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
AI Retrieval-Augmented Generation Platform is both simpler and lower-risk than Streaming Media Platform
AI Retrieval-Augmented Generation Platform is the simpler architecture. AI Retrieval-Augmented Generation Platform carries lower operational risk. They share 7 component(s). AI Retrieval-Augmented Generation Platform has 2 unique risk(s); Streaming Media Platform has 4.
12
Nodes
0
Edges
4
Risks
2
Seeds
0
Strengths
4
Adv. Risks
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
AI Retrieval-Augmented Generation Platform
high complexity, 12 nodes, 0 edges, 4 risks, 2 simulation seeds
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
AI Retrieval-Augmented Generation Platform is simpler: high operational complexity with 12 topology nodes vs 21 for Streaming Media Platform.
Operational Risk
AI Retrieval-Augmented Generation Platform
4 risks (top: high), 2 high/critical, 0 confirmed by simulation
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
AI Retrieval-Augmented Generation Platform has lower operational risk: weighted severity score 12 vs 22 (0 vs 0 simulation-confirmed).
Scalability
AI Retrieval-Augmented Generation Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Both scenarios have comparable scaling paths. The best choice depends on your specific growth trajectory. AI Retrieval-Augmented Generation Platform and Streaming Media Platform offer similar numbers of defined evolution steps.
Operational Maturity
AI Retrieval-Augmented Generation Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 9 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
AI Retrieval-Augmented Generation Platform
4 watched metrics, 3 observability recommendations, 2 simulation seeds
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
AI Retrieval-Augmented Generation Platform has lower observability burden: 4 watched metrics vs 8.
Generator Readiness
AI Retrieval-Augmented Generation Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 2 seeds with generator notes
Streaming Media 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 (7)
Only in AI Retrieval-Augmented Generation Platform (5)
Only in Streaming Media Platform (14)
Operational Risks
Only in AI Retrieval-Augmented Generation Platform (2)
Only in Streaming Media 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
AI Retrieval-Augmented Generation Platform has high complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
AI Retrieval-Augmented Generation Platform
AI Retrieval-Augmented Generation Platform: 4 risks (top: high), 2 high/critical, 0 confirmed by simulation
Streaming Media Platform
Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
AI Retrieval-Augmented Generation Platform offers 4 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
AI Retrieval-Augmented Generation Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
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.
AI Retrieval-Augmented Generation Platform
0 strengths, 4 risks
Streaming Media Platform
0 strengths, 6 risks
Migration Considerations
Migration Step 1
AI Retrieval-Augmented Generation Platform
LLM application with no retrieval augmentation (prompt-only context) → PostgreSQL + pgvector for semantic retrieval with manual embedding generation
Streaming Media Platform
Synchronous transcoding in the upload request handler (blocking API response) → Async transcoding via Kafka topic with competing consumer workers
Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'LLM responses requiring more factual accuracy or domain-spec'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
AI Retrieval-Augmented Generation Platform
Synchronous embedding generation on write path → Asynchronous embedding pipeline via Kafka consumer
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'Document ingestion p99 > 500ms due to embedding API call in '. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
AI Retrieval-Augmented Generation Platform
Single pgvector index serving all document types → Partitioned vector indexes per document namespace or tenant
Streaming Media Platform
Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting
Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'Index scan range too large for per-query latency targets; te'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
Advisor Notes
Risk (high): Thundering Herd (Cache Stampede)
When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
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.
Shared Operational Requirements
Both scenarios require: Apache Kafka: scenario has team_maturity below senior, Apache Kafka: scenario uses Kafka for event streaming or CDC, Cache sizing and eviction policy configuration.
Supporting Evidence · 14 items
Coverage Warnings
- ⚠AI Retrieval-Augmented Generation 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.
- ⚠Streaming Media 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.
- ·5 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.
AI Retrieval-Augmented Generation Platform is the recommended starting point over Streaming Media Platform
AI Retrieval-Augmented Generation Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 1.0 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between AI Retrieval-Augmented Generation Platform and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
AI Retrieval-Augmented Generation Platform is the recommended starting point over Streaming Media Platform
AI Retrieval-Augmented Generation Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 5.5 vs 1.0 for Streaming Media Platform. The architectures share 7 component(s), reducing migration cost if you switch later. AI Retrieval-Augmented Generation Platform is the operationally simpler choice.
Where to Start
Start with AI Retrieval-Augmented Generation Platform
LeftAI Retrieval-Augmented Generation 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: high complexity, 12 nodes, 0 edges, 4 risks, 2 simulation seeds
Migrate when:
- Retrieval quality metrics (MRR, NDCG) declining despite stable query volume; users reporting irrelevant context being surfaced; pgvector IVFFlat probes set below recommended value for current document count → Schedule periodic index rebuilds triggered by document count growth (e.g., rebuild at 2x the document count present at last index build); increase ivfflat.probes to improve recall at cost of query latency; evaluate HNSW for recall-critical workloads
- PostgreSQL process memory > 8GB; OOM killer events on the database host; vector query p99 latency increasing as shared_buffers evicts vector index pages; pg_stat_bgwriter showing high buffers_clean rate → Increase PostgreSQL shared_buffers to 40% of available RAM; move vector tables to a dedicated tablespace on NVMe; partition large vector tables by document category to reduce per-query index scan range; evaluate dedicated pgvector replica for query isolation
- Kafka consumer group lag growing for the embedding generation consumer; document ingestion reporting "indexing pending" status for > 5 minutes; embedding API rate limit errors in consumer logs → Increase embedding consumer parallelism (capped at Kafka partition count); batch documents per embedding API call to improve inference efficiency; implement priority queuing to index recent documents ahead of backlog
Decision Flow
Does your team have the operational maturity to run AI Retrieval-Augmented Generation Platform (advanced rating)?
If Yes
Your team can operate AI Retrieval-Augmented Generation 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 AI Retrieval-Augmented Generation 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.
Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?
If Yes
AI Retrieval-Augmented Generation Platform is the simpler choice: AI Retrieval-Augmented Generation Platform is simpler: high operational complexity with 12 topology nodes vs 21 for Streaming Media 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
AI Retrieval-Augmented Generation Platform
LeftWhen operational simplicity is a top priority
HighAI Retrieval-Augmented Generation Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalAI Retrieval-Augmented Generation Platform carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateAI Retrieval-Augmented Generation Platform has a lower observability burden: fewer watched metrics and monitoring targets.
When your system requires decoupled async event processing
HighAI Retrieval-Augmented Generation Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Streaming Media Platform
RightWhen your system requires decoupled async event processing
HighStreaming Media Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
When to Avoid Each Scenario
AI Retrieval-Augmented Generation Platform
LeftWhen your team cannot mitigate: thundering herd (cache stampede)
HighThis architecture is significantly exposed to Thundering Herd (Cache Stampede). When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
When your team cannot mitigate: memory pressure and oom kill
HighThis architecture is significantly exposed to Memory Pressure and OOM Kill. When total memory demand from a process or the entire host exceeds available physical RAM plus swap, the Linux OOM killer terminates one or more processes to reclaim memory, causing immediate connection loss, data corruption risk if in-flight writes are lost, and process restart overhead.
When your team is early-stage or solo
HighAI Retrieval-Augmented Generation 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 AI Retrieval-Augmented Generation Platform. Rapid growth will surface these limitations quickly.
Streaming Media Platform
RightWhen 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: thundering herd (cache stampede)
HighThis architecture is significantly exposed to Thundering Herd (Cache Stampede). When a popular cached key expires or a service recovers from downtime, all requests that were waiting or arrive simultaneously miss the cache and hit the origin database concurrently, producing a request spike that can overwhelm the database within seconds.
When your team is early-stage or solo
HighStreaming Media 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 Streaming Media Platform. Rapid growth will surface these limitations quickly.
Team Fit
Solo developer or small startup
LeftAI Retrieval-Augmented Generation 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)
LeftAI Retrieval-Augmented Generation Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Streaming Media 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.
- ↳Streaming Media Platform may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
RightA platform team can safely operate Streaming Media 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. AI Retrieval-Augmented Generation Platform: triggered by 'LLM responses requiring more factual accuracy or domain-spec'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'Document ingestion p99 > 500ms due to embedding API call in '. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Both scenarios define a migration step at this stage. AI Retrieval-Augmented Generation Platform: triggered by 'Index scan range too large for per-query latency targets; te'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
Retrieval quality metrics (MRR, NDCG) declining despite stable query volume; users reporting irrelevant context being surfaced; pgvector IVFFlat probes set below recommended value for current document count
Tier 1: Vector Index Recall Degradation: IVFFlat index not rebuilt after significant document additions; or probes too low for current index size. Recommended evolution: Schedule periodic index rebuilds triggered by document count growth (e.g., rebuild at 2x the document count present at last index build); increase ivfflat.probes to improve recall at cost of query latency; evaluate HNSW for recall-critical workloads .
PostgreSQL process memory > 8GB; OOM killer events on the database host; vector query p99 latency increasing as shared_buffers evicts vector index pages; pg_stat_bgwriter showing high buffers_clean rate
Tier 2: PostgreSQL Memory Pressure from Vector Operations: Vector index (HNSW or large IVFFlat) and embedding storage competing with relational data for shared_buffers. Recommended evolution: Increase PostgreSQL shared_buffers to 40% of available RAM; move vector tables to a dedicated tablespace on NVMe; partition large vector tables by document category to reduce per-query index scan range; evaluate dedicated pgvector replica for query isolation .
Kafka consumer group lag on the transcoding topic growing during peak upload hours; content availability delay > 10 minutes for newly uploaded videos; transcoding worker CPU consistently above 85% across all instances
Tier 1: Transcoding Worker Throughput: Transcoding consumer group undersized relative to peak upload volume. Recommended evolution: Increase transcoding consumer instances up to the transcoding topic partition count; tune partition count to match the maximum desired worker parallelism (set this at topic creation, not after lag appears); implement per-uploader upload rate limits to smooth burst input; consider priority queuing so premium-tier content does not wait behind bulk ingest jobs .
MinIO GET request rate spikes > 10x baseline immediately after content publish or CDN invalidation; MinIO p99 latency > 500ms; CDN miss ratio > 5% on popular content
Tier 2: CDN Origin Thundering Herd: CDN cache miss storm on first-play of new or recently-updated content. Recommended evolution: Implement origin request coalescing (single origin fetch per CDN node per object, queue subsequent requestors for the in-flight response); pre-warm CDN edges for anticipated high-traffic content before publish; add rate limiting at the origin gateway to cap per-second origin requests per content_id .
Readiness Requirements
Apache Kafka: scenario has team_maturity below senior
BothKafka 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
BothSet min.insync.replicas=2 with acks=all; monitor consumer lag as primary health signal
Required maturity: senior
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.
Event stream operations expertise
BothThis 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
BothThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
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
Both2 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Apache Cassandra: scenario has team_maturity below staff_plus
RightCassandra has the highest operational complexity of common datastores: consider managed options (Astra DB, Keyspaces) or simpler alternatives
Required maturity: staff_plus
Apache Cassandra: scenario has time_series or iot_telemetry workload
RightDesign partition keys with time-bucketing (e.g., date prefix) to prevent wide partitions as data grows
Required maturity: staff_plus
Apache Cassandra: scenario requires ad-hoc queries or analytics
RightCassandra cannot efficiently query non-partition-key dimensions: pair with Elasticsearch or ClickHouse for analytics
Required maturity: staff_plus
MinIO: scenario enables versioning without lifecycle expiration policies
RightConfigure ILM lifecycle policies with expiration rules for versioned objects; without expiration, version accumulation on high-churn objects consumes storage unboundedly
Required maturity: mid_level
MinIO: scenario stores large numbers of small objects (< 100KB average size)
RightMinIO's per-request overhead reduces effective throughput for small objects; evaluate aggregating small objects into larger archives or using a key-value store for small object access patterns
Required maturity: mid_level
Generator Constraints
AI Retrieval-Augmented Generation Platform
LeftGenerator relevance documented but not yet production-ready.
For AI product briefs requiring semantic retrieval over a document corpus, the generator should propose PostgreSQL + pgvector + Redis semantic cache + Kafka embedding pipeline as the canonical starting point. Dedicated vector databases (Qdrant, Weaviate, Pinecone) should be presented as migration paths for scale-out needs, not as default recommendations. The generator must output retrieval quality evaluation as a mandatory operational requirement alongside latency and error rate monitoring.
Streaming Media Platform
RightGenerator relevance documented but not yet production-ready.
For content platform briefs with video or audio delivery requirements, the generator should output the Kafka async transcoding pipeline, MinIO object storage, and CDN-first delivery as the canonical composition. Redis playback session with TTL enforcement and Cassandra for time-ordered viewing history should be generated as separate store responsibilities. The generator must flag the partition key design decision for the Cassandra history table as a mandatory architecture decision requiring explicit access pattern enumeration before schema creation.
Supporting Evidence
| Type | Reference | Explanation |
|---|---|---|
| Comparison | compare_ai_rag_platform_vs_streaming_media_platform | Full comparison of AI Retrieval-Augmented Generation Platform vs Streaming Media Platform: 6 dimensions, 7 shared components, 2 shared risks. |
| Advisor | advisor_ai_rag_platform | Advisor for AI Retrieval-Augmented Generation Platform: 0 strengths, 4 risks, maturity: advanced. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | ai_rag_platform | Scenario 'AI Retrieval-Augmented Generation Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_workload_profile_ai_embedding_lookup_risk_memory_pressure_oom | AI Embedding Lookup → Memory Pressure and OOM Kill |
| Risk Path | prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulation | Event Streaming → Queue Backlog Accumulation. also affects: Slow Consumer |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_ai_embedding_lookup_risk_memory_pressure_oom | 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.