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Databases · head to head

BigQuery vs Qdrant

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
Qdrant logo

Qdrant

Databases

High-performance vector database for similarity search and embedding-based retrieval

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.; Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Qdrant actually diverge.

Attributes where BigQuery and Qdrant differ
AttributeBigQueryQdrant
Pricing modelusage-basedfreemium
PlatformsWeb, Cloud APICloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming)
Founded2008Unknown

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 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 Qdrant

Nothing recorded that BigQuery does not also cover.

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

Qdrant

  • Retrieval-augmented generation (RAG) backends for LLM applicationsnot BigQuery
  • Semantic search across large document corporanot BigQuery
  • Multimodal retrieval (text, images, video) for recommendation systemsnot BigQuery
  • Similarity-based product or content recommendationsnot BigQuery
  • Real-time vector indexing for streaming embedding datanot 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.

Qdrant

  • Free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments
  • Standard and Premium pricing usage-based; specific costs not published; requires calculator or quote
  • Requires understanding of embeddings and vector search concepts; not suitable for SQL-only teams
  • Early-stage serverless offering (coming soon) suggests maturity gaps in that deployment model

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

Qdrant

Free
  • FreeFree
    • Single-node cluster
    • 0.5 vCPU
    • 1GB RAM
  • Standard$undefined/usage-based
    • Dedicated resources
    • Flexible scaling
    • High availability
  • Premium$undefined/minimum spend
    • SSO and SAML
    • Private VPC links
    • 99.9% uptime 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 Qdrant if

  • You want to start without paying.
  • You work on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
Qdrant review

Questions people ask

Is BigQuery or Qdrant better?
Neither clearly leads. BigQuery starts at Free and Qdrant at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Qdrant?
BigQuery starts at Free and Qdrant at Free.
Does BigQuery or Qdrant run on more platforms?
BigQuery runs on Web, Cloud API. Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming).
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 Qdrant is typically brought in for.
What can BigQuery do that Qdrant cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering.

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.

Qdrant: Can I use Qdrant for free?

Yes, Qdrant offers a free tier that provides a single node cluster with 0.5 vCPU, 1GB RAM, and 4GB disk space. It includes free cloud inference with selected models and is described as free forever for testing and prototypes.

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.

Qdrant: How is Qdrant Cloud billing calculated?

Billing is calculated based on actual resource usage during each billing period. You are charged hourly for compute (vCPU), memory (GB), storage (GB), backups, and any paid inference tokens used.

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.

Qdrant: What happens if I scale beyond the free tier?

The Standard Tier uses the same usage-based billing model as the free tier but adds features like dedicated resources with flexible scaling, highly available setups with backup and disaster recovery, and a 99.5% uptime SLA.

Source
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.

Qdrant: What SLA does Qdrant offer?

The Standard Tier provides 99.5% uptime SLA. The Premium Tier, which requires a minimum spend for enterprises, offers 99.9% uptime SLA along with single sign-on and private VPC links.

Source
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.

Qdrant: Are there dedicated cloud infrastructure options?

Yes, Qdrant offers Hybrid Cloud (runs on your infrastructure) and Private Cloud (complete isolation) options, both with custom pricing that requires contacting the sales team.

Source
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