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OpenSearch vs Teradata

OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

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 OpenSearch has a free tier, so it costs nothing to try first.
  • Each has a real cost: OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one; 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.
  • They diverge on capability: OpenSearch covers Full-text search, Teradata covers Massively parallel architecture.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which OpenSearch and Teradata actually diverge.

Attributes where OpenSearch and Teradata differ
AttributeOpenSearchTeradata
Starting priceFreeOn request
Pricing modelOpen source, no licence fee; managed services billed separatelyquote
Free tierYesNo
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb

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 OpenSearch

  • Full-text search
  • OpenSearch Dashboards
  • Log analytics
  • Vector search

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.

OpenSearch

  • Log and observability storage where an Apache-2.0 licence is a requirementnot Teradata
  • Replacing Elasticsearch after the licence change without changing architecturenot Teradata
  • Search plus analytics on one cluster rather than two systemsnot Teradata

Teradata

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

Where each one falls short

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

OpenSearch

  • Diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • Operationally heavy in the way Elasticsearch is: cluster sizing, shard strategy and JVM tuning are ongoing work
  • Smaller ecosystem of third-party tooling than Elasticsearch, which most integrations still target first
  • Overkill for plain application search, where a dedicated search engine is far simpler

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

OpenSearch

Free
  • OpenSearchFree
    • Full functionality
    • Self-hosted
    • No usage limits

Teradata

On request

No published plan breakdown. See the Teradata review.

Which should you pick?

Choose OpenSearch if

  • You need full-text search.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want opensearch dashboards.
OpenSearch review

Choose Teradata if

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

Questions people ask

Is OpenSearch or Teradata better?
Neither clearly leads. OpenSearch 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, OpenSearch or Teradata?
OpenSearch has a free tier; the other does not. Paid plans start at Free for OpenSearch and On request for Teradata.
Does OpenSearch or Teradata run on more platforms?
OpenSearch runs on Linux, Docker, Kubernetes, Self-hosted. Teradata runs on Web.
Can I use OpenSearch for free?
Yes. OpenSearch has a free tier, so you can try it without paying. Teradata starts at On request.
What is OpenSearch best used for?
OpenSearch is most often used for log and observability storage where an apache-2.0 licence is a requirement, replacing elasticsearch after the licence change without changing architecture, search plus analytics on one cluster rather than two systems. Of those, log and observability storage where an apache-2.0 licence is a requirement and replacing elasticsearch after the licence change without changing architecture are not what Teradata is typically brought in for.
What can OpenSearch do that Teradata cannot?
OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics, Vector search. Teradata covers Massively parallel architecture, Workload management, Mature cost-based optimiser, Cloud, on-premises and hybrid.

Answered from the vendors’ own pages

OpenSearch: Is OpenSearch free?

Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.

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.

OpenSearch: Why does OpenSearch exist?

Elastic moved Elasticsearch off the Apache 2.0 licence in 2021. AWS forked the last Apache-licensed version, and the project now sits under the Linux Foundation.

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.

OpenSearch: Is OpenSearch compatible with Elasticsearch?

It was at the 7.10 fork point. Both have developed independently since, so compatibility weakens with every release and should be verified for the features you use.

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.

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.

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.

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