Spark MLlib differ
AttributeHeliconeApache Spark MLlib
Pricing modelfreemiumopen-source
Platformsweb, apiLinux, macOS, Windows
CategoryAIMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Helicone

  • Request dashboard and tracking
  • Sessions and segments
  • Helicone Query Language (HQL)
  • Prompt datasets and improvement
  • Playground
  • Rate limits and alerts

Only in Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

What people use each for

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

Helicone

  • Monitoring cost and latency of production LLM applicationsnot Apache Spark MLlib
  • Debugging multi-step agent sessionsnot Apache Spark MLlib
  • Managing and iterating on prompts across a teamnot Apache Spark MLlib
  • Routing requests across multiple LLM providersnot Apache Spark MLlib

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Helicone
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Helicone
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Helicone
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Helicone

Where each one falls short

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

Helicone

  • The free Hobby plan is capped at 10,000 requests per month, which teams with production traffic can exceed quickly.
  • Advanced compliance features like SOC 2 and HIPAA are only available starting at the $799/month Team plan.
  • Usage beyond the free tier is billed on top of the base subscription, adding cost unpredictability at scale.
  • On-premises deployment is restricted to the custom Enterprise tier.

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Pricing, plan by plan

Helicone

Free
  • HobbyFree
    • 10,000 free requests
    • 1 GB storage
    • 1 seat
  • Pro$79/month
    • 10K free requests included, usage-based beyond
    • 7-day free trial
    • Unlimited playgrounds and workspaces
  • Team$799/month
    • 5 organizations
    • SOC 2 and HIPAA compliance
    • Dedicated Slack channel access
  • Enterprise$undefined/mo
    • Custom MSAs and SAML SSO
    • On-premises deployment
    • Bulk cloud discounts

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib review.

Which should you pick?

Choose Helicone if

  • You need request dashboard and tracking.
  • You want to start without paying.
  • You work on web, api.
  • You also want sessions and segments.
Helicone review

Choose Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Questions people ask

Is Helicone or Apache Spark MLlib better?
Neither clearly leads. Helicone starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Helicone or Apache Spark MLlib?
Helicone starts at Free and Apache Spark MLlib at Free.
Does Helicone or Apache Spark MLlib run on more platforms?
Helicone runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Helicone for free?
Both have a free tier, so you can try either at no cost before committing.
What is Helicone best used for?
Helicone is most often used for monitoring cost and latency of production llm applications, debugging multi-step agent sessions, managing and iterating on prompts across a team, routing requests across multiple llm providers. Of those, monitoring cost and latency of production llm applications and debugging multi-step agent sessions are not what Apache Spark MLlib is typically brought in for.
What can Helicone do that Apache Spark MLlib cannot?
Helicone covers Request dashboard and tracking, Sessions and segments, Helicone Query Language (HQL), Prompt datasets and improvement. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Helicone: What does Helicone cost?

Helicone offers a free Hobby plan, a Pro plan at $79/month, a Team plan at $799/month, and custom Enterprise pricing, with usage-based charges applying beyond included request limits.

Source
Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

Helicone: Is there a free plan, and what are its limits?

The free Hobby plan includes 10,000 requests per month, 1 GB of storage, 1 seat, and 1 organization, aimed at kickstarting AI projects.

Source
Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

Helicone: Are there discounts available?

Helicone offers 50% off the first year for qualifying startups, discounts for non-profits, a $100 annual credit for open-source projects, and free access for students.

Source
Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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