Managed Environment
- Persistent cloud storage for AI memory—documents, knowledge graphs, and embeddings.
- Preconfigured Modal environment instead of local installation, configuration, and maintenance.
- Backed by managed PostgreSQL, LanceDB, and Kuzu stores.
- Access is provided through a Cognee Cloud subscription and API keys used by the UI and SDK.
Pipeline execution
- Trigger add, cognify, and search from the Cloud UI. The Python SDK and notebooks also support memify.
- Execution and scheduling run in an isolated workspace context within the cloud runtime.
- Multi-tenancy and audit logging keep each workspace’s data and activity separate.
Python SDK
- Dedicated
cogwit-sdklibrary with API-key authentication. - Mirrors the open-source Cognee API signature and behavior.
- Supports uploads, pipeline execution, and graph-backed search.
Interactive UI
- Cognee Cloud UI to add, cognify and search your documents and datasets.
- Improve data structure using self-defined ontology.
- Surface pipeline runs, statuses, and outputs in one place.
- Enable interactive search, browsing, and dataset management.
Relationship to Cognee OSS
- Cognee Cloud uses the same concepts, operations, and API patterns as open-source Cognee, but differs in deployment and use.
- Cognee Cloud provides hosted persistence and collaboration.
- Open-source Cognee is for local development, custom infrastructure, or air-gapped needs.
- Local setups can sync with Cognee Cloud for combined workflows.
Explore Cognee Cloud
Create account & API key
Complete the sign-up checklist, billing, and key creation steps.
Use the Cloud UI
Manage datasets, upload files, and trigger cognify from the console.
Use the Python SDK
Install
cogwit-sdk and run add, cognify, and search from Python.Review the architecture
See how Modal compute, storage services, and datasets fit together.
Check permissions & security
Understand dataset isolation today and the planned RBAC rollout.
Connect local mode & sync
Link a local instance and review current sync behavior and limits.