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Machine Learning · head to head

Apache Spark MLlib vs Traceloop

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
Traceloop logo

Traceloop

Logging

LLM reliability platform with open-source observability and evaluation

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Traceloop free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Traceloop covers Open-source SDK (OpenLLMetry).
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and Traceloop differ
AttributeApache Spark MLlibTraceloop
Pricing modelopen-sourceFreemium with pay-as-you-go enterprise option
PlatformsLinux, macOS, WindowsCloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby
CategoryMachine LearningLogging
Founded1999Unknown

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 Apache Spark MLlib

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

Only in Traceloop

  • Open-source SDK (OpenLLMetry)
  • Multi-provider support
  • Observability platform integration
  • Framework support
  • Monitoring dashboard
  • Evaluation system
  • Deployment flexibility

What people use each for

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

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 Traceloop
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Traceloop
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Traceloop
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Traceloop

Traceloop

  • Monitoring LLM application performance in productionnot Apache Spark MLlib
  • Instrumenting LLM apps with minimal code overheadnot Apache Spark MLlib
  • Continuous evaluation and quality scoring of LLM outputsnot Apache Spark MLlib
  • Debugging LLM application issues with full trace visibilitynot Apache Spark MLlib
  • Integrating observability data into existing monitoring stacksnot Apache Spark MLlib

Where each one falls short

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

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.

Traceloop

  • Free tier limited to 50k spans per month and 24-hour retention, restricting production use
  • Company acquisition by ServiceNow creates uncertainty about future roadmap
  • Requires integration with separate observability platforms for visualization
  • Less feature-rich than dedicated LLM evaluation platforms

Pricing, plan by plan

Apache Spark MLlib

Free

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

Traceloop

Free
  • FreeFree
    • 50,000 spans per month
    • Up to 5 seats
    • 24-hour data retention
  • Enterprise$undefined/custom
    • Unlimited spans per month
    • Unlimited seats
    • Custom data retention

Which should you pick?

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

Choose Traceloop if

  • You need open-source sdk (openllmetry).
  • You want to start without paying.
  • You work on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
  • You also want multi-provider support.
Traceloop review

Questions people ask

Is Apache Spark MLlib or Traceloop better?
Neither clearly leads. Apache Spark MLlib starts at Free and Traceloop at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Traceloop?
Apache Spark MLlib starts at Free and Traceloop at Free.
Does Apache Spark MLlib or Traceloop run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Traceloop runs on Cloud, On-premises, Air-gapped, Python, TypeScript, Go, Ruby.
Can I use Apache Spark MLlib for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Traceloop is typically brought in for.
What can Apache Spark MLlib do that Traceloop cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Traceloop covers Open-source SDK (OpenLLMetry), Multi-provider support, Observability platform integration, Framework support.

Answered from the vendors’ own pages

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.

Traceloop: Is OpenLLMetry open-source?

Yes, OpenLLMetry is Traceloop's open-source SDK built on OpenTelemetry standards. It allows teams to instrument LLM applications with just 2 lines of code and send data to 25+ observability platforms.

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.

Traceloop: What is the impact of ServiceNow acquisition?

Traceloop is joining ServiceNow, representing a strategic acquisition that will broaden enterprise adoption and integration capabilities. Current operations continue with free and enterprise options available.

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.

Traceloop: How many LLM providers and frameworks does Traceloop support?

Traceloop supports 20+ LLM providers including OpenAI and Anthropic, and integrates with frameworks like LangChain and LlamaIndex. It can send data to 25+ observability platforms.

Source
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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