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Databases · head to head

DynamoDB vs Trino

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
Free plan
Yes
Visit DynamoDBFull review
Trino logo

Trino

Databases

A distributed SQL engine that queries data where it already lives, across object storage, warehouses and operational databases.

From
Free
Free plan
Yes
Visit TrinoFull review

The short version

  • Trino is free; DynamoDB starts at $0.625/mo.
  • Only DynamoDB lists managed and serverless; only Trino lists federated querying.
  • Prices and features on this page were last checked on 13 November 2026.

Where they differ

Only the attributes on which DynamoDB and Trino actually diverge.

Attributes where DynamoDB and Trino differ
AttributeDynamoDBTrino
Pricing modelUsage-basedOpen-source
PlatformsAWSWeb

Identical on both: starting price (Free), free tier (Yes), category (Databases).

Not yet recorded: founded for Trino.

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 Trino

  • Federated querying
  • Connector architecture
  • Predicate and aggregation pushdown
  • Massively parallel execution
  • Fault-tolerant execution
  • Resource groups
  • Iceberg and Delta table support
  • Standard client protocols

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 Trino
  • Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot Trino
  • Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot Trino
  • Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot Trino

Trino

  • Ad hoc analysis that spans a data lake and one or more operational databases, without building an ingestion pipeline firstnot DynamoDB
  • Serving a BI tool a single SQL endpoint over an estate that is actually several separate storage systemsnot DynamoDB
  • Querying Iceberg or Delta tables on object storage interactively, as the compute layer of a lakehousenot DynamoDB
  • Investigating whether a dataset is worth ingesting, by querying it in place before committing to a pipeline for itnot 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

Trino

  • Trino stores nothing and computes no statistics of its own, so the plan it produces is only as good as the partitioning, file sizes and table statistics on the source; a Hive table of thousands of small files or a lake with no stats produces a slow query the engine cannot improve
  • Federated queries pull data out of the systems they touch, so a join between a lake table and a production Postgres can put a full table scan onto an OLTP database that other applications depend on, and the person who wrote the query will not see the incident it causes
  • Releases come roughly every one to two weeks with no community long-term support line, and deprecations arrive quickly, so you either dedicate someone to keeping current or you buy Starburst Enterprise for a supported long-term version
  • It is memory-based and disk spilling was deprecated in favour of fault-tolerant execution, so a query exceeding cluster memory fails outright rather than degrading; enabling fault-tolerant execution requires an external exchange store on object storage and makes queries measurably slower
  • It is a query engine and not a warehouse: there is no built-in job scheduling, no incremental materialised view maintenance and no transformation framework, so producing curated tables still needs dbt or an equivalent layer that somebody has to own
  • The 2020 fork split the ecosystem, so documentation, connectors, Stack Overflow answers and vendor material written before then describe PrestoDB, which is now a different project with different behaviour, and following the wrong one wastes real time

Pricing, plan by plan

DynamoDB

Free
  • On-Demand CapacityOn request
    • 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 CapacityOn request
    • 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

Trino

Free

No published plan breakdown. See the Trino review.

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 Trino if

  • You need federated querying.
  • You want to start without paying.
  • You also want connector architecture.
Trino review

Questions people ask

Is DynamoDB or Trino better?
Neither clearly leads. DynamoDB starts at Free and Trino at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DynamoDB or Trino?
DynamoDB starts at Free and Trino at Free.
Does DynamoDB or Trino run on more platforms?
DynamoDB runs on AWS. Trino runs on Web.
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 Trino is typically brought in for.
What can DynamoDB do that Trino cannot?
DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes. Trino covers Federated querying, Connector architecture, Predicate and aggregation pushdown, Massively parallel execution.

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.

Trino: What is the difference between Trino and Presto?

They share an origin. The original creators left Meta and renamed their fork from PrestoSQL to Trino in December 2020; PrestoDB continues separately under the Linux Foundation. They have diverged in features, connectors and SQL behaviour, so material written for one may not apply to the other.

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.

Trino: Does Trino replace my data warehouse?

Not on its own. It is compute without storage, scheduling or transformation. Paired with Iceberg or Delta on object storage and something like dbt for modelling it can serve as a lakehouse; used alone it is a query layer over what you already have.

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.

Trino: Why is my federated query slow?

Usually because a connector could not push a filter or aggregation down, so Trino is pulling whole tables across the network to join them itself. The fix is usually better source-side partitioning or statistics, or ingesting that source rather than federating it.

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.

Trino: What happens when a query runs out of memory?

It fails. Disk spilling was deprecated in favour of fault-tolerant execution, which checkpoints to an external exchange store such as S3 and lets long queries survive memory pressure and worker loss, at the cost of noticeably slower execution.

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.

Trino: Is there commercial support?

Yes, from Starburst, which offers Starburst Enterprise with long-term supported releases and Starburst Galaxy as a managed service. The open source project itself has no long-term support line.

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