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Databases · head to head

BigQuery vs NATS

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
NATS logo

NATS

Databases

High-performance messaging system for cloud-native applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; NATS core NATS has no persistence at all, so messages are lost if no subscriber is listening
  • They diverge on capability: BigQuery covers Serverless compute, NATS covers Very low latency.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and NATS actually diverge.

Attributes where BigQuery and NATS differ
AttributeBigQueryNATS
Pricing modelusage-basedOpen source, no licence fee
PlatformsWeb, Cloud APILinux, macOS, Windows, Docker, Kubernetes
Founded2008Unknown

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 BigQuery

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

Only in NATS

  • Very low latency
  • JetStream
  • Single binary
  • Request-reply

What people use each for

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

BigQuery

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

NATS

  • Service-to-service messaging where latency is the binding constraintnot BigQuery
  • Edge and IoT messaging where a lightweight broker mattersnot BigQuery
  • Replacing a heavier broker when the workload does not need its guaranteesnot BigQuery

Where each one falls short

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

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.

NATS

  • Core NATS has no persistence at all, so messages are lost if no subscriber is listening
  • JetStream adds the durability but also the operational complexity NATS is chosen to avoid
  • A much smaller ecosystem than Kafka or RabbitMQ, with fewer connectors and integrations
  • Fewer people know it, so hiring and existing organisational knowledge favour the alternatives

Pricing, plan by plan

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

NATS

Free
  • NATSFree
    • Full functionality
    • No usage limits
    • Community support

Which should you pick?

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

Choose NATS if

  • You need very low latency.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want jetstream.
NATS review

Questions people ask

Is BigQuery or NATS better?
Neither clearly leads. BigQuery starts at Free and NATS at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or NATS?
BigQuery starts at Free and NATS at Free.
Does BigQuery or NATS run on more platforms?
BigQuery runs on Web, Cloud API. NATS runs on Linux, macOS, Windows, Docker, Kubernetes.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
What is BigQuery best used for?
BigQuery is most often used for a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place, bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running, event and clickstream analytics ingested continuously through the storage write api and queried without a load window, analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what NATS is typically brought in for.
What can BigQuery do that NATS cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. NATS covers Very low latency, JetStream, Single binary, Request-reply.

Answered from the vendors’ own pages

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.

NATS: Is NATS free?

Yes, open source and CNCF-graduated. Synadia sells a managed service.

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.

NATS: Does NATS persist messages?

Core NATS does not — it is fire-and-forget. JetStream adds persistence, streaming and replay when you need them.

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.

NATS: NATS or Kafka?

NATS is far lighter and lower latency, and much simpler to run. Kafka is the answer when you need a durable replayable log and a large connector ecosystem.

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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