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Databases · head to head

StarRocks vs Weaviate

StarRocks logo

StarRocks

Databases

Apache 2.0 MPP analytical database built for joins on open table formats

From
Free
Rated
-
Full reviewWebsite
Weaviate logo

Weaviate

Machine Learning

Open-source vector database

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: StarRocks self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
  • They diverge on capability: StarRocks covers Cost-based optimiser, Weaviate covers Vector and keyword search.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which StarRocks and Weaviate actually diverge.

Attributes where StarRocks and Weaviate differ
AttributeStarRocksWeaviate
Pricing modelOpen source, no licence feefreemium
PlatformsLinux, Docker, KubernetesLinux, Mac, Windows, Web
CategoryDatabasesMachine Learning
FoundedUnknown2019

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 StarRocks

  • Cost-based optimiser
  • Lakehouse query engine
  • Primary key tables
  • Materialised views
  • Shared-data mode
  • MySQL wire protocol

Only in Weaviate

  • Vector and keyword search
  • Built-in vectorizers
  • GraphQL API
  • Multi-tenancy
  • Hybrid search
  • OpenAI
  • Hugging Face
  • Cohere

What people use each for

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

StarRocks

  • Customer-facing analytics where queries join a fact table to several dimensions and must return in well under a secondnot Weaviate
  • Querying an Iceberg lakehouse directly without copying data into a proprietary warehouse formatnot Weaviate
  • Replacing a ClickHouse deployment that has become unmanageable because every new question needs another denormalised tablenot Weaviate
  • Real-time analytics fed by change data capture where rows must be updated in place rather than appendednot Weaviate

Weaviate

  • Running a vector database for semantic and hybrid searchnot StarRocks
  • Generating and storing embeddings alongside the objects they describenot StarRocks

Where each one falls short

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

StarRocks

  • Self-hosting is a genuine operations job: frontend and backend node roles, tablet distribution, compaction and materialised view refresh all need an owner, and there is no small-team-friendly single-binary mode.
  • CelerData is by far the dominant contributor despite Linux Foundation stewardship, so the practical roadmap risk is the same as any single-vendor open source project.
  • It inherits a MySQL-flavoured SQL dialect from its Doris ancestry, so queries written for PostgreSQL, Snowflake or Trino need rewriting rather than porting.
  • Ecosystem support is thinner than ClickHouse or Trino: fewer client libraries, fewer managed hosting options and a much smaller pool of engineers who have run it in production.
  • Memory pressure under concurrent large joins is a common production failure, and the tuning knobs for query memory limits are unforgiving compared with a cloud warehouse that just scales.

Weaviate

  • The free tier caps at 100,000 objects, 1 GB of memory and a single collection
  • Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
  • Premium is a prepaid contract starting at $400 a month rather than pay as you go
  • Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
  • The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond

Pricing, plan by plan

StarRocks

Free
  • StarRocksFree
    • Apache 2.0 licence
    • Linux Foundation governance
    • No usage or node limits
  • CelerData Cloud$undefined/year
    • Managed StarRocks from the primary contributor
    • BYOC and serverless deployment options
    • Enterprise support and SLAs

Weaviate

Free
  • Open SourceFree
    • Full features
    • Self-hosted
  • ServerlessFree
    • Managed service
    • Auto-scaling

Which should you pick?

Choose StarRocks if

  • You need cost-based optimiser.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want lakehouse query engine.
StarRocks review

Choose Weaviate if

  • You need vector and keyword search.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want built-in vectorizers.
Weaviate review

Questions people ask

Is StarRocks or Weaviate better?
Neither clearly leads. StarRocks starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, StarRocks or Weaviate?
StarRocks starts at Free and Weaviate at Free.
Does StarRocks or Weaviate run on more platforms?
StarRocks runs on Linux, Docker, Kubernetes. Weaviate runs on Linux, Mac, Windows, Web.
Can I use StarRocks for free?
Both have a free tier, so you can try either at no cost before committing.
What is StarRocks best used for?
StarRocks is most often used for customer-facing analytics where queries join a fact table to several dimensions and must return in well under a second, querying an iceberg lakehouse directly without copying data into a proprietary warehouse format, replacing a clickhouse deployment that has become unmanageable because every new question needs another denormalised table, real-time analytics fed by change data capture where rows must be updated in place rather than appended. Of those, customer-facing analytics where queries join a fact table to several dimensions and must return in well under a second and querying an iceberg lakehouse directly without copying data into a proprietary warehouse format are not what Weaviate is typically brought in for.
What can StarRocks do that Weaviate cannot?
StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Materialised views. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy.

Answered from the vendors’ own pages

StarRocks: Is StarRocks open source?

Yes, Apache 2.0, governed under the Linux Foundation since 2023.

Weaviate: What pricing options does Weaviate offer?

Weaviate provides a free tier with usage-based pricing, plus enterprise options. Visit the pricing page for detailed information on plans.

Source
StarRocks: How does it differ from ClickHouse?

StarRocks is built for joins across a star schema with a cost-based optimiser; ClickHouse is fastest on denormalised single tables.

Weaviate: Does Weaviate offer customer support?

Yes, support is included with Weaviate's cloud offerings. Enterprise customers receive first-class support from their global team of experts.

Source
StarRocks: Who maintains it?

CelerData, formerly StarRocks Inc, is the dominant contributor and sells the managed service.

Weaviate: Can I deploy Weaviate on my own infrastructure?

Yes. Weaviate is open source and deployment-agnostic. You can run it in your own cloud environment or use their managed cloud service.

Source
StarRocks: Can it query Iceberg tables directly?

Yes, along with Hudi, Delta Lake, Hive and Paimon, with a local cache for repeat queries.

Weaviate: What data security features does Weaviate provide?

Weaviate includes security & governance, RBAC, SOC 2 and HIPAA compliance, along with multi-tenancy and high availability for enterprise requirements.

Source
Weaviate: How do I get started with Weaviate?

Sign up for their cloud tier, create your first dataset, connect an LLM, and build your AI app. Documentation and quickstart guides are available for Python, Go, TypeScript, and JavaScript.

Source
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