Compare Scenarios
Side-by-side comparison with decision path analysis. Every dimension traces back to topology, risk propagation, simulation, and advisor intelligence.
Select Scenarios to Compare
Left Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Right Scenario
AI / RAG
Multi-Tenant SaaS
Analytics
Financial Ledger
Read-Heavy
Multi-Tenant SaaS
Write-Heavy
Marketplace
Event-Driven
Financial Ledger
Realtime Collab
Realtime Collab
Financial Ledger
Write-Heavy
AI / RAG
Multi-Tenant SaaS
Event-Driven
Analytics
Read-Heavy
Realtime Collab
Search-Heavy
Event-Driven
Event-Driven
Marketplace
Write-Heavy
Topology at a Glance
Architecture Comparison
Multi-Tenant SaaS Platform is both simpler and lower-risk than Financial Ledger Platform
Multi-Tenant SaaS Platform is the simpler architecture. Multi-Tenant SaaS Platform carries lower operational risk. They share 1 component(s). Financial Ledger Platform has 4 unique risk(s); Multi-Tenant SaaS Platform has 4. Multi-Tenant SaaS Platform requires lower team maturity to operate.
12
Nodes
9
Edges
4
Risks
2
Seeds
6
Strengths
4
Adv. Risks
11
Nodes
7
Edges
4
Risks
3
Seeds
4
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Financial Ledger Platform
expert complexity, 12 nodes, 9 edges, 4 risks, 2 simulation seeds
Multi-Tenant SaaS Platform
moderate complexity, 11 nodes, 7 edges, 4 risks, 3 simulation seeds
Multi-Tenant SaaS Platform is simpler: moderate operational complexity with 11 topology nodes vs 12 for Financial Ledger Platform.
Operational Risk
Financial Ledger Platform
4 risks (top: high), 4 high/critical, 0 confirmed by simulation
Multi-Tenant SaaS Platform
4 risks (top: high), 3 high/critical, 2 confirmed by simulation
Multi-Tenant SaaS Platform has lower operational risk: weighted severity score 14 vs 16 (2 vs 0 simulation-confirmed).
Scalability
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Multi-Tenant SaaS Platform
4 scaling thresholds, 3 migration paths, 9 advisor scaling signals
Multi-Tenant SaaS Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Financial Ledger Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements
Multi-Tenant SaaS Platform
Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements
Multi-Tenant SaaS Platform requires lower team maturity (Intermediate) vs Advanced for Financial Ledger Platform.
Observability
Financial Ledger Platform
4 watched metrics, 5 observability recommendations, 2 simulation seeds
Multi-Tenant SaaS Platform
9 watched metrics, 6 observability recommendations, 3 simulation seeds
Financial Ledger Platform has lower observability burden: 4 watched metrics vs 9.
Generator Readiness
Financial Ledger Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Multi-Tenant SaaS Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Both scenarios have comparable generator readiness at this stage. Generator support is preliminary. Neither scenario should be treated as fully generation-ready.
Architecture Components
Shared (1)
Only in Financial Ledger Platform (11)
Only in Multi-Tenant SaaS Platform (10)
Operational Risks
Only in Financial Ledger Platform (4)
Only in Multi-Tenant SaaS Platform (4)
Consistency Guarantees
Only Financial Ledger Platform (1)
Moving from Financial Ledger Platform to Multi-Tenant SaaS Platform
Green = kept, red = lost (the target explicitly excludes it), amber = unproven (the target has made no claim, not the same as losing it).
Only 'Financial Ledger Platform' claims: atomic_multi_object.
Tradeoff Summary
Complexity vs Risk
Financial Ledger Platform has expert complexity. Multi-Tenant SaaS Platform has moderate complexity. Simpler systems often carry different (not necessarily fewer) risks.
Financial Ledger Platform
Financial Ledger Platform: 4 risks (top: high), 4 high/critical, 0 confirmed by simulation
Multi-Tenant SaaS Platform
Multi-Tenant SaaS Platform: 4 risks (top: high), 3 high/critical, 2 confirmed by simulation
Scaling Path
Financial Ledger Platform offers 4 defined scaling thresholds. Multi-Tenant SaaS Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Financial Ledger Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Multi-Tenant SaaS Platform
4 scaling thresholds, 3 migration paths, 9 advisor scaling signals
Team Maturity Requirement
Multi-Tenant SaaS Platform can be operated by a less experienced team. Financial Ledger Platform requires deeper operational expertise.
Financial Ledger Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 7 operational requirements
Multi-Tenant SaaS Platform
Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements
Event-Driven vs Synchronous Processing
Financial Ledger Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Multi-Tenant SaaS Platform does not, keeping the stack simpler but less decoupled.
Financial Ledger Platform
Event stream: async decoupling, consumer lag risk, higher ops burden
Multi-Tenant SaaS Platform
No event stream: simpler stack, synchronous dependencies
Architecture Strengths vs Risks Balance
The advisor identifies strengths and risks grounded in knowledge relationships. A higher strengths-to-risks ratio suggests better mitigation coverage in the current topology.
Financial Ledger Platform
6 strengths, 4 risks
Multi-Tenant SaaS Platform
4 strengths, 4 risks
Migration Considerations
Migration Step 1
Financial Ledger Platform
Mutable account balance table with no event history → Event sourced ledger with append-only events and projected balance view
Multi-Tenant SaaS Platform
Single-tenant PostgreSQL with per-user row filtering in application code → Multi-tenant PostgreSQL with row-level security policies
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'.
Migration Step 2
Financial Ledger Platform
Synchronous Kafka publish in transaction (dual-write pattern) → Outbox pattern with CDC relay to Kafka
Multi-Tenant SaaS Platform
Shared schema multi-tenant with no caching → Shared schema + Redis with tenant-namespaced cache keys
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'.
Migration Step 3
Financial Ledger Platform
Single PostgreSQL primary serving all reads and writes → CQRS with separate read model and write model
Multi-Tenant SaaS Platform
Shared schema for all tenants → Hybrid: dedicated database per enterprise tenant + shared schema for standard tenants
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'.
Advisor Notes
Strength: The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…
The outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record and the outbox event in a single ACID transaction, ensuring events are published if and only if the database write committed. Key trade-off: Adds ~1ms write overhead per transaction for the outbox INSERT. Operational note: Outbox table requires a relay process: this is an additional operational component to monitor. Evidence: Atomic write to both business table and outbox table in one transaction: no window for inconsistency.
Strength: Redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude
Redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude. Key trade-off: Introduces eventual consistency: stale reads possible within TTL window. Operational note: Cache-aside (lazy population) is the dominant integration pattern. Evidence: Cache hit rates of 80–95% observed in production read-heavy APIs.
Risk (high): Lock Contention
Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
Risk (high): Hot Partition
One partition (a database shard, a Kafka topic partition, a Redis hash slot) receives traffic so far above its peers that it saturates while the others sit idle. Aggregate capacity looks healthy, but the hot partition throttles or lags, and everything routed to it degrades. The cause is skew in how keys map to partitions, and the fix depends on whether the skew is spread across many keys or concentrated in one.
Shared Operational Requirements
Both scenarios require: PostgreSQL: scenario includes high_write_throughput or write_heavy workload, Runbooks and alerting for high-severity risks.
Supporting Evidence · 16 items
Limitations
- ·Comparison grounded in YAML knowledge only. Not measured from any production system.
- ·Winner assessments are deterministic heuristics, not absolute recommendations. Context, team preferences, and workload specifics may change the conclusion.
- ·4 seed type(s) across both scenarios do not have execution previews. Risk confirmation for those types is unavailable.
Multi-Tenant SaaS Platform is the recommended starting point over Financial Ledger Platform
Multi-Tenant SaaS Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Scalability. Weighted score: 6.5 vs 1.0 for Financial Ledger Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Financial Ledger Platform and Multi-Tenant SaaS Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Multi-Tenant SaaS Platform is the recommended starting point over Financial Ledger Platform
Multi-Tenant SaaS Platform leads on 4 weighted dimension(s): Complexity, Operational Risk, Scalability. Weighted score: 6.5 vs 1.0 for Financial Ledger Platform. The architectures share 1 component(s), reducing migration cost if you switch later. Multi-Tenant SaaS Platform is the operationally simpler choice.
Where to Start
Start with Multi-Tenant SaaS Platform
RightMulti-Tenant SaaS Platform has lower operational complexity. Starting here reduces risk and cognitive load. Migrate to the more capable architecture only when you hit concrete scaling or feature limits.
Complexity: moderate complexity, 11 nodes, 7 edges, 4 risks, 3 simulation seeds
Migrate when:
- PgBouncer pool wait queue > 0 during peak hours; application errors reporting "connection pool exhausted" or pool timeout; pool utilization > 90% → 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 → 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)
- DDL migration duration > 30s on any shared table; lock acquisition timeouts reported during migration windows; migration deployment requiring off-hours scheduling → Adopt zero-downtime migration patterns exclusively: pg_repack for table rewrites, column additions without constraints first, then constraint additions via NOT VALID; never use ALTER TABLE ... ADD COLUMN with DEFAULT in PostgreSQL < 11
Decision Flow
Does your team have the operational maturity to run Financial Ledger Platform (advanced rating)?
If Yes
Your team can operate Financial Ledger Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: right scenario.
Is operational stability and minimising production risk your primary concern over feature richness or scalability ceiling?
If Yes
Prefer Multi-Tenant SaaS Platform: it carries lower operational risk weight per the advisor's assessment.
If No
Proceed to Step 3 to evaluate based on scaling requirements.
Do you expect your load to reach: PgBouncer pool wait queue > 0 during peak hours; application errors reporting "connection pool exhausted" or pool timeout; pool utilization > 90% ?
If Yes
Right scenario has more defined scaling evolution paths for this growth pattern.
If No
If you don't expect to hit these scaling signals soon, prefer the simpler architecture and re-evaluate when load patterns become clearer.
Does your team already operate an event streaming platform (e.g., Kafka, Kinesis, Pub/Sub) in production?
If Yes
Financial Ledger Platform builds on event stream infrastructure. Your existing platform reduces the adoption risk.
If No
If you don't have an event streaming platform, the other scenario avoids adding that infrastructure dependency. Evaluate whether the async decoupling benefit justifies the new ops burden.
Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?
If Yes
Multi-Tenant SaaS Platform is the simpler choice: Multi-Tenant SaaS Platform is simpler: moderate operational complexity with 11 topology nodes vs 12 for Financial Ledger Platform.
If No
If capability and scalability ceiling matter more than simplicity, evaluate the higher-complexity scenario against your specific load model.
When to Choose Each Scenario
Financial Ledger Platform
LeftWhen you want to minimise monitoring setup overhead
ModerateFinancial Ledger Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your architecture benefits from: the outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record…
ModerateThe outbox pattern eliminates split-brain between a database write and a message broker publish by writing both the domain record and the outbox event in a single ACID transaction, ensuring events are published if and only if the database write committed. Key trade-off: Adds ~1ms write overhead per transaction for the outbox INSERT. Operational note: Outbox table requires a relay process: this is an additional operational component to monitor. Evidence: Atomic write to both business table and outbox table in one transaction: no window for inconsistency.
When your architecture benefits from: financial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables…
ModerateFinancial transaction workloads benefit from event sourcing because the event log provides an immutable audit trail, enables temporal queries (balance at any past date), and makes the derivation of current state fully traceable: meeting regulatory requirements that state-mutation databases cannot satisfy. Key trade-off: Event log growth is unbounded for long-lived accounts: snapshot and archival strategy required. Operational note: Financial event logs must be retained for 7-10 years (regulatory requirement): plan storage accordingly. Evidence: PCI-DSS and SOX require immutable audit trails: event sourcing provides this structurally.
When your system requires decoupled async event processing
HighFinancial Ledger Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Multi-Tenant SaaS Platform
RightWhen operational simplicity is a top priority
HighMulti-Tenant SaaS Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalMulti-Tenant SaaS Platform carries lower overall risk weight per the advisor's assessment.
When you need well-defined scaling thresholds and migration paths
HighMulti-Tenant SaaS Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your team has limited operational maturity
CriticalMulti-Tenant SaaS Platform is rated intermediate , accessible for teams without deep platform expertise.
When your architecture benefits from: redis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude
ModerateRedis caching absorbs repeated read requests at the edge, reducing database load and latency for high read-to-write ratio workloads by orders of magnitude. Key trade-off: Introduces eventual consistency: stale reads possible within TTL window. Operational note: Cache-aside (lazy population) is the dominant integration pattern. Evidence: Cache hit rates of 80–95% observed in production read-heavy APIs.
When your architecture benefits from: a connection pool bounds the total database connections an application can open, preventing connection storms during traffic…
ModerateA connection pool bounds the total database connections an application can open, preventing connection storms during traffic spikes and protecting the database server from exceeding its connection limit. Key trade-off: Pooler becomes a new single point of failure if not replicated. Operational note: PgBouncer transaction-mode pooling is most effective for stateless APIs. Evidence: PgBouncer reduces PostgreSQL connections by 10–100x in typical deployments.
When to Avoid Each Scenario
Financial Ledger Platform
LeftWhen your team cannot mitigate: lock contention
HighThis architecture is significantly exposed to Lock Contention. Concurrent writers to the same rows serialize behind each other's row locks, so latency is set not by the work a transaction does but by how long it waits for the writers ahead of it. On a hot row the queue depth, and therefore the tail latency, grows with concurrency while throughput flattens. Blocked writers hold connections open, so a single contended row can drain the connection pool as a secondary failure.
When your team cannot mitigate: split-brain
HighThis architecture is significantly exposed to Split-Brain. A failover mechanism promotes a new leader without confirming the old one has stopped, so two nodes simultaneously believe they hold the primary role and both accept writes. The two histories diverge, and when the partition that triggered the failover heals, one set of committed transactions must be discarded.
When your team is early-stage or solo
HighFinancial Ledger Platform is rated 'advanced'. It requires experienced backend engineers or platform tooling to operate reliably at scale.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 7 predicted bottlenecks for Financial Ledger Platform. Rapid growth will surface these limitations quickly.
Multi-Tenant SaaS Platform
RightWhen your team cannot mitigate: hot partition
HighThis architecture is significantly exposed to Hot Partition. One partition (a database shard, a Kafka topic partition, a Redis hash slot) receives traffic so far above its peers that it saturates while the others sit idle. Aggregate capacity looks healthy, but the hot partition throttles or lags, and everything routed to it degrades. The cause is skew in how keys map to partitions, and the fix depends on whether the skew is spread across many keys or concentrated in one.
When your team cannot mitigate: connection pool exhaustion
HighThis architecture is significantly exposed to Connection Pool Exhaustion. All database connections in the pool are in use; new requests queue and then time out, causing cascading latency and errors across all dependent services.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 6 predicted bottlenecks for Multi-Tenant SaaS Platform. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
RightMulti-Tenant SaaS Platform is more accessible for small teams. Fewer operational moving parts reduces on-call burden.
- ↳Validate that the simpler architecture can handle your projected load before committing.
Small product team (2–6 engineers)
RightMulti-Tenant SaaS Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Financial Ledger Platform only if your workload pattern specifically requires it.
Experienced backend team
DependsAn experienced team can operate either architecture. Choose based on workload fit, not team capability.
- ↳Prioritise alignment with existing infrastructure and tooling.
- ↳Financial Ledger Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
LeftA platform team can safely operate Financial Ledger Platform and will benefit from its more advanced scaling characteristics.
- ↳Ensure observability and alerting are configured before launch.
Migration Triggers
Migration Step 1
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Audit requirement to reconstruct account state at any histor'. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'.
Migration Step 2
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Kafka publish failures causing financial transaction rollbac'. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'.
Migration Step 3
Both scenarios define a migration step at this stage. Financial Ledger Platform: triggered by 'Financial dashboard query p99 > 500ms causing dashboard-driv'. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'.
pg_locks shows contended rows on accounts table; write p99 > 50ms; deadlock errors in application logs; pg_stat_activity showing many transactions waiting for RowExclusiveLock on the same account rows
Tier 1: Hot Account Lock Contention: Concurrent debit/credit transactions competing for the same account row versions. Recommended evolution: Implement optimistic locking with version column and retry; or queue concurrent updates for the same account entity through an account-scoped serialization queue at the application layer; or partition the accounts table by account range .
Write p99 > 100ms with synchronous_commit = remote_apply; replica WAL apply lag visible in pg_stat_replication; network jitter between primary and replica causing write latency spikes correlating with replication ACK delays
Tier 2: Synchronous Replication Write Latency: Synchronous replication write-ahead wait amplifying network latency for every committed transaction. Recommended evolution: Co-locate primary and replica in the same availability zone for lowest replication RTT; tune wal_sender_timeout and recovery_min_apply_delay; evaluate whether synchronous_commit = on (durable to primary WAL only) is acceptable for your regulatory risk model .
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) .
Readiness Requirements
PostgreSQL: scenario includes high_write_throughput or write_heavy workload
BothDeploy PgBouncer in transaction-mode pooling before relying on vertical scaling
Required maturity: mid_level
Runbooks and alerting for high-severity risks
Both4 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Apache Kafka: scenario has team_maturity below senior
LeftKafka operational complexity requires dedicated expertise: consider MSK or Confluent Cloud to reduce ops burden
Required maturity: senior
Apache Kafka: scenario uses Kafka for event streaming or CDC
LeftSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Event stream operations expertise
LeftThis architecture includes event stream infrastructure (Kafka, Kinesis, or similar). Operations requires consumer group management, partition assignment, dead-letter handling, and lag monitoring.
Required maturity: platform_engineering_team
Minimum team maturity: Platform Engineering Team
LeftThis scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.
Required maturity: platform_engineering_team
Replica lag monitoring and lag-aware routing
LeftRead replicas must be monitored for replication lag. The application router must include a max_lag_ms threshold; queries above that threshold must be redirected to the primary.
Cache sizing and eviction policy configuration
RightRedis or equivalent cache requires correct maxmemory configuration, eviction policy selection (allkeys-lru is common), and cold-start warming strategy after restarts.
Minimum team maturity: Small Product Team
RightThis scenario has moderate operational complexity. It is recommended for Small Product Team teams or higher.
Required maturity: small_product_team
Redis: scenario has read_heavy workload with high cache miss risk
RightImplement cache stampede protection (probabilistic early expiry or locking) to prevent thundering herd on cold start
Required maturity: junior
Redis: scenario relies on Redis for data that cannot be re-derived
RightRedis is not a durable store: add persistence layer or treat Redis as expendable cache only
Required maturity: junior
Generator Constraints
Financial Ledger Platform
LeftGenerator relevance documented but not yet production-ready.
For financial product briefs, the generator must output event sourcing + outbox + CQRS as mandatory components, not optional enhancements. synchronous_commit settings, replication standby configuration, and Kafka min.insync.replicas must be generated as explicit configuration, not left as defaults. Two-phase commit should be presented as a cross-service coordination option with explicit complexity warnings.
Multi-Tenant SaaS Platform
RightGenerator relevance documented but not yet production-ready.
For SaaS product briefs with multi-tenant requirements, the generator should propose the shared-schema + RLS + Redis cache composition as the starting point for small to medium tenant counts (< 1000 tenants). Database-per-tenant (silo model) should be presented as an alternative for high isolation requirements or large enterprise tenants. The generator must output RLS policy templates and cache key namespacing as non-optional components.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_financial_ledger_platform_vs_multi_tenant_saas_platform | Full comparison of Financial Ledger Platform vs Multi-Tenant SaaS Platform: 6 dimensions, 1 shared components, 0 shared risks. |
| Advisor | advisor_financial_ledger_platform | Advisor for Financial Ledger Platform: 6 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_multi_tenant_saas_platform | Advisor for Multi-Tenant SaaS Platform: 4 strengths, 4 risks, maturity: intermediate. |
| Scenario | financial_ledger_platform | Scenario 'Financial Ledger Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| Scenario | multi_tenant_saas_platform | Scenario 'Multi-Tenant SaaS Platform': 4 scaling thresholds, 3 migration paths, complexity: moderate. |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_lock_contention | Write-Heavy Transactional → Lock Contention |
| Risk Path | prop_architecture_pattern_two_phase_commit_risk_split_brain | Two-Phase Commit (2PC) → Split-Brain |
| Risk Path | prop_architecture_pattern_sharding_risk_hot_partition | Sharding → Hot Partition |
| Risk Path | prop_technology_profile_redis_risk_connection_exhaustion | Redis → Connection Pool Exhaustion |
| Risk Path | prop_workload_profile_write_heavy_transactional_risk_lock_contention | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_architecture_pattern_two_phase_commit_risk_split_brain | Referenced by the operational risk comparison dimension. |
Limitations
- ·Decision guidance is grounded in YAML knowledge only. Not measured from any production system.
- ·Recommendations are deterministic heuristics based on structured knowledge. Your specific workload, team profile, and business context may lead to different conclusions.
- ·Generator constraints are preliminary. No scenario should be treated as production generation-ready at this stage.
Comparison complete
Profile, topology, simulation, advisor, comparison, and decision path are ready. Your architecture decision is grounded in structured knowledge and deterministic reasoning.