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
Realtime Collaborative Editor is both simpler and lower-risk than Streaming Media Platform
Realtime Collaborative Editor is the simpler architecture. Realtime Collaborative Editor carries lower operational risk. They share 2 component(s). Realtime Collaborative Editor has 1 unique risk(s); Streaming Media Platform has 6. Streaming Media Platform requires lower team maturity to operate.
5
Nodes
2
Edges
1
Risks
1
Seeds
1
Strengths
1
Adv. Risks
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
Realtime Collaborative Editor
expert complexity, 5 nodes, 2 edges, 1 risks, 1 simulation seeds
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Realtime Collaborative Editor is simpler: expert operational complexity with 5 topology nodes vs 21 for Streaming Media Platform.
Operational Risk
Realtime Collaborative Editor
1 risks (top: high), 1 high/critical, 1 confirmed by simulation
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Realtime Collaborative Editor has lower operational risk: weighted severity score 4 vs 22 (1 vs 0 simulation-confirmed).
Scalability
Realtime Collaborative Editor
3 scaling thresholds, 2 migration paths, 6 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Streaming Media Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Realtime Collaborative Editor
Advisor assessment: Expert Only; recommended team: Enterprise Architecture Team; 6 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Streaming Media Platform requires lower team maturity (Advanced) vs Expert Only for Realtime Collaborative Editor.
Observability
Realtime Collaborative Editor
4 watched metrics, 2 observability recommendations, 1 simulation seeds
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Realtime Collaborative Editor has lower observability burden: 4 watched metrics vs 8.
Generator Readiness
Realtime Collaborative Editor
generator relevance documented; topology generation relevance noted; simulation relevance noted; 1 seeds with generator notes
Streaming Media Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 3 seeds with generator notes
Streaming Media Platform has more documented generator readiness signals. Note: this is still preliminary.
Architecture Components
Only in Realtime Collaborative Editor (3)
Only in Streaming Media Platform (19)
Operational Risks
Only in Realtime Collaborative Editor (1)
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
Realtime Collaborative Editor has expert complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Realtime Collaborative Editor
Realtime Collaborative Editor: 1 risks (top: high), 1 high/critical, 1 confirmed by simulation
Streaming Media Platform
Streaming Media Platform: 6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Scaling Path
Realtime Collaborative Editor offers 3 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Realtime Collaborative Editor
3 scaling thresholds, 2 migration paths, 6 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Team Maturity Requirement
Streaming Media Platform can be operated by a less experienced team. Realtime Collaborative Editor requires deeper operational expertise.
Realtime Collaborative Editor
Advisor assessment: Expert Only; recommended team: Enterprise Architecture Team; 6 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Event-Driven vs Synchronous Processing
Streaming Media Platform uses event stream infrastructure (Kafka/Kinesis), enabling decoupled async processing. Realtime Collaborative Editor does not, keeping the stack simpler but less decoupled.
Realtime Collaborative Editor
No event stream: simpler stack, synchronous dependencies
Streaming Media 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.
Realtime Collaborative Editor
1 strengths, 1 risks
Streaming Media Platform
0 strengths, 6 risks
Migration Considerations
Migration Step 1
Realtime Collaborative Editor
Short-polling API with version-based conflict detection → WebSocket + Redis pub/sub live propagation
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. Realtime Collaborative Editor: triggered by 'User-visible edit conflicts > 5% of sessions; poll interval '. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
Realtime Collaborative Editor
Last-write-wins conflict resolution → Operational transformation (OT) or CRDT-based conflict resolution
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Both scenarios define a migration step at this stage. Realtime Collaborative Editor: triggered by 'Data loss complaints from users editing simultaneously; conf'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Realtime Collaborative Editor
No further migration step defined
Streaming Media Platform
Single CDN provider with no origin rate limiting → Multi-CDN with origin request coalescing and rate limiting
Streaming Media Platform has a defined migration; Realtime Collaborative Editor does not at this stage.
Advisor Notes
Strength: A connection pool bounds the total database connections an application can open, preventing connection storms during traffic…
A 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.
Risk (high): 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.
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: 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 · 13 items
Coverage Warnings
- ⚠Realtime Collaborative Editor: 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.
- ⚠Realtime Collaborative Editor: fewer than 2 operational risks identified. the risk comparison dimension will reflect low advisor coverage, not a low-risk architecture.
- ⚠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.
- ·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.
Realtime Collaborative Editor is the recommended starting point over Streaming Media Platform
Realtime Collaborative Editor leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 4.5 vs 3.0 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Realtime Collaborative Editor and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Realtime Collaborative Editor is the recommended starting point over Streaming Media Platform
Realtime Collaborative Editor leads on 3 weighted dimension(s): Complexity, Operational Risk, Observability. Weighted score: 4.5 vs 3.0 for Streaming Media Platform. The architectures share 2 component(s), reducing migration cost if you switch later. Realtime Collaborative Editor is the operationally simpler choice.
Where to Start
Start with Realtime Collaborative Editor
LeftRealtime Collaborative Editor 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: expert complexity, 5 nodes, 2 edges, 1 risks, 1 simulation seeds
Migrate when:
- Server memory growing with active connections; file descriptor limits approached; new WebSocket connections refused → Increase file descriptor limits (ulimit); move to dedicated WebSocket server tier; implement connection multiplexing (multiple documents per connection where safe)
- Consecutive writes to the same document causing lock contention; write latency rising; auto-save batching queue depth increasing → Move to operational transformation or CRDT-based conflict resolution; batch writes and resolve conflicts in-process before database commit; consider append-only event log for document operations
- Redis memory growing; high number of active pub/sub channels per Redis instance; SUBSCRIBE/UNSUBSCRIBE operations becoming significant overhead → Shard Redis pub/sub by document range; implement channel expiry; consider dedicated messaging tier (e.g. Ably, Pusher) for very high session counts
Decision Flow
Does your team have the operational maturity to run Realtime Collaborative Editor (expert only rating)?
If Yes
Your team can operate Realtime Collaborative Editor. 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 Realtime Collaborative Editor: 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: 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 ?
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
Streaming Media 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
Realtime Collaborative Editor is the simpler choice: Realtime Collaborative Editor is simpler: expert operational complexity with 5 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
Realtime Collaborative Editor
LeftWhen operational simplicity is a top priority
HighRealtime Collaborative Editor has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalRealtime Collaborative Editor carries lower overall risk weight per the advisor's assessment.
When you want to minimise monitoring setup overhead
ModerateRealtime Collaborative Editor has a lower observability burden: fewer watched metrics and monitoring targets.
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.
Streaming Media Platform
RightWhen you need well-defined scaling thresholds and migration paths
HighStreaming Media Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your team has limited operational maturity
CriticalStreaming Media Platform is rated advanced , accessible for teams without deep platform expertise.
When 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
Realtime Collaborative Editor
LeftWhen 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 your team does not have platform engineering expertise
CriticalRealtime Collaborative Editor is rated 'expert only'. It requires deep operational expertise to run safely. Operating it without the right team leads to incidents.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 3 predicted bottlenecks for Realtime Collaborative Editor. 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
RightStreaming Media 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)
RightStreaming Media Platform suits small teams that need to move fast without deep platform tooling investment.
- ↳Consider Realtime Collaborative Editor 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.
- ↳Realtime Collaborative Editor may require additional runbook coverage and alerting investment.
Platform engineering team or SRE-equipped organisation
LeftA platform team can safely operate Realtime Collaborative Editor 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. Realtime Collaborative Editor: triggered by 'User-visible edit conflicts > 5% of sessions; poll interval '. 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. Realtime Collaborative Editor: triggered by 'Data loss complaints from users editing simultaneously; conf'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Streaming Media Platform has a defined migration; Realtime Collaborative Editor does not at this stage.
Server memory growing with active connections; file descriptor limits approached; new WebSocket connections refused
Tier 1: WebSocket Connection Ceiling: WebSocket server process connection limit or OS file descriptor ceiling. Recommended evolution: Increase file descriptor limits (ulimit); move to dedicated WebSocket server tier; implement connection multiplexing (multiple documents per connection where safe) .
Consecutive writes to the same document causing lock contention; write latency rising; auto-save batching queue depth increasing
Tier 2: Database Write Contention: High-frequency auto-save operations conflicting at the document row level; row-level locking under concurrent user edits . Recommended evolution: Move to operational transformation or CRDT-based conflict resolution; batch writes and resolve conflicts in-process before database commit; consider append-only event log for document operations .
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
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
Both1 high-severity risk identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
Minimum team maturity: Enterprise Architecture Team
LeftThis scenario has expert operational complexity. It is recommended for Enterprise Architecture Team teams or higher.
Required maturity: enterprise_architecture_team
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
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
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
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
Minimum team maturity: Experienced Backend Team
RightThis scenario has high operational complexity. It is recommended for Experienced Backend Team teams or higher.
Required maturity: experienced_backend_team
Generator Constraints
Realtime Collaborative Editor
LeftGenerator relevance documented but not yet production-ready.
When generating architectures for collaborative editing or presence-aware applications, the WebSocket + Redis pub/sub + PostgreSQL composition should be presented as the baseline. CRDT/OT conflict resolution should be surfaced as a required upgrade path before launch in production collaborative contexts. The knowledge base currently lacks detailed CRDT/OT pattern entries: these should be added as the knowledge base expands.
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_realtime_collaborative_editor_vs_streaming_media_platform | Full comparison of Realtime Collaborative Editor vs Streaming Media Platform: 6 dimensions, 2 shared components, 0 shared risks. |
| Advisor | advisor_realtime_collaborative_editor | Advisor for Realtime Collaborative Editor: 1 strengths, 1 risks, maturity: expert_only. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | realtime_collaborative_editor | Scenario 'Realtime Collaborative Editor': 3 scaling thresholds, 2 migration paths, complexity: expert. |
| Scenario | streaming_media_platform | Scenario 'Streaming Media Platform': 4 scaling thresholds, 3 migration paths, complexity: high. |
| Risk Path | prop_technology_profile_redis_risk_connection_exhaustion | Redis → Connection Pool Exhaustion |
| 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_connection_exhaustion | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_workload_profile_event_streaming_workload_risk_queue_backlog_accumulation | 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.