Custodian Labs vs Nano

from custodian_labs import Custodian model = Custodian( model="gpt-4o", system_prompt="You are a helpful assistant.

An AI-native security and observability platform for cloud, local, hybrid, and air-gapped teams. Sub-second search, detection engineering, OpenTelemetry, pivt AI, and...

About Custodian Labs

from custodian_labs import Custodian model = Custodian( model="gpt-4o", system_prompt="You are a helpful assistant. The proprietary Guardian Layer is the guardrail that detects PII and puts you in control of how it's handled before it reaches any model. Give your agent long-term memory and document retrieval without configuring embeddings or vector DBs. Your agent logic stays exactly as it is; only the model underneath changes. No database to provision, no vector store to connect, no hosting to manage.

About Nano

An AI-native security and observability platform for cloud, local, hybrid, and air-gapped teams. Sub-second search, detection engineering, OpenTelemetry, pivt AI, and deployment control in one place. Run nano in the cloud, locally, hybrid, or air-gapped. nano was built from scratch, not refactored: a dedicated cluster, one query language over your security and OpenTelemetry data, agentic investigation, and deployment options from cloud to air-gapped. So when a service slows down, "is this an outage or a breach?" is one query, not two tools and a guess.