How to Reduce Datadog Auto-Instrumentation Granularity in AWS Lambda?

Question

I have an AWS Lambda function packaged as a ZIP and using the Datadog v65 extension as a layer. I haven't imported ddtrace in any part of my code.

I have set the following environment variable:

DD_TRACE_ENABLED=true
DD_PROFILING_ENABLED=true

When the Lambda starts, the logs show that Datadog automatically instruments 60 integrations

Configured ddtrace instrumentation for 60 integration(s). The following modules have been patched: aioredis,aiomysql,aredis,asyncio,avro,boto,botocore,bottle,cassandra,celery,consul,django,dramatiq,elasticsearch,algoliasearch,futures,google_generativeai,gevent,graphql,grpc,httpx,kafka,mongoengine,mysql,mysqldb,pymysql,mariadb,psycopg,pylibmc,pymemcache,pymongo,redis,rediscluster,requests,rq,sanic,sqlite3,aiohttp,aiohttp_jinja2,aiopg,vertica,molten,jinja2,mako,flask,starlette,falcon,pyramid,pynamodb,pyodbc,fastapi,dogpile_cache,yaaredis,asyncpg,aws_lambda,openai,langchain,anthropic,subprocess,unittest

This results in excessive low-level traces, which I don’t need. It feels like code profiling rather than just tracking high-level requests.

I don't want to profile the code, I want traces for pymongo / requests module at a high level.

Is there an easy way to remove this level of granularity?

I tried

DD_PROFILING_ENABLED=false

But it doesn't seem to do the trick. It creates a separate resource in my traces called aws.lambda and it includes the low level data on it.