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 Two-Sided Marketplace Platform
Multi-Tenant SaaS Platform is the simpler architecture. Multi-Tenant SaaS Platform carries lower operational risk. They share 5 component(s). Multi-Tenant SaaS Platform has 3 unique risk(s); Two-Sided Marketplace Platform has 4. Multi-Tenant SaaS Platform requires lower team maturity to operate.
11
Nodes
7
Edges
4
Risks
3
Seeds
4
Strengths
4
Adv. Risks
19
Nodes
0
Edges
5
Risks
2
Seeds
0
Strengths
5
Adv. Risks
Comparison Dimensions
Complexity
Multi-Tenant SaaS Platform
moderate complexity, 11 nodes, 7 edges, 4 risks, 3 simulation seeds
Two-Sided Marketplace Platform
expert complexity, 19 nodes, 0 edges, 5 risks, 2 simulation seeds
Multi-Tenant SaaS Platform is simpler: moderate operational complexity with 11 topology nodes vs 19 for Two-Sided Marketplace Platform.
Operational Risk
Multi-Tenant SaaS Platform
4 risks (top: high), 3 high/critical, 2 confirmed by simulation
Two-Sided Marketplace Platform
5 risks (top: high), 5 high/critical, 1 confirmed by simulation
Multi-Tenant SaaS Platform has lower operational risk: weighted severity score 14 vs 20 (2 vs 1 simulation-confirmed).
Scalability
Multi-Tenant SaaS Platform
4 scaling thresholds, 3 migration paths, 9 advisor scaling signals
Two-Sided Marketplace Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. Multi-Tenant SaaS Platform and Two-Sided Marketplace Platform offer similar numbers of defined evolution steps.
Operational Maturity
Multi-Tenant SaaS Platform
Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements
Two-Sided Marketplace Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 15 operational requirements
Multi-Tenant SaaS Platform requires lower team maturity (Intermediate) vs Advanced for Two-Sided Marketplace Platform.
Observability
Multi-Tenant SaaS Platform
9 watched metrics, 6 observability recommendations, 3 simulation seeds
Two-Sided Marketplace Platform
5 watched metrics, 7 observability recommendations, 2 simulation seeds
Two-Sided Marketplace Platform has lower observability burden: 5 watched metrics vs 9.
Generator Readiness
Multi-Tenant SaaS Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Two-Sided Marketplace Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 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 (5)
Only in Multi-Tenant SaaS Platform (6)
Only in Two-Sided Marketplace Platform (14)
Operational Risks
Shared (1)
Only in Multi-Tenant SaaS Platform (3)
Only in Two-Sided Marketplace Platform (4)
Consistency Guarantees
Neither scenario has a recorded consistency-guarantee claim.
Neither scenario has recorded a consistency-guarantee claim; no guarantee comparison is possible from the data on record.
Tradeoff Summary
Complexity vs Risk
Multi-Tenant SaaS Platform has moderate complexity. Two-Sided Marketplace Platform has expert complexity. Simpler systems often carry different (not necessarily fewer) risks.
Multi-Tenant SaaS Platform
Multi-Tenant SaaS Platform: 4 risks (top: high), 3 high/critical, 2 confirmed by simulation
Two-Sided Marketplace Platform
Two-Sided Marketplace Platform: 5 risks (top: high), 5 high/critical, 1 confirmed by simulation
Scaling Path
Multi-Tenant SaaS Platform offers 4 defined scaling thresholds. Two-Sided Marketplace Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Multi-Tenant SaaS Platform
4 scaling thresholds, 3 migration paths, 9 advisor scaling signals
Two-Sided Marketplace Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Team Maturity Requirement
Multi-Tenant SaaS Platform can be operated by a less experienced team. Two-Sided Marketplace Platform requires deeper operational expertise.
Multi-Tenant SaaS Platform
Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements
Two-Sided Marketplace Platform
Advisor assessment: Advanced; recommended team: Platform Engineering Team; 15 operational requirements
Event-Driven vs Synchronous Processing
Two-Sided Marketplace Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Multi-Tenant SaaS Platform does not, keeping the stack simpler but less decoupled.
Multi-Tenant SaaS Platform
No event stream: simpler stack, synchronous dependencies
Two-Sided Marketplace Platform
Event stream: async decoupling, consumer lag risk, higher ops burden
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.
Multi-Tenant SaaS Platform
4 strengths, 4 risks
Two-Sided Marketplace Platform
0 strengths, 5 risks
Migration Considerations
Migration Step 1
Multi-Tenant SaaS Platform
Single-tenant PostgreSQL with per-user row filtering in application code → Multi-tenant PostgreSQL with row-level security policies
Two-Sided Marketplace Platform
Monolithic marketplace application with single database → Event-driven marketplace with Kafka + saga-based checkout flow
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'. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'.
Migration Step 2
Multi-Tenant SaaS Platform
Shared schema multi-tenant with no caching → Shared schema + Redis with tenant-namespaced cache keys
Two-Sided Marketplace Platform
PostgreSQL full-text search for listing discovery → Elasticsearch for listing search with CDC-based indexing
Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'.
Migration Step 3
Multi-Tenant SaaS Platform
Shared schema for all tenants → Hybrid: dedicated database per enterprise tenant + shared schema for standard tenants
Two-Sided Marketplace Platform
Monolithic PostgreSQL serving all domain writes → Domain-separated databases with event-based cross-domain data propagation
Both scenarios define a migration step at this stage. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'.
Advisor Notes
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): 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.
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: Cache sizing and eviction policy configuration, PostgreSQL: scenario includes high_write_throughput or write_heavy workload, Redis: scenario has read_heavy workload with high cache miss risk.
Supporting Evidence · 16 items
Coverage Warnings
- ⚠Two-Sided Marketplace Platform: fewer than 2 architectural strengths identified. the risk/complexity dimensions are present but the strength analysis is thin. Consider enriching the relationship YAMLs referenced by this scenario to improve coverage.
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.
- ·Neither scenario has a recorded consistency-guarantee claim. Guarantee comparison is uninformative for this pair, not silently empty.
Multi-Tenant SaaS Platform is the recommended starting point over Two-Sided Marketplace Platform
Multi-Tenant SaaS Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 5.5 vs 1.0 for Two-Sided Marketplace Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Multi-Tenant SaaS Platform and Two-Sided Marketplace 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 Two-Sided Marketplace Platform
Multi-Tenant SaaS Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Operational Maturity. Weighted score: 5.5 vs 1.0 for Two-Sided Marketplace Platform. The architectures share 5 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
LeftMulti-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 Two-Sided Marketplace Platform (advanced rating)?
If Yes
Your team can operate Two-Sided Marketplace Platform. Continue to Step 2 to refine based on risk tolerance and workload fit.
If No
Prefer the lower-maturity option: left 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: high sustained load with clear migration paths?
If Yes
Both scenarios have comparable scaling paths. Choose based on complexity preference.
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
Two-Sided Marketplace 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 19 for Two-Sided Marketplace 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
Multi-Tenant SaaS Platform
LeftWhen 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 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.
Two-Sided Marketplace Platform
RightWhen you want to minimise monitoring setup overhead
ModerateTwo-Sided Marketplace Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your system requires decoupled async event processing
HighTwo-Sided Marketplace Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
When to Avoid Each Scenario
Multi-Tenant SaaS Platform
LeftWhen 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.
Two-Sided Marketplace 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: cascading failure
HighThis architecture is significantly exposed to Cascading Failure. A failure or degradation in one service causes increased load, held resources, or error propagation in its callers, which in turn degrade their callers, until the failure front propagates through the entire dependency graph and brings down services with no direct dependency on the original failure point.
When your team is early-stage or solo
HighTwo-Sided Marketplace 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 9 predicted bottlenecks for Two-Sided Marketplace Platform. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
LeftMulti-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)
LeftMulti-Tenant SaaS Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Two-Sided Marketplace 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.
- ↳Two-Sided Marketplace Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Two-Sided Marketplace 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. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'. Two-Sided Marketplace Platform: triggered by 'Checkout failures from payment provider unavailability causi'.
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'. Two-Sided Marketplace Platform: triggered by 'Listing search p99 > 1s; faceted navigation (category + pric'.
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'. Two-Sided Marketplace Platform: triggered by 'Domain teams stepping on each other's schema migrations; dat'.
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) .
Redis cache miss spike visible in monitoring; PostgreSQL query rate spiking for listing reads despite stable write volume; p99 listing API latency > 500ms during traffic spike events
Tier 1: Viral Listing Thundering Herd: Cache TTL expiry on hot listings during peak traffic: all concurrent requests bypass cache simultaneously. Recommended evolution: Implement staggered TTL jitter on listing cache entries; use probabilistic early refresh (refresh before TTL expiry when remaining TTL < 20% and request rate is high); implement single-flight/request coalescing at the application layer to collapse concurrent cache misses into a single database read .
Saga compensation events appearing in order event log; checkout p99 > 2s; pg_locks showing contended rows on inventory_reservations table; idempotency key conflicts increasing in payment service logs
Tier 2: Checkout Saga Contention: Concurrent checkout transactions competing for the same inventory rows; saga timeout thresholds too aggressive. Recommended evolution: Increase inventory reservation table partition count; tune saga step timeout to 2x the observed p99 for each step under load; implement a per-listing checkout serialization queue to prevent N concurrent sagas competing for the same inventory .
Readiness Requirements
Cache sizing and eviction policy configuration
BothRedis or equivalent cache requires correct maxmemory configuration, eviction policy selection (allkeys-lru is common), and cold-start warming strategy after restarts.
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
Redis: scenario has read_heavy workload with high cache miss risk
BothImplement 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
BothRedis is not a durable store: add persistence layer or treat Redis as expendable cache only
Required maturity: junior
Runbooks and alerting for high-severity risks
Both3 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Minimum team maturity: Small Product Team
LeftThis scenario has moderate operational complexity. It is recommended for Small Product Team teams or higher.
Required maturity: small_product_team
Apache Kafka: scenario has team_maturity below senior
RightKafka 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
RightSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
Elasticsearch: scenario has full_text_search or log_analytics workload
RightConfigure ILM policies from day one to prevent shard explosion as data grows
Required maturity: senior
Elasticsearch: scenario uses Elasticsearch as a primary datastore
RightElasticsearch is a search index, not a source of truth: add a durable primary store and sync to ES
Required maturity: senior
Elasticsearch: scenario uses dynamic mappings on high-cardinality fields
RightDefine explicit index mappings: dynamic mapping on high-cardinality fields causes mapping explosions and cluster instability
Required maturity: senior
Event stream operations expertise
RightThis 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
RightThis scenario has expert operational complexity. It is recommended for Platform Engineering Team teams or higher.
Required maturity: platform_engineering_team
RabbitMQ: scenario has high_throughput_writes exceeding 50k messages/second
RightRabbitMQ throughput ceiling may be insufficient: evaluate Kafka for sustained high-throughput event streams
Required maturity: mid_level
RabbitMQ: scenario requires event replay or consumer catch-up from historical messages
RightRabbitMQ deletes acknowledged messages: use Kafka for replay-capable event streaming
Required maturity: mid_level
RabbitMQ: scenario uses classic mirrored queues for HA
RightMigrate to quorum queues: classic mirrored queues have known split-brain behavior under network partition
Required maturity: mid_level
Generator Constraints
Multi-Tenant SaaS Platform
LeftGenerator 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.
Two-Sided Marketplace Platform
RightGenerator relevance documented but not yet production-ready.
For marketplace product briefs, the generator must produce the full event-driven composition: API gateway → domain services → outbox → Kafka → downstream consumers. Saga orchestration templates for the checkout flow (create_order → reserve_inventory → charge_payment → notify_seller) must be generated with explicit compensation paths. The notification subsystem (RabbitMQ + dead-letter queue) must be generated as a separate deployable unit with its own operational SLA.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_multi_tenant_saas_platform_vs_marketplace_platform | Full comparison of Multi-Tenant SaaS Platform vs Two-Sided Marketplace Platform: 6 dimensions, 5 shared components, 1 shared risks. |
| Advisor | advisor_multi_tenant_saas_platform | Advisor for Multi-Tenant SaaS Platform: 4 strengths, 4 risks, maturity: intermediate. |
| Advisor | advisor_marketplace_platform | Advisor for Two-Sided Marketplace Platform: 0 strengths, 5 risks, maturity: advanced. |
| Scenario | multi_tenant_saas_platform | Scenario 'Multi-Tenant SaaS Platform': 4 scaling thresholds, 3 migration paths, complexity: moderate. |
| Scenario | marketplace_platform | Scenario 'Two-Sided Marketplace Platform': 4 scaling thresholds, 3 migration paths, complexity: expert. |
| 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_marketplace_mixed_workload_risk_hot_partition | Marketplace Mixed → Hot Partition |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_architecture_pattern_sharding_risk_hot_partition | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_technology_profile_redis_risk_connection_exhaustion | 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.