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Databases · head to head

Apache Pulsar vs BigQuery

Apache Pulsar logo

Apache Pulsar

Databases

Cloud-native messaging and streaming with separated storage

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: Apache Pulsar more components than Kafka: brokers, BookKeeper and ZooKeeper each need operating; 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: Apache Pulsar covers Separated storage, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Apache Pulsar and BigQuery actually diverge.

Attributes where Apache Pulsar and BigQuery differ
AttributeApache PulsarBigQuery
Pricing modelOpen source, no licence feeusage-based
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb, Cloud API
FoundedUnknown2008

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 Apache Pulsar

  • Separated storage
  • Queuing and streaming
  • Built-in multi-tenancy
  • Geo-replication

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.

Apache Pulsar

  • Platforms needing both work queues and replayable streams without running two systemsnot BigQuery
  • Multi-tenant messaging where isolation between teams is a requirementnot BigQuery
  • Deployments where storage and traffic grow at genuinely different ratesnot BigQuery

BigQuery

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

Where each one falls short

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

Apache Pulsar

  • More components than Kafka: brokers, BookKeeper and ZooKeeper each need operating
  • Correspondingly harder to run well, and the expertise is rarer than Kafka expertise
  • A much smaller ecosystem of connectors, tooling and hiring pool than Kafka
  • The architectural advantages only pay off at a scale most deployments never reach

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

Apache Pulsar

Free
  • Apache PulsarFree
    • Full functionality
    • No usage limits
    • Community 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 Apache Pulsar if

  • You need separated storage.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want queuing and streaming.
Apache Pulsar 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 Apache Pulsar or BigQuery better?
Neither clearly leads. Apache Pulsar 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, Apache Pulsar or BigQuery?
Apache Pulsar starts at Free and BigQuery at Free.
Does Apache Pulsar or BigQuery run on more platforms?
Apache Pulsar runs on Linux, Docker, Kubernetes, Self-hosted. BigQuery runs on Web, Cloud API.
Can I use Apache Pulsar for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Pulsar best used for?
Apache Pulsar is most often used for platforms needing both work queues and replayable streams without running two systems, multi-tenant messaging where isolation between teams is a requirement, deployments where storage and traffic grow at genuinely different rates. Of those, platforms needing both work queues and replayable streams without running two systems and multi-tenant messaging where isolation between teams is a requirement are not what BigQuery is typically brought in for.
What can Apache Pulsar do that BigQuery cannot?
Apache Pulsar covers Separated storage, Queuing and streaming, Built-in multi-tenancy, Geo-replication. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

Answered from the vendors’ own pages

Apache Pulsar: Is Apache Pulsar free?

Yes, open source under the Apache Software Foundation.

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.

Apache Pulsar: Pulsar or Kafka?

Pulsar separates storage from compute and covers queuing and streaming in one system. Kafka has a far larger ecosystem and hiring pool. Most teams should have a specific reason before choosing Pulsar.

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.

Apache Pulsar: Why does separated storage matter?

Brokers hold no data, so adding or replacing one requires no rebalancing, and storage can grow without adding serving capacity.

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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