Amazon Nova Micro: The Fastest Text Model on AWS Bedrock
Nova Micro is Amazon's text-only model with sub-millisecond time-to-first-token and a $0.035/1M input price - designed for high-volume classification, extraction, and routing pipelines inside AWS infrastructure.
Nova Micro is not designed to compete with GPT-4o or Claude 3.5 Sonnet on reasoning tasks. It is engineered for one purpose: the highest possible throughput at the lowest cost for simple text tasks that do not require image understanding or complex reasoning.
The target workloads:
Classification - route support tickets, categorize emails, classify intent
Extraction - pull structured fields from unstructured text
Routing - determine which specialized model or queue to send a request to
Simple Q&A - answer questions from a provided context passage
Filtering - determine if content meets criteria before expensive processing
For these tasks, Nova Micro's sub-millisecond time-to-first-token (TTFT) changes what is architecturally possible - you can run it synchronously in a request path without adding perceptible latency.
Pricing
Model
Input ($/1M)
Output ($/1M)
Context
Nova Micro
$0.035
$0.140
128k
Nova Lite
$0.060
$0.240
300k
Nova Pro
$0.800
$3.200
300k
Claude 3 Haiku
$0.250
$1.250
200k
Nova Micro is roughly 7x cheaper than Claude 3 Haiku for input tokens - the cheapest general-purpose LLM available on AWS Bedrock.
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import boto3
import json
bedrock = boto3.client("bedrock-runtime", region_name="us-east-1")
def classify_intent(user_message: str) -> str:
response = bedrock.invoke_model(
modelId="amazon.nova-micro-v1:0",
body=json.dumps({
"messages": [
{
"role": "user",
"content": [
{
"text": f"""Classify this customer message into one category.
Categories: BILLING, TECHNICAL, SHIPPING, RETURNS, OTHER
Message: {user_message}
Respond with only the category name."""
}
]
}
],
"inferenceConfig": {
"maxTokens": 10,
"temperature": 0.0,
}
}),
)
result = json.loads(response["body"].read())
return result["output"]["message"]["content"][0]["text"].strip()
# Example usage
intent = classify_intent("I was charged twice for my order last week")
print(intent) # BILLING
Cascaded Routing Pattern with Nova Pro
The most cost-effective pattern on Bedrock is cascaded routing: use Nova Micro to classify complexity, then route hard requests to Nova Pro.
def intelligent_route(user_query: str) -> str:
# Step 1: Nova Micro classifies complexity (cost: ~$0.000035 per call)
complexity_check = bedrock.invoke_model(
modelId="amazon.nova-micro-v1:0",
body=json.dumps({
"messages": [{"role": "user", "content": [{"text": f"""Is this query simple or complex?
Simple: factual questions, short extraction, yes/no answers
Complex: multi-step reasoning, analysis, synthesis, long generation
Query: {user_query}
Answer with one word: SIMPLE or COMPLEX"""}]}],
"inferenceConfig": {"maxTokens": 5, "temperature": 0.0},
}),
)
complexity = json.loads(complexity_check["body"].read())["output"]["message"]["content"][0]["text"].strip()
# Step 2: Route accordingly
if complexity == "SIMPLE":
model_id = "amazon.nova-micro-v1:0" # $0.035/1M
else:
model_id = "amazon.nova-pro-v1:0" # $0.800/1M
# Step 3: Generate actual response
response = bedrock.invoke_model(
modelId=model_id,
body=json.dumps({
"messages": [{"role": "user", "content": [{"text": user_query}]}],
"inferenceConfig": {"maxTokens": 1024},
}),
)
return json.loads(response["body"].read())["output"]["message"]["content"][0]["text"]
In practice, 60 - 80% of support queries are "simple" by this definition. A pipeline that routes correctly saves 20x on model costs for those queries.
Batch Inference on Bedrock
For non-latency-sensitive workloads (nightly report processing, weekly data enrichment), Bedrock Batch Inference applies additional discounts (up to 50% off) and removes the need to handle rate limits:
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// written byFIG. AUTH-01
530
Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
