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Databases · head to head

Apache Solr vs BigQuery

Apache Solr logo

Apache Solr

Databases

Enterprise search platform built on Apache Lucene

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 Solr xML-heavy configuration and a developer experience that feels dated beside newer engines; 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 Solr covers Lucene-based indexing, BigQuery covers Serverless compute.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Solr and BigQuery differ
AttributeApache SolrBigQuery
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 Solr

  • Lucene-based indexing
  • Faceted search
  • SolrCloud
  • Schema control

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 Solr

  • Library, archive and catalogue search where faceting is centralnot BigQuery
  • Long-lived enterprise deployments valuing stability over noveltynot BigQuery
  • Search requiring precise, explicitly configured relevance tuningnot BigQuery

BigQuery

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

Where each one falls short

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

Apache Solr

  • XML-heavy configuration and a developer experience that feels dated beside newer engines
  • SolrCloud depends on ZooKeeper, adding a component Elasticsearch removed years ago
  • Smaller mindshare now, so newer tutorials, hiring and integrations favour Elasticsearch
  • Considerably heavier than a purpose-built application search engine

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 Solr

Free
  • Apache SolrFree
    • 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 Solr if

  • You need lucene-based indexing.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want faceted search.
Apache Solr 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 Solr or BigQuery better?
Neither clearly leads. Apache Solr 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 Solr or BigQuery?
Apache Solr starts at Free and BigQuery at Free.
Does Apache Solr or BigQuery run on more platforms?
Apache Solr runs on Linux, Docker, Kubernetes, Self-hosted. BigQuery runs on Web, Cloud API.
Can I use Apache Solr for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Solr best used for?
Apache Solr is most often used for library, archive and catalogue search where faceting is central, long-lived enterprise deployments valuing stability over novelty, search requiring precise, explicitly configured relevance tuning. Of those, library, archive and catalogue search where faceting is central and long-lived enterprise deployments valuing stability over novelty are not what BigQuery is typically brought in for.
What can Apache Solr do that BigQuery cannot?
Apache Solr covers Lucene-based indexing, Faceted search, SolrCloud, Schema control. BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

Answered from the vendors’ own pages

Apache Solr: Is Apache Solr 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 Solr: Solr or Elasticsearch?

Both are built on Lucene. Elasticsearch has the larger ecosystem and a friendlier API; Solr is very mature and strong on faceted search, and remains common in library and catalogue systems.

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 Solr: Is Solr still maintained?

Yes, actively, as a top-level Apache project.

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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