Compare Scenarios

Migration Step 1

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'. ML Feature Serving Platform: triggered by 'Feature computation logic duplicated across 3+ model serving'.

Migration Step 2

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'. ML Feature Serving Platform: triggered by 'Regulatory or reproducibility requirement to re-run model pr'.

Migration Step 3

Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'. ML Feature Serving Platform: triggered by 'Semantic similarity search recall insufficient from BM25 ful'.

PgBouncer pool wait queue > 0 during peak hours; application errors reporting "connection pool exhausted" or pool timeout; pool utilization > 90%

Tier 1: Connection Pool Exhaustion: Aggregate tenant connection demand exceeding PgBouncer pool_size. Recommended evolution: Implement per-tenant connection quotas at the application layer before hitting the pool; increase pool_size incrementally; identify top-N connection consumers by tenant and implement connection reuse within tenant request handlers .

pg_stat_activity shows one tenant's queries dominating query runtime; other tenants reporting p99 latency regression while their own query counts are stable; PostgreSQL shared_buffers cache eviction rate increasing

Tier 2: Noisy Tenant I/O Saturation: Single large tenant displacing other tenants' working sets from shared buffer cache. Recommended evolution: Implement pg_cgroups or connection-level resource groups if available; consider database-per-tenant for the top N largest tenants while keeping the shared schema for smaller tenants (hybrid isolation model) .

Feature store p99 rising from < 5ms to > 50ms immediately following a model deployment or pod scaling event; Cassandra or PostgreSQL feature store read QPS spiking 10–100x simultaneously with serving fleet restart; feature store connection pool exhaustion visible in application logs during the cold start window; Redis cache hit rate dropping to < 10% for the first 60 seconds after deployment

Tier 1: Cache Cold Start Latency Spike: Simultaneous cache cold start across all serving pods after deployment, converting sequential feature requests into a thundering herd against the backing feature store. Recommended evolution: Implement pre-warming: before a new model version receives traffic, a warm-up job runs inference requests for a representative sample of entity IDs, populating the Redis cache before the pod enters the serving fleet. Use probabilistic early cache refresh (fetch from backing store before TTL expiry when remaining TTL < 20% under high request rate) to prevent simultaneous TTL expiry on hot features. Stagger deployment rollout: deploy 10% of pods, wait for cache warm, then proceed to the next 10%. .

Model performance metrics (precision, recall, AUC) degrading without a corresponding input distribution shift; ClickHouse drift dashboard showing feature value distributions served online diverging from training population distributions; explicit skew audit (comparing offline training feature values against replayed online feature values for the same entity at the same timestamp) showing systematic differences in specific features

Tier 2: Training-Serving Skew Detection: Feature computation logic divergence between the offline training pipeline and the online serving pipeline: a feature transformation applied in training is not applied identically in serving. Recommended evolution: Enforce a single feature computation function registered per feature in a shared feature registry, executed by both the online pipeline and the offline training pipeline. The two paths must use the same code, not independently maintained implementations. Implement a skew monitoring job that computes a random sample of features using both paths and alerts on distribution divergence above a threshold. .