{
  "assumption_density": 0.4,
  "assumptions": [
    "DynamoDB cost (~$28K/month) is the primary driver for migration, not a misidentified implementation issue",
    "The existing SaaS can tolerate a cross-cloud database dependency on Azure if other services remain on AWS",
    "90%+ of queries are single-tenant scoped (tenant_id filtered), making shard-local routing the dominant access pattern",
    "The engineering team has sufficient PostgreSQL operational expertise to manage the migration and ongoing operations even with managed Citus",
    "The 2-week dual-write cutover window is achievable given schema complexity and data volume across 2,000 tenants"
  ],
  "confidence": 0.72,
  "id": "cbfe26f4-4e75-417d-951d-0d3ef481fdd9",
  "next_action": "Deploy a 2-node Azure Cosmos DB for PostgreSQL (Hyperscale Citus) proof-of-concept cluster with 1 coordinator + 1 worker node, load 3 representative tenants (including the largest by data volume), distribute on tenant_id, replay 24 hours of production query logs via pgbench, and measure p99 latency against the 50ms target before committing to full migration.",
  "question": "Should we migrate from DynamoDB to PostgreSQL with Citus for a multi-tenant SaaS with 2,000 tenants and 50ms p99 latency?",
  "question_fit_score": 0,
  "rejected_alternatives": [
    {
      "path": "Conduct comprehensive performance audit before any migration decision (b002/b007)",
      "rationale": "Both b002 and b007 propose analysis-first approaches. While sound in principle, they lack architectural specificity — b002 sets a 45ms latency target and 30% cost reduction threshold but names no specific technology, node configuration, or migration tooling. b007 is even more abstract, proposing an audit without any concrete migration architecture. The question implies DynamoDB cost/complexity is already an identified problem (motivating the migration question). b003 subsumes the valid concern by specifying a 2-week dual-write validation period while providing a fully actionable architecture."
    },
    {
      "path": "Hybrid DynamoDB + PostgreSQL/Citus architecture (b001, killed)",
      "rationale": "Doubles operational surface area without eliminating DynamoDB's read unit costs (typically 70%+ of the bill). Application-level joins between DynamoDB and Postgres entities at 2,000 tenants blow past 50ms p99. No concrete cost numbers or workload split provided."
    },
    {
      "path": "Polyglot persistence strategy (b004, killed)",
      "rationale": "Named zero specific technologies, databases, or thresholds. Proposed redefining SLAs when the 50ms p99 SLA is already specified. Structurally hollow."
    }
  ],
  "reversal_conditions": [
    {
      "condition": "DynamoDB costs are primarily driven by implementation issues (poor partition key design, over-provisioned capacity) and a 30%+ cost reduction is achievable through optimization alone",
      "flips_to": "Optimize existing DynamoDB setup: redesign partition keys, implement auto-scaling, add DAX caching layer, defer migration"
    },
    {
      "condition": "Proof-of-concept shows coordinator bottleneck at 2,000 tenants causes p99 \u003e 50ms under production-equivalent concurrent load",
      "flips_to": "Evaluate self-managed Citus on AWS with multiple coordinators, or consider CockroachDB/TiDB as distributed SQL alternatives without single-coordinator constraint"
    },
    {
      "condition": "Existing infrastructure is entirely AWS-native and cross-cloud latency to Azure adds \u003e10ms to p99, eating the safety margin",
      "flips_to": "Deploy self-managed Citus on AWS EC2/EKS with increased budget allocation for DBA operational overhead"
    }
  ],
  "unresolved_uncertainty": [
    "Coordinator bottleneck at 2,000 tenants: killed branch b005 cited case studies from Framer and Heap showing coordinator hotspotting spiking p99 to 150ms+. This was auto-pruned as unsupported but the concern is architecturally valid and untested in this specific workload profile.",
    "Cross-cloud migration complexity: if existing services are on AWS, moving the database to Azure introduces cross-cloud latency and data transfer costs not accounted for in the $4,200/month estimate.",
    "The $4,200/month Azure cost and $28K/month DynamoDB cost are model-generated projections without cited production benchmarks for this specific workload volume.",
    "No evidence that the current DynamoDB bottleneck has been formally diagnosed — b002/b007's concern that the problem may be implementation rather than technology remains valid.",
    "Actual query patterns and data volume per tenant not specified — latency projections assume typical multi-tenant SaaS workloads."
  ],
  "url": "https://vectorcourt.com/v/cbfe26f4-4e75-417d-951d-0d3ef481fdd9",
  "verdict": "Migrate to PostgreSQL with Citus on Azure Managed Hyperscale (Cosmos DB for PostgreSQL): 1 coordinator (8 vCores, 32GB RAM) + 4 worker nodes (4 vCores, 32GB RAM each). Use tenant_id as distribution column with co-location. Estimated cost ~$4,200/month vs $28K+/month DynamoDB. Single-tenant queries (90%+ of workload) route to a single shard at 5-15ms p99; cross-tenant JOINs hit 20-45ms p99, meeting the 50ms target with ~10% headroom. Use pgloader for bulk migration, AWS DMS for CDC during cutover, with a 2-week dual-write period (DynamoDB as read fallback via application-level routing). Critical failure mode: hot tenant skew. If the top 3 tenants represent \u003e40% of data/queries, isolate them onto dedicated worker nodes using Citus tenant isolation (shard_count=1 per large tenant). If skew exceeds 60% on any single worker, p99 will breach 50ms under concurrent load. Self-managed Citus on AWS is rejected as a hidden budget killer — dual-running DynamoDB ($28K/month) + self-managed Citus ($8K/month) + engineering blows the budget by month 4.",
  "verdict_core": {
    "recommendation": "Migrate to PostgreSQL with Citus on Azure Managed Hyperscale (Cosmos DB for PostgreSQL) with 1 coordinator (8 vCores, 32GB RAM) + 4 worker nodes (4 vCores, 32GB RAM each), using tenant_id as distribution column with co-location.",
    "mechanism": "Because managed Hyperscale Citus eliminates DBA overhead for vacuum tuning, shard rebalancing, and HA failover — the three operational tasks that cause self-managed Citus clusters to degrade p99 latency past 50ms during maintenance windows — while tenant_id distribution routes 90%+ of single-tenant queries to a single shard at 5-15ms p99, leaving ~10% headroom against the 50ms target.",
    "tradeoffs": [
      "Cloud vendor lock-in to Azure Managed Hyperscale instead of staying AWS-native with DynamoDB",
      "Cross-cloud data transfer costs and latency if other services remain on AWS during or after migration",
      "Loss of DynamoDB's fully serverless operational model — even managed Citus requires capacity planning for coordinator and worker node sizing"
    ],
    "failure_modes": [
      "Hot tenant skew: if top 3 tenants represent \u003e40% of total data/queries, their co-located shards saturate individual worker nodes, breaching 50ms p99 under concurrent load",
      "Coordinator bottleneck: single coordinator node becomes a chokepoint for cross-shard queries or high-concurrency metadata operations at 2,000 tenants",
      "Budget blowout during dual-running period if migration extends beyond the planned 2-week cutover window"
    ],
    "thresholds": [
      "~$4,200/month on Azure Managed Hyperscale vs $8K+ self-managed on AWS",
      "p99 5-15ms for single-tenant queries, 20-45ms for cross-tenant analytical JOINs",
      "\u003e40% data/query skew on top 3 tenants triggers shard isolation",
      "\u003e60% skew on any single worker node breaches 50ms p99",
      "2-week dual-write cutover period, not 60-day full dual-running"
    ]
  },
  "verdict_type": ""
}