Comparison
Canary trades rollout speed for safety and data-driven confidence. Blue-green offers instant rollback but full exposure. Rolling updates are simple but blend metrics. Shadow mode provides risk-free validation.
BLUE-GREEN
Swap all traffic in seconds between two full environments (blue=current, green=new). If green is broken, 100% of users see failures until flip back. Requires 2× capacity during cutover. Fast rollback but all-or-nothing blast radius.
ROLLING UPDATES
Gradually replace instances one or few at a time until fleet is updated. Rollback requires another rolling cycle in reverse (tens of minutes). No extra capacity cost. Simple orchestration. Downside: metrics blend old and new versions throughout rollout, making detection slow.
SHADOW MODE
Mirror traffic to canary for measurement, primary responses still go to users—zero user impact. Powerful for ML: compare prediction distributions and latency under live load before real exposure. Cannot catch issues from real user state changes or high write rates. Adds compute overhead (doubles read traffic).
💡 Decision Guide: Blue-green for instant rollback or schema changes. Rolling for simplicity with strong pre-prod testing. Shadow for initial ML validation. Canary when offline metrics do not predict production behavior well.
CANARY TRADE-OFFS
Canary exposes 5-10% initially (limited blast radius), takes 15-30 min to ramp to 50%, needs only 1.1-1.2× capacity. Rollback is immediate. Requires routing complexity, observability pipelines, and enough traffic volume for statistical validity.
✓Blue green flips 100 percent of traffic instantly with 2 times capacity cost, canary ramps over 15 to 30 minutes with 1.1 to 1.2 times capacity but limits blast radius to 5 to 10 percent initially
✓Rolling updates have zero capacity overhead and simple orchestration but slow rollback (requires reverse rolling cycle), canary rollback is immediate
✓Shadow traffic provides zero user impact validation for ML models (compare predictions and latency under load) but cannot catch write side effects or state dependent issues, adds compute overhead
✓Canary is ideal for ML when offline metrics do not predict production behavior and you need to measure real user CTR, conversion, or prediction quality under actual traffic
✓Choose blue green for instant cutover needs or incompatible schema changes, rolling for simplicity with strong testing, shadow then canary for ML model validation