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Databases · head to head

BigQuery vs OpsLevel

BigQuery logo

BigQuery

Databases

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

From
Free
Free plan
Yes
Visit BigQueryFull review
OpsLevel logo

OpsLevel

Developer Tools

Internal developer portal that scores service ownership and production readiness

From
On request
Free plan
No
Visit OpsLevelFull review

The short version

  • BigQuery starts at $6.25/mo; OpsLevel prices on request.
  • Only BigQuery lists serverless compute; only OpsLevel lists service catalogue.
  • Prices and features on this page were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and OpsLevel actually diverge.

Attributes where BigQuery and OpsLevel differ
AttributeBigQueryOpsLevel
Starting priceFreeOn request
Pricing modelUsage-basedQuote
Free tierYesNo
PlatformsWeb, APIWeb
CategoryDatabasesDeveloper Tools

Not yet recorded: founded for OpsLevel.

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 OpsLevel

  • Service catalogue
  • Scorecards and rubric
  • Checks
  • Campaigns
  • Self-service actions
  • Slack and Teams integration
  • Catalogue auto-enrichment
  • Deployment on request

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

OpsLevel

  • A platform team that needs to prove every production service has a named owner and an on-call rota before an auditnot BigQuery
  • Driving a fleet-wide migration such as a runtime upgrade across 400 services with a deadline and visible progressnot BigQuery
  • Giving developers a paved road to create a new service from a template without waiting on the platform teamnot BigQuery
  • An organisation where an incident took an hour to route because nobody could identify the owning teamnot 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

OpsLevel

  • OpsLevel does not publish prices at all, not even a starting figure, so you cannot size a budget or compare against Backstage's zero licence cost without entering a sales cycle
  • The Standard plan caps at 50 users, which is below the size at which service ownership becomes a real problem, so most genuine buyers land on Enterprise and its unpublished pricing
  • Scores are only as honest as the metadata teams maintain; where ownership records go stale the rubric produces confident numbers about a catalogue that no longer reflects reality, and leadership acts on them
  • It reports on quality rather than producing it, so it can generate friction between platform teams who set the rubric and product teams who see it as a scoreboard imposed on them without extra headcount to fix the findings
  • As a hosted product it needs read access to source control, cloud accounts and observability tooling, which is a meaningful vendor review in regulated environments; on-premises exists but only on the Enterprise tier

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

OpsLevel

On request
  • StandardOn request
    • Up to 50 users
    • Unlimited catalogued components
    • Scorecards, campaigns and the global rubric
  • EnterpriseOn request
    • Unlimited users
    • Custom integrations
    • Customisable dashboards

Which should you pick?

Choose BigQuery if

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

Choose OpsLevel if

  • You need service catalogue.
  • You also want scorecards and rubric.
OpsLevel review

Questions people ask

Is BigQuery or OpsLevel better?
Neither clearly leads. BigQuery starts at Free and OpsLevel at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or OpsLevel?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for OpsLevel.
Does BigQuery or OpsLevel run on more platforms?
BigQuery runs on Web, API. OpsLevel runs on Web.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. OpsLevel starts at On request.
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 OpsLevel is typically brought in for.
What can BigQuery do that OpsLevel cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. OpsLevel covers Service catalogue, Scorecards and rubric, Checks, Campaigns.

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.

OpsLevel: How much does OpsLevel cost?

It does not publish pricing. Billing is per developer using the portal, with volume discounts, and a quote requires a sales conversation.

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.

OpsLevel: How does it compare with Backstage?

Backstage has no licence fee but you staff a team to build and run it. OpsLevel is hosted with checks, campaigns and scorecards out of the box, and you pay per developer instead.

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.

OpsLevel: Does it require writing catalogue YAML by hand?

No. It auto-discovers and enriches entries from connected systems, though teams still curate ownership and metadata.

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.

OpsLevel: Can it run on premises?

Yes, but only on the Enterprise plan.

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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