Last refreshed 2026-06-15. Next refresh: weekly.
Why use Amazon Titan Text Embeddings V2 on AWS Bedrock?
AWS Bedrock offers Amazon Titan Text Embeddings V2 with competitive pricing. AWS Bedrock is Amazon's fully managed foundation-model service, providing unified API access to top models from Anthropic, Meta, Mistral, and other leading AI labs with built-in tools for RAG, fine-tuning, and AI agent development.
Compare Amazon Titan Text Embeddings V2 across 2 providers to find the best fit for your use caseInput / 1M
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Output / 1M
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Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install boto3Auth
export AWS_ACCESS_KEY_ID=...Call
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="titan-text-embeddings-v2",Model ID
titan-text-embeddings-v2Request example
import boto3
# Reads AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION from env
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="titan-text-embeddings-v2",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])Gotchas
- Use Amazon Bedrock model IDs, e.g. "anthropic.claude-3-opus-20240229-v1:0" for on-demand, or cross-region inference profile IDs like "us.anthropic.claude-opus-4-7-20251101-v1:0". These differ from the public model slug.
- The endpoint template includes a region segment; set the same region in your SDK/client configuration.
- The examples expect AWS_ACCESS_KEY_ID; rename it only if your application config maps the new variable.
Compare Amazon Titan Text Embeddings V2 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| AWS Bedrock | — | — |
| Vercel AI Gateway | $0.02 | — |
Capabilities
No model capability flags are currently sourced.
About Amazon Titan Text Embeddings V2
Amazon Titan Text Embeddings V2 is Amazon's Titan model. It was released 2024-01-01.
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Model Specs
Released2024-01-01