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
Social Feed Platform is both simpler and lower-risk than Streaming Media Platform
Social Feed Platform is the simpler architecture. Social Feed Platform carries lower operational risk. They share 9 component(s). Social Feed Platform has 2 unique risk(s); Streaming Media Platform has 3.
18
Nodes
0
Edges
5
Risks
4
Seeds
0
Strengths
5
Adv. Risks
21
Nodes
0
Edges
6
Risks
3
Seeds
0
Strengths
6
Adv. Risks
Comparison Dimensions
Complexity
Social Feed Platform
high complexity, 18 nodes, 0 edges, 5 risks, 4 simulation seeds
Streaming Media Platform
high complexity, 21 nodes, 0 edges, 6 risks, 3 simulation seeds
Social Feed Platform is simpler: high operational complexity with 18 topology nodes vs 21 for Streaming Media Platform.
Operational Risk
Social Feed Platform
5 risks (top: high), 4 high/critical, 1 confirmed by simulation
Streaming Media Platform
6 risks (top: high), 5 high/critical, 0 confirmed by simulation
Social Feed Platform has lower operational risk: weighted severity score 18 vs 22 (1 vs 0 simulation-confirmed).
Scalability
Social Feed Platform
4 scaling thresholds, 3 migration paths, 6 advisor scaling signals
Streaming Media Platform
4 scaling thresholds, 3 migration paths, 4 advisor scaling signals
Social Feed Platform has more defined scaling paths: 4 thresholds and 3 migration paths.
Operational Maturity
Social Feed Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 12 operational requirements
Streaming Media Platform
Advisor assessment: Advanced; recommended team: Experienced Backend Team; 14 operational requirements
Both scenarios require equivalent team maturity: Advanced.
Observability
Social Feed Platform
12 watched metrics, 6 observability recommendations, 4 simulation seeds
Streaming Media Platform
8 watched metrics, 7 observability recommendations, 3 simulation seeds
Streaming Media Platform has lower observability burden: 8 watched metrics vs 12.
Generator Readiness
Social Feed Platform
generator relevance documented; topology generation relevance noted; simulation relevance noted; 4 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 (9)
Only in Social Feed Platform (9)
Only in Streaming Media Platform (12)
Operational Risks
Only in Social Feed Platform (2)
Only in Streaming Media 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
Social Feed Platform has high complexity. Streaming Media Platform has high complexity. Simpler systems often carry different (not necessarily fewer) risks.
Social Feed Platform
Social Feed Platform: 5 risks (top: high), 4 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
Social Feed Platform offers 4 defined scaling thresholds. Streaming Media Platform offers 4. More defined paths means clearer evolution steps but also more anticipated growth.
Social Feed Platform
4 scaling thresholds, 3 migration paths, 6 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.
Social Feed Platform
0 strengths, 5 risks
Streaming Media Platform
0 strengths, 6 risks
Migration Considerations
Migration Step 1
Social Feed Platform
Monolithic feed built on PostgreSQL timeline queries → Redis pre-materialized feed with Kafka async fan-out workers
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. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'. Streaming Media Platform: triggered by 'Upload API p99 exceeding 30 seconds due to in-process transc'.
Migration Step 2
Social Feed Platform
Uniform fan-out-on-write for all accounts → Hybrid fan-out model (fan-out-on-write for <10k followers, fan-out-on-read for high-follower accounts)
Streaming Media Platform
Viewing history in PostgreSQL → Viewing history in Cassandra
Both scenarios define a migration step at this stage. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'. Streaming Media Platform: triggered by 'PostgreSQL viewing history table exceeding 500M rows; write '.
Migration Step 3
Social Feed Platform
Single Redis primary for all feed data → Redis Cluster with feed keys sharded by user_id range
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. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'. 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 · 15 items
Coverage Warnings
- ⚠Social Feed 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.
- ·6 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.
Social Feed Platform is the recommended starting point over Streaming Media Platform
Social Feed Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Scalability. Weighted score: 5.5 vs 2.0 for Streaming Media Platform.
Decision Intelligence
Architecture Decision Path
Structured reasoning for choosing between Social Feed Platform and Streaming Media Platform. Every condition and trigger traces back to comparison dimensions, advisor insights, and topology evidence.
Social Feed Platform is the recommended starting point over Streaming Media Platform
Social Feed Platform leads on 3 weighted dimension(s): Complexity, Operational Risk, Scalability. Weighted score: 5.5 vs 2.0 for Streaming Media Platform. The architectures share 9 component(s), reducing migration cost if you switch later. Social Feed Platform is the operationally simpler choice.
Where to Start
Start with Social Feed Platform
LeftSocial Feed 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, 18 nodes, 0 edges, 5 risks, 4 simulation seeds
Migrate when:
- Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal → Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric.
- Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online → Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance
- PostgreSQL replica lag > 10s during peak fan-out periods; follower list queries appearing in pg_stat_activity with wait_event = Lock; read replica CPU > 70%; fan-out worker follower fetch latency rising; incorrect fan-out events (missing recent followers) appearing in feed correctness monitoring → Materialize hot follower lists in Redis (TTL 60s) to absorb fan-out worker read volume; route all fan-out follower reads through Redis cache-aside before touching PostgreSQL replica; add a dedicated read replica for fan-out worker social graph reads, isolated from timeline API read replicas
Decision Flow
Does your team have the operational maturity to run Social Feed Platform (advanced rating)?
If Yes
Your team can operate Social Feed 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 Social Feed 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: Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal ?
If Yes
Left 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.
Is operational simplicity (fewer moving parts, easier debugging, lower ops burden) more important than maximum architectural capability?
If Yes
Social Feed Platform is the simpler choice: Social Feed Platform is simpler: high operational complexity with 18 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
Social Feed Platform
LeftWhen operational simplicity is a top priority
HighSocial Feed Platform has lower operational complexity: fewer moving parts, easier to reason about and debug.
When stability and predictability matter most
CriticalSocial Feed Platform carries lower overall risk weight per the advisor's assessment.
When you need well-defined scaling thresholds and migration paths
HighSocial Feed Platform has more documented scaling evolution steps (4 thresholds, 3 migration paths).
When your system requires decoupled async event processing
HighSocial Feed Platform includes event stream infrastructure (e.g., Kafka/Kinesis), enabling async decoupling between producers and consumers.
Streaming Media Platform
RightWhen you want to minimise monitoring setup overhead
ModerateStreaming Media Platform has a lower observability burden: fewer watched metrics and monitoring targets.
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
Social Feed 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: 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 is early-stage or solo
HighSocial Feed Platform is rated 'advanced'. It requires experienced backend engineers or platform tooling to operate reliably at scale.
When you expect rapid growth within the next 12–18 months
ModerateThe advisor identifies 7 predicted bottlenecks for Social Feed 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
LeftSocial Feed 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)
LeftSocial Feed 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. Social Feed Platform: triggered by 'PostgreSQL timeline read query p99 > 500ms; query plan for "'. 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. Social Feed Platform: triggered by 'Fan-out worker queue lag during celebrity post events > 5 mi'. 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. Social Feed Platform: triggered by 'Redis memory utilization approaching 80% of a single node; R'. Streaming Media Platform: triggered by 'CDN provider incident causing total origin failover; CDN mis'.
Kafka consumer group lag for fan-out worker group growing steadily; feed propagation latency (time from post write to follower feed update) exceeding 30s p95; Redis write rate on feed keys elevated but not saturated; post activity rate normal
Tier 1: Fan-Out Worker Queue Backlog: Fan-out worker pool undersized for burst post activity or a celebrity post creating a sustained high-fan-out event. Recommended evolution: Add fan-out worker replicas; implement fan-out cost routing: route high-follower-count fan-out events to a dedicated high-cost worker pool with separate Kafka consumer group and Redis write quota; use follower count threshold (e.g., >50k followers) as the routing decision. Monitor fan-out cost per post as a first-class metric. .
Redis memory utilization > 75%; eviction rate rising; cache miss rate on feed reads increasing; cold feed read fallback queries appearing in PostgreSQL slow query log; feed read p99 > 100ms despite Redis being online
Tier 2: Redis Feed Memory Ceiling: Redis feed list storage approaching memory limit; feed items being evicted before TTL; or feed list cap set too high for available memory. Recommended evolution: Reduce feed list cap from current value toward 100–150 items; increase Redis cluster capacity or shard feed keys by user_id range across multiple Redis primaries; implement tiered feed storage: hot recent items in Redis, older items fetched from PostgreSQL on demand with explicit product UX affordance .
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
Both4 high-severity risks identified. Each requires a documented runbook, alerting threshold, and on-call response procedure before running in production.
RabbitMQ: scenario has high_throughput_writes exceeding 50k messages/second
LeftRabbitMQ 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
LeftRabbitMQ deletes acknowledged messages: use Kafka for replay-capable event streaming
Required maturity: mid_level
RabbitMQ: scenario uses classic mirrored queues for HA
LeftMigrate to quorum queues: classic mirrored queues have known split-brain behavior under network partition
Required maturity: mid_level
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
Social Feed Platform
LeftGenerator relevance documented but not yet production-ready.
For social product briefs with user-follows-user semantics, the generator must produce the hybrid fan-out composition: outbox → Kafka → fan-out worker → Redis feed list. The generator must include the follower count routing threshold as a first-class configuration parameter, and must generate the feed merge logic for high-follower-count accounts at read time. Redis feed list schema (LPUSH + LTRIM pattern with feed cap) must be generated with explicit cap configuration and PostgreSQL fallback handling.
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_social_feed_platform_vs_streaming_media_platform | Full comparison of Social Feed Platform vs Streaming Media Platform: 6 dimensions, 9 shared components, 3 shared risks. |
| Advisor | advisor_social_feed_platform | Advisor for Social Feed Platform: 0 strengths, 5 risks, maturity: advanced. |
| Advisor | advisor_streaming_media_platform | Advisor for Streaming Media Platform: 0 strengths, 6 risks, maturity: advanced. |
| Scenario | social_feed_platform | Scenario 'Social Feed 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_architecture_pattern_fan_out_on_write_risk_fanout_amplification | Fan-Out on Write → Fanout Amplification |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | Redis → Thundering Herd (Cache Stampede) |
| 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_architecture_pattern_fan_out_on_write_risk_fanout_amplification | Referenced by the operational risk comparison dimension. |
| Risk Path | prop_technology_profile_redis_risk_thundering_herd | 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.