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Apache Spark vs Envoy

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
Envoy logo

Envoy

Technology

A high-performance L7 proxy written in C++ that is configured by an API rather than a config file, and is usually deployed under a control plane.

From
Free
Rated
-
Full reviewWebsite

The short version

  • 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.; Envoy the configuration surface is very large and hand-written bootstrap YAML runs to hundreds of lines for routing that Nginx expresses in twenty, which is why nearly every production deployment sits under a control plane and inherits that control plane's constraints as well.
  • They diverge on capability: Apache Spark covers Unified engine, Envoy covers xDS dynamic configuration.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark and Envoy differ
AttributeApache SparkEnvoy

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), platforms (Web), 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 Envoy

  • xDS dynamic configuration
  • Protocol breadth
  • Filter chain architecture
  • Observability by default
  • Outlier detection
  • Traffic shaping
  • mTLS termination and origination
  • Hot restart

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

Envoy

  • Acting as the data plane under a service mesh or Gateway API implementation, which is how the overwhelming majority of deployments use itnot Apache Spark
  • An edge or API gateway that needs per-route retry budgets, circuit breaking and outlier detection rather than round-robin proxyingnot Apache Spark
  • Migrating traffic between service versions or between a monolith and its replacement, using weighted splits and shadow trafficnot Apache Spark
  • Standardising observability across a polyglot estate, so that latency, error rates and tracing look the same regardless of the language a service is written innot 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.

Envoy

  • The configuration surface is very large and hand-written bootstrap YAML runs to hundreds of lines for routing that Nginx expresses in twenty, which is why nearly every production deployment sits under a control plane and inherits that control plane's constraints as well.
  • xDS is the real API and it is not stable in the comfortable sense; the v2 API set was removed outright, resource types continue to be deprecated, and your control plane and Envoy binaries have to be upgraded roughly in step or the proxies stop accepting configuration.
  • Extending it properly means writing a C++ filter and building and maintaining your own Envoy binary; the alternatives are Lua, which adds per-request overhead, and proxy-wasm, whose ABI has remained effectively experimental for years with a real performance cost.
  • At sidecar density the per-proxy memory and CPU footprint is a measurable share of cluster capacity, since thousands of workloads each carry a full proxy, and this is precisely the cost that has pushed mesh projects towards node-level or ambient architectures.
  • There is no single vendor selling support for Envoy itself; you get the community plus control-plane vendors such as Solo.io and Tetrate, so an Envoy-level production bug is your own engineers in a C++ codebase unless a support contract happens to cover it.
  • Diagnosing why a request got a particular response involves reading config dumps, the stats endpoint and the RESPONSE_FLAGS codes in access logs rather than a readable error, which is a specific skill you must hire or spend months growing.

Pricing, plan by plan

Apache Spark

Free

No published plan breakdown. See the Apache Spark review.

Envoy

Free

No published plan breakdown. See the Envoy review.

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 Envoy if

  • You need xds dynamic configuration.
  • You want to start without paying.
  • You also want protocol breadth.
Envoy review

Questions people ask

Is Apache Spark or Envoy better?
Neither clearly leads. Apache Spark starts at Free and Envoy at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark or Envoy?
Apache Spark starts at Free and Envoy at Free.
Does Apache Spark or Envoy run on more platforms?
Both run on Web, so platform support will not decide this one for you.
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 Envoy is typically brought in for.
What can Apache Spark do that Envoy cannot?
Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Envoy covers xDS dynamic configuration, Protocol breadth, Filter chain architecture, Observability by default.

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.

Envoy: Should I run Envoy on its own, or under a control plane?

Almost always under one. Directly authoring xDS or static bootstrap configuration is viable for a handful of routes and becomes unmanageable beyond that. Envoy Gateway, Istio, Contour, Gloo and Consul all exist to generate that configuration for you.

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.

Envoy: How does it compare with Nginx or HAProxy?

Envoy is dynamically configured over an API and instrumented far more heavily; Nginx and HAProxy are faster to configure and lighter for straightforward reverse proxying. If you never need to change routing without a reload, Envoy is more machinery than the problem requires.

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.

Envoy: What does it cost?

Nothing to licence; it is Apache 2.0 and there is no paid edition. The cost is engineering time and, for most organisations, a commercial control plane or cloud service that packages it.

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.

Envoy: Can I write extensions without C++?

You can write Lua filters or proxy-wasm modules in Rust, Go, C++ or AssemblyScript. Both carry per-request overhead compared with a native filter, and the Wasm path has been slower to stabilise than the project originally projected.

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.

Envoy: Is it a CNCF project?

Yes, it is a graduated CNCF project licensed under Apache 2.0, which means the trademark and governance sit with the foundation rather than with Lyft or any vendor.

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