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Databases · head to head

BigQuery vs Logi Analytics

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
Logi Analytics logo

Logi Analytics

Business Intelligence

Embedded analytics for developers

From
$800/month
Rated
-
Full reviewWebsite

The short version

  • Only BigQuery has a free tier, so it costs nothing to try first.
  • 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.; Logi Analytics no pricing information published on website; quote-based model requires completing request form
  • They diverge on capability: BigQuery covers Serverless compute, Logi Analytics covers Low-code Embedding.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Logi Analytics actually diverge.

Attributes where BigQuery and Logi Analytics differ
AttributeBigQueryLogi Analytics
Starting priceFree$800/month
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsWeb, Cloud APIWeb
CategoryDatabasesBusiness Intelligence
Founded20082003

Identical on both: user rating (Not yet rated).

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 Logi Analytics

  • Low-code Embedding
  • Self-service Analytics
  • Data Connectors
  • Custom Branding
  • Multi-tenancy
  • SQL Server
  • PostgreSQL
  • MySQL

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 Logi Analytics
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Logi Analytics
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Logi Analytics
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Logi Analytics

Logi Analytics

  • Embedded analytics and data visualization for software applicationsnot BigQuery
  • Custom business intelligence application development with low-code platformnot 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.

Logi Analytics

  • No pricing information published on website; quote-based model requires completing request form

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

Logi Analytics

$800/month
  • Team$800/month
    • Embedded Dashboards
    • Self-service
    • APIs
  • EnterpriseFree
    • Full Platform
    • Multi-tenant
    • Custom Development

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 Logi Analytics if

  • You need low-code embedding.
  • You also want self-service analytics.
Logi Analytics review

Questions people ask

Is BigQuery or Logi Analytics better?
Neither clearly leads. BigQuery starts at Free and Logi Analytics at $800/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Logi Analytics?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and $800/month for Logi Analytics.
Does BigQuery or Logi Analytics run on more platforms?
BigQuery runs on Web, Cloud API. Logi Analytics runs on Web.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Logi Analytics starts at $800/month.
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 Logi Analytics is typically brought in for.
What can BigQuery do that Logi Analytics cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Logi Analytics covers Low-code Embedding, Self-service Analytics, Data Connectors, Custom Branding.

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.

Logi Analytics: How much does Logi Analytics cost?

Logi Analytics does not publish pricing on its website. The platform uses a quote-based pricing model where prospective customers must fill out a request form on their website to speak with a sales expert about customized pricing based on their specific needs.

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.

Logi Analytics: What Logi Analytics products are available?

Logi Analytics offers four main products: Logi Composer for customizable dashboards, Logi Report for pixel-perfect reporting, Logi Info as a full SaaS analytics application, and Logi Symphony as a comprehensive analytics suite.

Source
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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