Databases · head to head
Apache Spark vs Chroma
Apache Spark
Databases
A distributed engine for batch, SQL, streaming and machine learning workloads over data that does not fit on one machine.
- From
- Free
- Free plan
- Yes
Chroma
Databases
Apache 2.0 vector and full-text search engine that runs as an embedded library, a single server or a distributed cloud service.
- From
- Free
- Free plan
- Yes
The short version
- Apache Spark is free; Chroma starts at $250/mo.
- Only Apache Spark lists unified engine; only Chroma lists embedded mode.
- Prices and features on this page were last checked on 13 November 2026.
Where they differ
Only the attributes on which Apache Spark and Chroma actually diverge.
| Attribute | Apache Spark | Chroma |
|---|---|---|
| Pricing model | Open-source | Usage-based |
Identical on both: starting price (Free), free tier (Yes), platforms (Web), category (Databases).
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 Chroma
- Embedded mode
- Single-node server
- Distributed architecture
- Vector search
- Full-text search
- Metadata filtering
- Consistent API across modes
- Multi-language clients
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 Chroma
- Building and maintaining a lakehouse on Iceberg or Delta Lake, where Spark handles both the writes and the compactionnot Chroma
- Feature engineering and model training across datasets too large to fit in pandas on one nodenot Chroma
- Migrating legacy MapReduce or Hive workloads onto an engine that is still actively developed and widely supported by cloud vendorsnot Chroma
Chroma
- Prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent storenot Apache Spark
- Agent memory in a single application process, where an embedded store avoids adding a network dependencynot Apache Spark
- A departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroomnot Apache Spark
- Local and CI testing of retrieval code with the same client library used in productionnot 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
Chroma
- On a single node, available memory sets a hard upper bound on collection size, roughly 245,000 records per gigabyte of RAM at 1024 dimensions, so capacity planning is a memory purchase and the ceiling arrives without warning
- Single-node queries parallelise only up to the number of vCPUs, after which requests queue and latency rises linearly with concurrency, so throughput problems appear as a slow application rather than as errors
- The distributed deployment behind Chroma Cloud is a different architecture from the embedded library, so latency, consistency and failure behaviour observed in a local prototype do not predict production behaviour
- The open source server has no built-in authentication or multi-tenancy worth relying on, so a self-hosted deployment needs its own auth proxy and network controls before anything untrusted can reach it
- The project has moved quickly through major internal rewrites and version changes, so upgrades have historically involved data migrations and client changes, and pinning versions is necessary rather than cautious
Pricing, plan by plan
Apache Spark
FreeNo published plan breakdown. See the Apache Spark review.
Chroma
Free- StarterFree
- 10 databases
- 10 team members
- Community Slack access
- Team$250/mo
- 100 databases
- 30 team members
- $100 in included credits
- EnterpriseOn request
- Unlimited databases
- Unlimited team members
- Dedicated support
Which should you pick?
Choose Apache Spark if
- You need unified engine.
- You want to start without paying.
- You also want catalyst optimiser.
Choose Chroma if
- You need embedded mode.
- You want to start without paying.
- You also want single-node server.
Questions people ask
- Is Apache Spark or Chroma better?
- Neither clearly leads. Apache Spark starts at Free and Chroma at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Spark or Chroma?
- Apache Spark starts at Free and Chroma at Free.
- Does Apache Spark or Chroma 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 Chroma is typically brought in for.
- What can Apache Spark do that Chroma cannot?
- Apache Spark covers Unified engine, Catalyst optimiser, DataFrame and SQL APIs, Structured Streaming. Chroma covers Embedded mode, Single-node server, Distributed architecture, Vector search.
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.
Chroma: Do I need to run a server?
No. Chroma runs embedded in your process with persistence to a local directory, which is how most projects start. The server and distributed modes exist for when multiple clients or larger collections require them.
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.
Chroma: How large can a single node get?
The project puts single-node deployments at fewer than about ten million records across a handful of collections, with collection size bounded by system memory at roughly 245,000 records per gigabyte at 1024 dimensions.
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.
Chroma: Is Chroma Cloud the same software?
It is the same API and project, but the distributed deployment is a different architecture, using independent services, object storage and SSD caches rather than a single process. Behaviour under load differs accordingly.
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.
Chroma: How does it compare with pgvector?
pgvector keeps vectors in a Postgres database you already operate, with SQL, joins and transactions. Chroma is a dedicated retrieval engine with a lower setup cost and a retrieval-shaped API. If you already run Postgres, pgvector removes a system; if you do not, Chroma removes a decision.
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.
Chroma: What licence is it under?
Apache 2.0, which permits self-hosting and embedding in commercial products without a competing-use restriction.
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