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Databases · head to head

BigQuery vs Zeplo

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
Zeplo logo

Zeplo

APIs

Message queue as a URL prefix, adding retries and delays to any HTTP request

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.; Zeplo zeplo sits directly in the execution path of your background work, so an outage at a very small vendor means your jobs do not run at all, and there is no published SLA below Enterprise.
  • They diverge on capability: BigQuery covers Serverless compute, Zeplo covers URL prefix interface.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which BigQuery and Zeplo actually diverge.

Attributes where BigQuery and Zeplo differ
AttributeBigQueryZeplo
Pricing modelusage-basedPer month by request count
PlatformsWeb, Cloud APIWeb, Cloud
CategoryDatabasesAPIs
Founded2008Unknown

Identical on both: starting price (Free), free tier (Yes), 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 Zeplo

  • URL prefix interface
  • Retries with backoff
  • Delayed delivery
  • Cron scheduling
  • Concurrency limits
  • Deduplication keys
  • Request logs
  • Team workspaces

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

Zeplo

  • A Vercel or Cloudflare Workers application that needs retried background jobs without deploying a separate always-on workernot BigQuery
  • A team that wants a webhook receiver protected by a concurrency limit so a burst does not overwhelm a downstream APInot BigQuery
  • A small product needing cron jobs against HTTP endpoints without adding a scheduler to its infrastructurenot BigQuery
  • A developer adding idempotent retries to an unreliable third-party API call with a one-line URL changenot 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.

Zeplo

  • Zeplo sits directly in the execution path of your background work, so an outage at a very small vendor means your jobs do not run at all, and there is no published SLA below Enterprise.
  • The free Developer tier includes only 500 requests a month and one team member, so it is a demonstration allowance rather than a usable free plan for anything real.
  • Overage on the Developer tier is 20 US dollars per 100,000 requests, two hundred times the marginal rate on the Company tier, so exceeding the free allowance without upgrading is expensive.
  • Team members cost 15 US dollars each beyond the plan allowance, which is an odd charge on an infrastructure product billed by request volume.
  • Log retention is 30 days on both published tiers, so anything requiring longer audit trails on job execution has to be logged separately.

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

Zeplo

Free
  • DeveloperFree
    • 500 requests a month included
    • $20 per additional 100,000 requests
    • 1 team member
  • Company$39/month
    • 1,000,000 requests a month included
    • $10 per additional million requests
    • Up to 3 team members
  • Enterprise$undefined/month
    • Volume discounts
    • Custom team size
    • Support with SLA

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 Zeplo if

  • You need url prefix interface.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want retries with backoff.
Zeplo review

Questions people ask

Is BigQuery or Zeplo better?
Neither clearly leads. BigQuery starts at Free and Zeplo at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Zeplo?
BigQuery starts at Free and Zeplo at Free.
Does BigQuery or Zeplo run on more platforms?
BigQuery runs on Web, Cloud API. Zeplo runs on Web, Cloud.
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 Zeplo is typically brought in for.
What can BigQuery do that Zeplo cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Zeplo covers URL prefix interface, Retries with backoff, Delayed delivery, Cron scheduling.

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.

Zeplo: How does Zeplo work?

You prefix your endpoint URL with zeplo.to and add query parameters for retries, delay, cron or concurrency. No SDK or broker is needed.

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.

Zeplo: What does it cost?

Free for 500 requests a month; 39 US dollars a month for a million requests, then 10 dollars per additional million.

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.

Zeplo: Is it suitable for critical jobs?

It is a small vendor in your execution path with no published SLA below Enterprise, so weigh that against the convenience.

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.

Zeplo: Does it work with serverless platforms?

Yes, that is its main use, covering Vercel, Cloudflare Workers, Netlify and similar environments that lack long-running workers.

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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