Deployment strategy	Main idea	Complexity level	Downtime	Infrastructure resources	Deployment risk	Ease of rollback
AB Testing Deployment	Comparing two (or more) application versions on real user groups to determine the superior one.	Medium - requires advanced traffic management and data analysis.	None - tested versions run in parallel.	Medium - sometimes a cloned environment is needed, but usually on a smaller scale.	Low to medium - changes are exposed to a limited user group, so issues can be caught early.	Simple - just revert traffic to Version A (the control).
Blue-Green Deployment	Two parallel environments (Blue and Green); one handles production traffic while the other is prepared for immediate switchover.	Medium - requires maintaining and synchronizing two full production environments.	None - switchover occurs instantly after successful tests.	High - having two complete production environments increases infrastructure costs.	Low - no downtime and an option to instantly revert traffic if needed.	Very easy - simply switch traffic back to the previous environment.
Canary Release	Gradually introducing the new version on a small portion of the environment/traffic, with an option to scale up as results prove stable.	High - requires dynamic routing and continuous monitoring.	None - a small fraction of users see the new version, the rest remain on the old one.	Medium - storing and handling part of the traffic on the new version; sometimes needs extra instances.	Low to medium - issues only affect a small percentage of users; relies heavily on observability.	Easy - reduce traffic directed to the new version and revert to the previous one if issues arise.
Recreate Deployment	Completely shutting down the old version before launching the new version (stop-then-start).	Low - very straightforward from a process standpoint.	High - the application is unavailable during the switchover.	Low - only one environment to maintain, no cloning.	High - a failure means the entire application is offline.	Difficult - requires a second full deployment of the old version to revert (another maintenance window).
Rolling Deployment	Updating instances in batches with the new version until the old version is fully replaced.	Medium - requires coordination and monitoring of instance batches.	None - the service remains available (some instances run the old version, some the new).	Medium - both old and new instances coexist temporarily.	Medium - issues emerge early but may affect part of the traffic.	Medium - one can halt further rollout and revert, but it requires coordination.
Shadow Deployment	A parallel “shadow” environment receives a mirrored copy of user traffic but does not respond to end users.	High - duplicate live traffic and isolate data, so nothing conflicts with production.	None - end users do not see the “shadow” version, which runs in the background.	High - additional resources are needed for the shadow environment and traffic mirroring.	Low - no direct impact on real users; tests occur in near-production conditions.	N/A - it does not interact with end users, so there is no need to fail over or revert in this mode.
AI Deployment	Integrating and serving machine learning (ML) / AI models within the software release lifecycle (MLOps).	High - requires specialized infrastructure, data pipelines, and continuous training/monitoring.	None - typically the new model can be deployed in parallel (e.g. as a shadow or canary).	High - may need GPU/accelerators and data versioning for retraining.	Medium to high - issues can manifest in model inaccuracies or drift, requiring immediate rollback or retraining.	Moderate - revert to the previous model version or switch traffic back to the older model if performance regresses.
Federated Deployment	Coordinating deployment across multiple clusters or regions under a shared governance model.	High - multi-cluster orchestration, policy synchronization, and potential edge devices.	None - each cluster can continue serving independently while updates propagate.	High - multiple autonomous clusters require tooling for placement rules and policy overrides.	Medium to high - partial cluster failures or geo-distributed outages must be handled carefully.	Complex - requires reverting or rolling back changes in each cluster, and keeping all of them consistent.