embedding

Amazon Titan Text Embeddings V2

Amazon Bedrock text embedding model that takes up to 8,192 tokens and returns 1,024, 512 or 256-dimension vectors, for retrieval, search and classification.

AmazonUSA

Levelmodel

Classembedding

Parametersnot in the notes

Context8,192

Releasednot in the notes

Opennessproprietary

Runs onnot in the notes

Statusactive

R&D Topics

Retrieval-augmented generation (RAG)used by Text embeddings for semantic searchproduced by

Identity

Developer: Amazon
Family: not in the notes
Released: – sources
Weights published: –
Record checked: 12 Oct 2026

Where this comes from

Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture: not in the notes
Total parameters: – sources
Active parameters: –
Checkpoint parameters: –
Context length: 8,192 tokens
Input: text
Output: text

Where this comes from

Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness: proprietary
Licence: Proprietary sources
Access routes: API, cloud marketplace

Where this comes from

The licence file governs over registry tags and aggregator labels.

Deployment

Runs on: not in the notes
Footprint: not in the notes
Runtimes: not in the notes

Where this comes from

Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support: not stated
Regional languages: not in the notes
Jakarta hosting: unknown
Languages: English (100+ languages in preview)

Where this comes from

Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Links

Developer

Amazonbig tech, USA

Model

Amazon Titan Text Embeddings V2Amazon, embedding

R&D TopicsRetrieval-Augmented Generation (RAG)Text Embeddings for Semantic Search

Use CasesEmbeddings and Retrieval for SEA LanguagesPublic SectorUse cases of models in the same R&D topic; none is recorded for this model itself.

Who Runs Itnone recordedDeployments of these use cases; none names this model.

Graph

Organisations 1

Amazonbig tech, USADeveloper

R&D topics 2

Retrieval-Augmented Generation (RAG)Grounding generation in retrieved documents.used by Text Embeddings for Semantic SearchTraining encoders that turn text in many languages into vectors so that search, clusterin…produced by

Sources

docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-mode... https://docs.aws.amazon.com/bedrock/latest/userguide/titan-embedding-models.html
Models at a glance - Amazon Bedrock docs.aws.amazon.com https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards.html

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Amazon Titan Text Embeddings V2", https://aurion.semai.id/models/amazon-titan-text-embeddings-v2/, records of October 2026.