Databases · head to head
ArangoDB vs DynamoDB

ArangoDB
Databases
Multi-model database for graph, document, and search
- From
- Free
- Rated
- -

DynamoDB
Databases
AWS-only managed key-value and document database with fixed per-partition throughput limits and no ad hoc queries.
- From
- Free
- Rated
- -
The short version
- Each has a real cost: ArangoDB the company has repositioned around a wider platform, so ArangoDB is now described as the foundation inside Arango rather than the product itself; 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.
- They diverge on capability: ArangoDB covers Multi-model Support, DynamoDB covers Managed and serverless.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which ArangoDB and DynamoDB actually diverge.
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 ArangoDB
- Multi-model Support
- AQL Query Language
- Graph Traversals
- Full-text Search
- ACID Transactions
- SmartGraphs
- Satellite Collections
- Foxx Microservices
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
What people use each for
The jobs each tool is most often brought in to do.
ArangoDB
- Graph, document and key-value data in one databasenot DynamoDB
- Vector and full-text search alongside graph traversalnot DynamoDB
- Avoiding separate stores for related and unstructured datanot DynamoDB
- Backing AI applications needing both graph context and vectorsnot DynamoDB
DynamoDB
- High-volume keyed workloads such as sessions, shopping carts, device state or user profiles where the access pattern is fixed and knownnot ArangoDB
- Traffic that spikes unpredictably, where on-demand capacity absorbs a burst without a capacity-planning exercisenot ArangoDB
- Serverless applications on Lambda, where an HTTP-based datastore avoids the connection pooling problem relational databases havenot ArangoDB
- Event or telemetry ingestion where writes vastly outnumber reads and each record is retrieved by a known identifiernot ArangoDB
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
ArangoDB
- The company has repositioned around a wider platform, so ArangoDB is now described as the foundation inside Arango rather than the product itself
- Neither the community licence terms nor cloud pricing are stated on the main site
- arangodb.com redirects to arango.ai
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.
Pricing, plan by plan
ArangoDB
Free- CommunityFree
- All data models
- AQL queries
- Full-text search
- ArangoGraph$99/month
- Managed service
- Graph analytics
- Enterprise support
DynamoDB
Free- On-Demand Capacity$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
Which should you pick?
Choose ArangoDB if
- You need multi-model support.
- You want to start without paying.
- You work on Linux, Windows, Mac, Docker, Web.
- You also want aql query language.
Choose DynamoDB if
- You need managed and serverless.
- You want to start without paying.
- You work on AWS.
- You also want predictable latency.
Questions people ask
- Is ArangoDB or DynamoDB better?
- Neither clearly leads. ArangoDB starts at Free and DynamoDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, ArangoDB or DynamoDB?
- ArangoDB starts at Free and DynamoDB at Free.
- Does ArangoDB or DynamoDB run on more platforms?
- ArangoDB runs on Linux, Windows, Mac, Docker, Web. DynamoDB runs on AWS.
- Can I use ArangoDB for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is ArangoDB best used for?
- ArangoDB is most often used for graph, document and key-value data in one database, vector and full-text search alongside graph traversal, avoiding separate stores for related and unstructured data, backing ai applications needing both graph context and vectors. Of those, graph, document and key-value data in one database and vector and full-text search alongside graph traversal are not what DynamoDB is typically brought in for.
- What can ArangoDB do that DynamoDB cannot?
- ArangoDB covers Multi-model Support, AQL Query Language, Graph Traversals, Full-text Search. DynamoDB covers Managed and serverless, Predictable latency, On-demand or provisioned capacity, Global secondary indexes.
Answered from the vendors’ own pages
ArangoDB: How is ArangoDB priced?
ArangoDB offers Community Edition (free) and Enterprise Edition. Pricing is customized based on deployment model (self-managed, managed cloud AWS/GCP, or OEM/embedded) and customer requirements. Contact Arango for a quote.
SourceDynamoDB: 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.
ArangoDB: What deployment options does ArangoDB offer?
ArangoDB can be deployed self-managed on customer infrastructure, as managed cloud (Arango Managed Platform on AWS/GCP), or as OEM/embedded solutions.
SourceDynamoDB: 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.
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.
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.
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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