Softwr

Databases · head to head

Qdrant vs Teradata

Qdrant logo

Qdrant

Databases

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

From
Free
Rated
-
Full reviewWebsite
Teradata logo

Teradata

Databases

Long-established enterprise MPP data warehouse, rebranded in 2026 as the Autonomous Knowledge Platform, sold for cloud, on-premises and hybrid.

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only Qdrant has a free tier, so it costs nothing to try first.
  • Each has a real cost: Qdrant free tier extremely limited (0.5 vCPU, 1GB RAM, 4GB disk); suitable only for experiments; Teradata licensing is negotiated rather than published, so there is no way to compare total cost against a consumption-priced warehouse without entering a sales cycle, and the comparison is only ever as good as the workload profile you gave them.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Qdrant and Teradata actually diverge.

Attributes where Qdrant and Teradata differ
AttributeQdrantTeradata
Starting priceFreeOn request
Pricing modelfreemiumquote
Free tierYesNo
PlatformsCloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming)Web

Identical on both: 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 Qdrant

Nothing recorded that Teradata does not also cover.

Only in Teradata

  • Massively parallel architecture
  • Workload management
  • Mature cost-based optimiser
  • Cloud, on-premises and hybrid
  • Bulk load utilities
  • BTEQ scripting
  • In-database analytics
  • Enterprise Vector Store

What people use each for

The jobs each tool is most often brought in to do.

Qdrant

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

Teradata

  • A large existing Teradata estate where the practical question is which workloads to migrate first rather than whether to adoptnot Qdrant
  • High-concurrency mixed workloads where hundreds of analysts and scheduled jobs contend and predictable prioritisation matters more than peak single-query speednot Qdrant
  • Regulated reporting where the same query must produce the same answer for years and the audit trail of the existing implementation has valuenot Qdrant
  • Hybrid deployments where regulatory or data-residency rules keep a portion of the warehouse on-premises while the rest moves to cloudnot Qdrant

Where each one falls short

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

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

Teradata

  • Licensing is negotiated rather than published, so there is no way to compare total cost against a consumption-priced warehouse without entering a sales cycle, and the comparison is only ever as good as the workload profile you gave them.
  • The SQL dialect and the loading utilities are Teradata-specific, so every stored procedure, macro and BTEQ script written against the platform is migration debt that grows with each release you ship.
  • Primary index choice determines data distribution, and a poorly chosen index concentrates rows on a few processing units, which surfaces as one slow query rather than an error and needs a specialist to diagnose.
  • The skills market is contracting, so DBA and workload-management expertise is expensive to hire, hard to replace when someone retires, and increasingly hard to buy from consultancies whose own bench has moved to cloud warehouses.
  • The 2026 renaming of Vantage, VantageCloud, ClearScape and QueryGrid split documentation, runbooks and vendor material across two naming systems, so searching for an error or a configuration now returns results for a product that is described under a different name.

Pricing, plan by plan

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

Teradata

On request

No published plan breakdown. See the Teradata review.

Which should you pick?

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

Choose Teradata if

  • You need massively parallel architecture.
  • You also want workload management.
Teradata review

Questions people ask

Is Qdrant or Teradata better?
Neither clearly leads. Qdrant starts at Free and Teradata at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Qdrant or Teradata?
Qdrant has a free tier; the other does not. Paid plans start at Free for Qdrant and On request for Teradata.
Does Qdrant or Teradata run on more platforms?
Qdrant runs on Cloud (AWS, GCP, Azure), Kubernetes, Self-hosted, Edge (beta), Serverless (coming). Teradata runs on Web.
Can I use Qdrant for free?
Yes. Qdrant has a free tier, so you can try it without paying. Teradata starts at On request.
What is Qdrant best used for?
Qdrant is most often used for retrieval-augmented generation (rag) backends for llm applications, semantic search across large document corpora, multimodal retrieval (text, images, video) for recommendation systems, similarity-based product or content recommendations. Of those, retrieval-augmented generation (rag) backends for llm applications and semantic search across large document corpora are not what Teradata is typically brought in for.
What can Qdrant do that Teradata cannot?
Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

Answered from the vendors’ own pages

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
Teradata: Is Teradata only on-premises?

No. It is sold for cloud, on-premises and hybrid deployment, and the cloud offering is now branded Teradata Cloud. A large part of the installed base is still on-premises or hybrid.

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
Teradata: How does it compare to Snowflake or BigQuery?

On raw elasticity and cost transparency the cloud warehouses win. On mixed-workload concurrency management against a large existing query estate Teradata is still hard to replace, which is why migrations off it take years rather than quarters.

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
Teradata: Why do organisations stay on it?

Because the cost of leaving is the estate, not the data. Thousands of procedures, scripts and extracts written in a proprietary dialect have to be rewritten and revalidated, and in regulated reporting that revalidation is the expensive part.

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
Teradata: What changed in the 2026 rebrand?

Vantage became the Autonomous Knowledge Platform, VantageCloud became Teradata Cloud, ClearScape Analytics became AI Studio and QueryGrid became Fabric. The underlying products are continuous with what came before.

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
Teradata: Can it handle AI and vector workloads?

It has added an Enterprise Vector Store and in-database analytics branded AI Studio. Whether that is preferable to moving the data into a purpose-built vector store depends on how much of your data already lives in the warehouse.

Share

Related pages

More on Qdrant

More on Teradata

Keep looking

Other head to heads