Last refreshed 2026-06-15. Next refresh: weekly.
Why use Titan Text Lite on AWS Bedrock?
AWS Bedrock offers Titan Text Lite with pay-as-you-go pricing at $0.15/1M input tokens. 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.
Setup recipe
Python + curlpip install boto3export AWS_ACCESS_KEY_ID=...import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="titan-text-lite",titan-text-liteRequest 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-lite",
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.
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.15 |
| Output tokens | $0.20 |
Capabilities
About Titan Text Lite
Amazon Titan Text Lite is a lightweight and efficient large language model designed specifically for English-language tasks. It excels in fine-tuning applications such as summarization and copywriting, providing a cost-effective and highly customizable solution for users. Despite being smaller and less expensive than other Titan Text models, it supports a variety of text generation tasks. The model can handle a maximum context length of 4,000 tokens, ensuring flexibility in handling longer text inputs while maintaining efficiency and performance.