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
Analytics Data Platform vs Multi-Tenant SaaS Platform: Multi-Tenant SaaS Platform is the simpler choice
Multi-Tenant SaaS Platform is the simpler architecture. Analytics Data Platform carries lower operational risk. They share 2 component(s). Analytics Data Platform has 2 unique risk(s); Multi-Tenant SaaS Platform has 3. Multi-Tenant SaaS Platform requires lower team maturity to operate.
11
Nodes
5
Edges
3
Risks
1
Seeds
4
Strengths
3
Adv. Risks
11
Nodes
7
Edges
4
Risks
3
Seeds
4
Strengths
4
Adv. Risks
Comparison Dimensions
Complexity
Analytics Data Platform
high complexity, 11 nodes, 5 edges, 3 risks, 1 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 11 for Analytics Data Platform.
Operational Risk
Analytics Data Platform
3 risks (top: high), 2 high/critical, 0 confirmed by simulation
Multi-Tenant SaaS Platform
4 risks (top: high), 3 high/critical, 2 confirmed by simulation
Analytics Data Platform has lower operational risk: weighted severity score 10 vs 14 (0 vs 2 simulation-confirmed).
Scalability
Analytics Data 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
Analytics Data Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 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 Analytics Data Platform.
Observability
Analytics Data Platform
4 watched metrics, 3 observability recommendations, 1 simulation seeds
Multi-Tenant SaaS Platform
9 watched metrics, 6 observability recommendations, 3 simulation seeds
Analytics Data Platform has lower observability burden: 4 watched metrics vs 9.
Generator Readiness
Analytics Data Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes
Multi-Tenant SaaS Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Multi-Tenant SaaS Platform has more documented generator readiness signals. Note: this is still preliminary.
Architecture Components
Shared (2)
Only in Analytics Data Platform (9)
Only in Multi-Tenant SaaS Platform (9)
Operational Risks
Shared (1)
Only in Analytics Data Platform (2)
Only in Multi-Tenant SaaS Platform (3)
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
Analytics Data Platform has high complexity. Multi-Tenant SaaS Platform has moderate complexity. Simpler systems often carry different (not necessarily fewer) risks.
Analytics Data Platform
Analytics Data Platform: 3 risks (top: high), 2 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
Analytics Data Platform offers 4 defined scaling thresholds. Multi-Tenant SaaS Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Analytics Data 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. Analytics Data Platform requires deeper operational expertise.
Analytics Data Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 operational requirements
Multi-Tenant SaaS Platform
Advisor assessment: Intermediate; recommended team: Small Product Team; 6 operational requirements
Event-Driven vs Synchronous Processing
Analytics Data Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Multi-Tenant SaaS Platform does not, keeping the stack simpler but less decoupled.
Analytics Data 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.
Analytics Data Platform
4 strengths, 3 risks
Multi-Tenant SaaS Platform
4 strengths, 4 risks
Migration Considerations
Migration Step 1
Analytics Data Platform
Analytics queries running directly against PostgreSQL OLTP primary → Read replica serving analytics queries via polling ETL
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. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. Multi-Tenant SaaS Platform: triggered by 'Adding a second paying customer; first audit or security rev'.
Migration Step 2
Analytics Data Platform
Polling ETL from read replica to analytics store → WAL CDC → Kafka → ClickHouse streaming ingestion
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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. Multi-Tenant SaaS Platform: triggered by 'Database read load growing disproportionately with tenant co'.
Migration Step 3
Analytics Data Platform
ClickHouse with raw event tables only → ClickHouse with materialized views and pre-aggregated summary tables
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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'.
Advisor Notes
Strength: Analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query…
Analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query cost from minutes per query to milliseconds per lookup. Key trade-off: Materialized views add write overhead (refresh cost) and storage overhead (duplicate data). Operational note: Refresh interval determines data freshness: every 1 hour is typical for BI dashboards. Evidence: Snowflake automatic clustering and materialized views reduce repeated full-scan aggregation by 10-100x.
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): Queue Backlog Accumulation
Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.
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 · 13 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.
- ·3 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 Analytics Data Platform
Multi-Tenant SaaS Platform leads on 3 weighted dimension(s): Complexity, Scalability, Operational Maturity. Weighted score: 4.5 vs 3.0 for Analytics Data Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Analytics Data 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 Analytics Data Platform
Multi-Tenant SaaS Platform leads on 3 weighted dimension(s): Complexity, Scalability, Operational Maturity. Weighted score: 4.5 vs 3.0 for Analytics Data Platform. The architectures share 2 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 Analytics Data Platform (advanced rating)?
If Yes
Your team can operate Analytics Data 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 Analytics Data 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
Analytics Data 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 11 for Analytics Data 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
Analytics Data Platform
LeftWhen stability and predictability matter most
CriticalAnalytics Data Platform carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateAnalytics Data Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your architecture benefits from: analytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query…
ModerateAnalytics-heavy workloads pre-compute expensive aggregations and joins into materialized views, reducing repeated full-scan query cost from minutes per query to milliseconds per lookup. Key trade-off: Materialized views add write overhead (refresh cost) and storage overhead (duplicate data). Operational note: Refresh interval determines data freshness: every 1 hour is typical for BI dashboards. Evidence: Snowflake automatic clustering and materialized views reduce repeated full-scan aggregation by 10-100x.
When your architecture benefits from: clickhouse's columnar storage engine, vectorized query execution, and mergetree family of table engines are specifically designed…
ModerateClickHouse's columnar storage engine, vectorized query execution, and MergeTree family of table engines are specifically designed for analytics-heavy workloads: high-throughput aggregations over billions of rows with sub-second query latency. Key trade-off: ClickHouse has limited transaction support: ACID transactions are not a design goal. Operational note: ClickHouse is optimized for inserts, not updates: use ReplacingMergeTree or CollapsingMergeTree for mutable data. Evidence: ClickHouse processes 100 million rows/second per core for aggregation queries in documented benchmarks.
When your system requires decoupled async event processing
HighAnalytics Data 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 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
Analytics Data Platform
LeftWhen your team cannot mitigate: queue backlog accumulation
HighThis architecture is significantly exposed to Queue Backlog Accumulation. Message queue or event stream consumer processing rate falls below producer write rate, causing consumer lag to grow unboundedly: eventually leading to increased end-to-end latency, producer backpressure, data expiry, or queue resource exhaustion.
When 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 is early-stage or solo
HighAnalytics Data 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 6 predicted bottlenecks for Analytics Data 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 Analytics Data 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.
- ↳Analytics Data Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
LeftA platform team can safely operate Analytics Data 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. Analytics Data Platform: triggered by 'OLTP query p99 degrading during analytics reporting windows;'. 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. Analytics Data Platform: triggered by 'Sub-minute analytics freshness SLA required; ETL scheduling '. 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. Analytics Data Platform: triggered by 'Dashboard query p95 > 5s on frequently accessed aggregation '. Multi-Tenant SaaS Platform: triggered by 'Enterprise customer requesting dedicated infrastructure in c'.
Kafka consumer group lag (bytes or offsets) growing for the analytics topic group; ClickHouse dashboard timestamps falling behind wall clock by > 60s; ClickHouse insert throughput < Kafka produce rate
Tier 1: Consumer Lag and Freshness Degradation: ClickHouse insert throughput insufficient for Kafka produce rate. Recommended evolution: Tune ClickHouse insert buffer size and async_insert settings; increase consumer parallelism up to the Kafka partition count; batch inserts into ClickHouse using the Buffer engine or materialized views with merge trees .
One Kafka partition offset growing significantly faster than others; one consumer instance CPU/network saturated while others are idle
Tier 2: Hot Partition and Skewed Consumer Load: Skewed partition key distribution: high-cardinality entity routing the same high-volume key to one partition. Recommended evolution: Add a secondary hash suffix to the partition key to distribute load; increase topic partition count (note: keyed ordering breaks for existing messages); re-evaluate partition key selection based on actual cardinality measurements .
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
Both2 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
ClickHouse: scenario has analytics_olap or event_aggregation workload
LeftBatch inserts to ClickHouse in minimum 1k-row batches; single-row inserts cause part fragmentation
Required maturity: mid_level
ClickHouse: scenario uses ClickHouse for OLTP workloads
LeftClickHouse is an OLAP engine: mutations (UPDATE/DELETE) are async and expensive; use PostgreSQL for transactional workloads
Required maturity: mid_level
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: Experienced Backend Team
LeftThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_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
Analytics Data Platform
LeftGenerator relevance documented but not yet production-ready.
For product briefs requiring operational or large-scale analytics with streaming freshness, the generator should propose the WAL CDC → Kafka → ClickHouse composition as the canonical analytics path. Polling ETL should be presented as the lower-complexity starting point for basic_reporting needs. Materialized views in ClickHouse should be generated as optional acceleration for identified high-cost query patterns.
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_analytics_data_platform_vs_multi_tenant_saas_platform | Full comparison of Analytics Data Platform vs Multi-Tenant SaaS Platform: 6 dimensions, 2 shared components, 1 shared risks. |
| Advisor | advisor_analytics_data_platform | Advisor for Analytics Data Platform: 4 strengths, 3 risks, maturity: advanced. |
| Advisor | advisor_multi_tenant_saas_platform | Advisor for Multi-Tenant SaaS Platform: 4 strengths, 4 risks, maturity: intermediate. |
| Scenario | analytics_data_platform | Scenario 'Analytics Data Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | multi_tenant_saas_platform | Scenario 'Multi-Tenant SaaS Platform': 4 scaling thresholds, 3 migration paths, complexity: moderate. |
| Risk Path | prop_failure_mode_slow_consumer_risk_queue_backlog_accumulation | Slow Consumer → Queue Backlog Accumulation |
| 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_failure_mode_slow_consumer_risk_queue_backlog_accumulation | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_architecture_pattern_sharding_risk_hot_partition | 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.