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Databases · head to head

ArangoDB vs BigQuery

ArangoDB logo

ArangoDB

Databases

Multi-model database for graph, document, and search

From
Free
Rated
-
Full reviewWebsite
BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Rated
-
Full reviewWebsite

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; BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • They diverge on capability: ArangoDB covers Multi-model Support, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which ArangoDB and BigQuery actually diverge.

Attributes where ArangoDB and BigQuery differ
AttributeArangoDBBigQuery
Pricing modelfreemiumusage-based
PlatformsLinux, Windows, Mac, Docker, WebWeb, Cloud API
Founded20142008

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 BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

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 BigQuery
  • Vector and full-text search alongside graph traversalnot BigQuery
  • Avoiding separate stores for related and unstructured datanot BigQuery
  • Backing AI applications needing both graph context and vectorsnot BigQuery

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot ArangoDB
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot ArangoDB
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot ArangoDB
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot 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

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.

Pricing, plan by plan

ArangoDB

Free
  • CommunityFree
    • All data models
    • AQL queries
    • Full-text search
  • ArangoGraph$99/month
    • Managed service
    • Graph analytics
    • Enterprise support

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

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.
ArangoDB review

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.
BigQuery review

Questions people ask

Is ArangoDB or BigQuery better?
Neither clearly leads. ArangoDB starts at Free and BigQuery at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ArangoDB or BigQuery?
ArangoDB starts at Free and BigQuery at Free.
Does ArangoDB or BigQuery run on more platforms?
ArangoDB runs on Linux, Windows, Mac, Docker, Web. BigQuery runs on Web, Cloud API.
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 BigQuery is typically brought in for.
What can ArangoDB do that BigQuery cannot?
ArangoDB covers Multi-model Support, AQL Query Language, Graph Traversals, Full-text Search. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

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.

Source
BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

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.

Source
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

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