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Databases · head to head

DynamoDB vs StarRocks

DynamoDB logo

DynamoDB

Databases

AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.

From
Free
Rated
-
Full reviewWebsite
StarRocks logo

StarRocks

Databases

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: DynamoDB access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.; 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.
  • They diverge on capability: DynamoDB covers Managed and serverless, StarRocks covers Cost-based optimiser.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which DynamoDB and StarRocks actually diverge.

Attributes where DynamoDB and StarRocks differ
AttributeDynamoDBStarRocks
Pricing modelusage-basedOpen source, no licence fee
PlatformsAWSLinux, Docker, Kubernetes
Founded2006Unknown

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

  • Managed and serverless
  • Predictable latency
  • On-demand or provisioned capacity
  • Global secondary indexes
  • Transactions
  • DynamoDB Streams
  • Global tables
  • Point-in-time recovery

Only in StarRocks

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

What people use each for

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

DynamoDB

  • High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot StarRocks
  • Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot StarRocks
  • Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot StarRocks
  • Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot StarRocks

StarRocks

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

Where each one falls short

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

DynamoDB

  • Access patterns must be designed into the key schema before launch; a query nobody anticipated needs a new global secondary index, which is a full extra copy of the projected attributes billed as storage and as writes, or an offline migration.
  • Global secondary indexes are eventually consistent and cannot be read strongly, so a read-after-write against an index can legitimately miss the item that was just written, and application code must be written to tolerate that.
  • Per-partition throughput is capped at roughly 3,000 read and 1,000 write units, so a hot key throttles even when the table has spare capacity overall, and the only real fix is changing the key design to spread the load.
  • Items are limited to 400 KB and query results paginate at 1 MB, so large or list-shaped data has to be split, offloaded to S3 with a pointer, or read through pagination loops that complicate every consumer.
  • It runs only on AWS and the API is proprietary rather than a standard, so moving the data layer means rewriting it; ScyllaDB's Alternator is the only meaningfully compatible target and it brings a much smaller ecosystem.

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.

Pricing, plan by plan

DynamoDB

Free
  • On-Demand Capacity$null/usage-based
    • Pay-per-request pricing with automatic scaling
    • Read: 0.5 RRU per 4 KB (eventually consistent), 1 RRU per 4 KB (strongly consistent), 2 RRU per 4 KB (transactional)
    • Write: 1 WRU per 1 KB
  • Provisioned Capacity$null/hourly
    • Fixed hourly charges based on reserved capacity
    • RCU rate: $0.00013 per hour (Standard)
    • WCU rate: $0.00065 per hour (Standard)
  • Standard Table Class Storage$0.25/per GB/month
    • $0.25 per GB/month after free tier
    • First 25 GB free per month (free tier)
  • Standard-Infrequent Access Table Class$0.1/per GB/month
    • $0.10 per GB/month

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

Which should you pick?

Choose DynamoDB if

  • You need managed and serverless.
  • You want to start without paying.
  • You work on AWS.
  • You also want predictable latency.
DynamoDB review

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

Questions people ask

Is DynamoDB or StarRocks better?
Neither clearly leads. DynamoDB starts at Free and StarRocks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DynamoDB or StarRocks?
DynamoDB starts at Free and StarRocks at Free.
Does DynamoDB or StarRocks run on more platforms?
DynamoDB runs on AWS. StarRocks runs on Linux, Docker, Kubernetes.
Can I use DynamoDB for free?
Both have a free tier, so you can try either at no cost before committing.
What is DynamoDB best used for?
DynamoDB is most often used for high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known, traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise, serverless applications on lambda, where an http-based datastore avoids the connection pooling problem relational databases have, event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifier. Of those, high-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and known and traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercise are not what StarRocks is typically brought in for.
What can DynamoDB do that StarRocks cannot?
DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes. StarRocks covers Cost-based optimiser, Lakehouse query engine, Primary key tables, Materialised views.

Answered from the vendors’ own pages

DynamoDB: On-demand or provisioned capacity?

On-demand suits unpredictable or spiky traffic and removes capacity planning. Provisioned with autoscaling is considerably cheaper for steady high-volume workloads. Tables can be switched between them, though not arbitrarily often.

StarRocks: Is StarRocks open source?

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

DynamoDB: Can I run DynamoDB outside AWS?

No. DynamoDB Local exists for development and testing only. For a production-compatible alternative elsewhere, ScyllaDB's Alternator implements the DynamoDB API, but it is a different system with a different ecosystem.

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.

DynamoDB: Can I run ad hoc queries or analytics?

Not on the table itself. Scans are slow and expensive at scale. The usual pattern is to export to S3 or stream changes out and query them in Athena, Redshift or another analytical engine.

StarRocks: Who maintains it?

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

DynamoDB: Is single-table design necessary?

It is the pattern that gets the most from DynamoDB when access patterns are well known, because it lets related items be retrieved in one query. It also makes the model harder to evolve, so many teams reasonably choose multiple simpler tables and accept extra requests.

StarRocks: Can it query Iceberg tables directly?

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

DynamoDB: What are the real limits I should design around?

400 KB per item, 1 MB per query or scan page, 100 items per transaction, roughly 3,000 read and 1,000 write units per partition, and eventual consistency on global secondary indexes.

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