Canonical MLOps
5.4#23 in mlops platformsWebLinuxSelf-hosted
VisitCanonical MLOps vs Flyte
What both publish
| Fact | Canonical MLOps | Flyte |
|---|---|---|
| Deployment | Charmed MLflow can run on a workstation, CNCF-conformant Kubernetes, or a public cloud. | Flyte 2 can run locally with a devbox or on a user's cluster; the site provides `pip install flyte` and `flyte start devbox` setup commands. |
| Integrations | Canonical lists Jupyter, Grafana, and Prometheus as tools to use with Charmed MLflow. | The Flyte site lists integrations including Apache Spark, Ray, PyTorch Elastic, BigQuery, Snowflake, Weights & Biases, OpenAI Agents SDK, LangGraph, CrewAI, Pydantic AI, MLflow, Dask, and Databricks. |
| Purpose | Canonical MLOps is an open source platform for moving machine learning models from experimentation to production across the ML lifecycle. | Flyte 2 is a durable AI runtime for workloads involving training, serving, and agents. |
Canonical MLOps plans
- Canonical MLOps support subscription—
Flyte plans
- Flyte OSSFree
- Flyte OSSFree
- Team$950 a month
- Union.ai Team (enterprise Flyte platform)$950 a month
- EnterprisePriced on request
Pick Canonical MLOps if…
It suits teams running machine-learning workflows across Linux, self-hosted environments, or the web. It is also relevant for organisations needing experiment tracking, model registries, orchestration, serving, and cloud Kubernetes support.
Pick Flyte if…
Flyte suits teams building AI workflows for training, serving, or agents, particularly those needing dynamic execution and recovery from infrastructure failures. It offers local devbox and cluster deployment options.