Imported from sickn33/agentic-awesome-skills (skills/monopoly/tech-matrix/SKILL.md). Install upstream with npx skills add sickn33/agentic-awesome-skills --skill tech-matrix. Copyright stays with the author.
MONOPOLY — Technology Decision Matrix
When to Use
- Use this skill when the task matches this description: Reference document for monopoly tech-matrix.
Use this as a qualitative candidate list. Verify exact editions, versions, service quotas and pricing with current primary docs; fixed throughput, memory, storage, edge counts and prices are not established capacity evidence. Decide from measured workload, operations and failure tests rather than user/team counts.
Table of Contents
- Database Selection
- Cache Selection
- Message Queue / Event Streaming
- API Protocol
- Search Engine
- Object Storage
- Container Orchestration
- Load Balancer
- Observability Stack
- CDN
1. Database Selection
Relational (SQL)
| Database |
Best For |
Avoid When |
Scale Ceiling |
| PostgreSQL |
Complex queries, JSONB, GIS, strong consistency, most default use cases |
Ultra-high write throughput (>100K writes/s) |
~10TB single node; use Citus for horizontal |
| MySQL / MariaDB |
Read-heavy apps, legacy systems, WordPress/Drupal ecosystem |
Complex queries, full ACID at scale |
~10TB; use Vitess for sharding |
| CockroachDB |
Global distributed SQL, geo-partitioning, multi-region |
Simple single-region apps (overkill) |
Petabyte-scale |
| PlanetScale |
MySQL-compatible, serverless, branch-based workflow |
Check current product/engine support for required joins and foreign keys |
Very high — Vitess based |
| Amazon Aurora |
AWS-native apps, managed PostgreSQL/MySQL, high availability |
Non-AWS environments |
Up to 128TB, 15 replicas |
NoSQL
| Database |
Best For |
Avoid When |
Scale Ceiling |
| MongoDB |
Flexible schema, document model, prototyping |
Requires validating transaction/isolation and distributed-operation costs; MongoDB supports transactions |
Petabyte-scale with sharding |
| DynamoDB |
Key-value at massive scale, AWS-native, serverless, predictable latency |
Complex queries, ad-hoc analytics, JOINs |
Subject to account/table/partition quotas |
| Cassandra |
Write-heavy, time-series, wide-column, geographically distributed |
Read-heavy with complex queries |
Petabyte-scale; used at Apple, Netflix |
| Redis |
Cache, sessions, leaderboards, pub/sub, rate limiting |
Primary data store for complex models |
~1TB per node; cluster for more |
| Elasticsearch |
Full-text search, log aggregation, analytics |
Primary database (durability risk) |
Petabyte-scale with clusters |
| InfluxDB |
Time-series metrics, IoT, monitoring data |
General-purpose data |
Very high write throughput |
| Neo4j |
Graph data, social networks, recommendation engines, fraud detection |
Non-graph data (overhead not worth it) |
Billions of nodes |
Decision Framework
Is your data relational (joins, foreign keys, transactions)?
YES → Start with PostgreSQL
NO → Continue below
Is your primary access pattern key-value?
YES, need extreme scale → DynamoDB or Cassandra
YES, need speed/cache → Redis
Is your data document-shaped (nested, flexible schema)?
YES → MongoDB
Is it time-series (metrics, logs, IoT)?
YES → InfluxDB or TimescaleDB
Is it graph (relationships are the data)?
YES → Neo4j
Is it search?
YES → Elasticsearch / OpenSearch
2. Cache Selection
| Technology |
Best For |
Max Single Node |
Cluster Support |
| Redis |
Sessions, leaderboards, pub/sub, complex data structures, Lua scripting |
~1TB RAM |
Cluster sharding; Sentinel provides non-cluster failover |
| Memcached |
Simple key-value, multi-threaded, large object cache |
~64GB RAM |
Yes (client-side sharding) |
| Varnish |
HTTP reverse proxy cache, full-page caching |
RAM bound |
Limited |
| CloudFront / CDN |
Static assets, edge caching globally |
N/A (distributed) |
Built-in global distribution |
Default recommendation: Redis — more features, better ecosystem, active development.
Use Memcached only when: you need multi-threading for CPU-bound caching workloads and don't need data structures beyond string.
3. Message Queue / Event Streaming
| Technology |
Model |
Best For |
Throughput |
Retention |
| Apache Kafka |
Log-based streaming |
Event sourcing, high-throughput pipelines, replay, audit |
Millions msg/s |
Days to forever |
| RabbitMQ |
AMQP message broker |
Task queues, RPC, routing, fanout |
50K–100K msg/s |
Until consumed |
| AWS SQS |
Managed queue |
AWS-native, simple task queue, serverless |
Very high (managed) |
Up to 14 days |
| AWS SNS |
Pub/sub notification |
Fan-out to many subscribers (email, SMS, Lambda, SQS) |
Very high (managed) |
No retention |
| Google Pub/Sub |
Managed streaming |
GCP-native, global, serverless |
Very high (managed) |
Configurable; verify current topic/subscription retention limits |
| Redis Pub/Sub |
In-memory pub/sub |
Real-time notifications, low latency, fire-and-forget |
Very high |
None (no retention) |
| NATS |
Lightweight messaging |
IoT, microservices, low latency |
Very high |
JetStream adds retention |
Decision Matrix
Need event replay / audit trail?
YES → Kafka or Kinesis
Need simple task queue with retries and DLQ?
AWS shop → SQS
Self-hosted → RabbitMQ
Need real-time pub/sub with no persistence?
Redis Pub/Sub or NATS
Need fan-out to multiple consumers?
Kafka (consumer groups) or SNS → SQS fan-out
Need a managed AWS-native task queue with explicit retry/latency objectives?
SQS
Volume > 1 million messages/second?
Kafka (self-hosted) or Kinesis (managed)
4. API Protocol
| Protocol |
Best For |
Avoid When |
| REST (HTTP/JSON) |
Public APIs, CRUD, browser clients, simplicity |
Strict typing required; high-performance internal services |
| GraphQL |
Complex client data requirements, mobile (reduce over-fetching), BFF pattern |
Simple CRUD; not worth the complexity |
| gRPC (HTTP/2 + Protobuf) |
Internal microservice communication, low latency, strict contracts, streaming |
Public browser APIs (needs gRPC-web) |
| WebSocket |
Real-time bidirectional (chat, live dashboards, multiplayer games) |
One-way server push (use SSE instead) |
| SSE (Server-Sent Events) |
Server → client push (notifications, live feeds) |
Bidirectional communication |
| GraphQL Subscriptions |
Real-time with GraphQL schema consistency |
Simple push scenarios |
Default recommendation:
- External / public: REST
- Internal service-to-service: gRPC
- Real-time features: WebSocket or SSE
5. Search Engine
| Technology |
Best For |
Avoid When |
| Elasticsearch |
Full-text search, log analytics (ELK), complex aggregations |
Simple lookups; operational overhead is high |
| OpenSearch |
AWS-native Elasticsearch alternative |
Non-AWS preferred setups |
| Typesense |
Simple, fast full-text search, typo tolerance, easy ops |
Complex aggregations at massive scale |
| Algolia |
Managed search-as-a-service, fast setup, great UI |
High volume (expensive); self-hosted preference |
| Meilisearch |
Self-hosted, developer-friendly, fast relevancy |
Enterprise-scale analytics |
| PostgreSQL FTS |
Basic full-text search, already using PostgreSQL |
High relevancy requirements or large datasets |
Selection: Benchmark relevance, query mix, indexes, language support, data size and update latency. Document count alone does not determine when to leave PostgreSQL FTS.
6. Object Storage
Compare AWS S3, Cloudflare R2, GCS, Azure Blob, Backblaze B2 and self-hosted S3-compatible systems against region/residency, consistency/API compatibility, operations, availability, replication and object lifecycle needs. Build a dated cost model including storage, requests, retrieval, inter-region transfer, internet egress and connected compute. Zero egress for one service does not make the full delivery path free. Use current calculators; this reference supplies no price quote.
7. Container Orchestration
| Technology |
Best For |
Avoid When |
| Kubernetes (K8s) |
Large teams, complex deployments, multi-cloud, full control |
Small teams (ops overhead is very high) |
| AWS ECS + Fargate |
AWS-native, serverless containers, simpler than K8s |
Multi-cloud or K8s ecosystem tools needed |
| AWS EKS |
Managed K8s on AWS, best of both |
Small teams; Fargate may be enough |
| GKE (Google) |
Best managed K8s, GCP-native, Autopilot mode |
Non-GCP environments |
| Docker Compose |
Local dev, small single-server deployments |
Production at any meaningful scale |
| Nomad |
HashiCorp ecosystem, simpler than K8s, multi-workload |
K8s ecosystem tools required |
Startup default: ECS + Fargate (zero cluster management).
Scale decision: Choose orchestration from operational requirements and team capacity, not fixed engineer/service counts.
8. Load Balancer
| Technology |
Layer |
Best For |
| AWS ALB |
L7 (HTTP/HTTPS) |
AWS apps, path-based routing, WebSocket, HTTP/2 |
| AWS NLB |
L4 (TCP/UDP) |
Ultra-low latency, static IP, non-HTTP protocols |
| GCP GLB |
L7 global |
GCP apps, global anycast, single IP worldwide |
| Nginx |
L4/L7 |
Self-hosted, reverse proxy, flexible config |
| HAProxy |
L4/L7 |
High performance self-hosted, advanced routing |
| Cloudflare |
L7 global + DDoS |
DDoS protection + CDN + load balancing combined |
| Traefik |
L7 |
Kubernetes-native, automatic SSL, service discovery |
9. Observability Stack
Metrics
| Tool |
Best For |
| Prometheus + Grafana |
Self-hosted, open-source, Kubernetes-native |
| Datadog |
Managed, APM + infra + logs unified, expensive |
| CloudWatch |
AWS-native, zero setup, integrated with AWS services |
| New Relic |
APM-focused, good for application-level insights |
Logging
| Tool |
Best For |
| ELK Stack (Elasticsearch + Logstash + Kibana) |
Self-hosted, powerful, high volume |
| Loki + Grafana |
Lightweight, Kubernetes-native, cheap |
| Splunk |
Enterprise, compliance, expensive |
| AWS CloudWatch Logs |
AWS-native, zero setup |
| Datadog Logs |
Unified with metrics, expensive |
Distributed Tracing
| Tool |
Best For |
| Jaeger |
Open-source, Kubernetes-native, OpenTelemetry |
| Zipkin |
Simple, lightweight, good integrations |
| AWS X-Ray |
AWS-native, integrates with Lambda, ECS |
| Datadog APM |
Managed, unified with metrics and logs |
| Honeycomb |
High-cardinality event-based observability |
Recommended open-source stack: Prometheus + Grafana + Loki + Jaeger (all integrate via OpenTelemetry)
Recommended managed stack: Datadog (expensive but unified) or Grafana Cloud
10. CDN
| Technology |
Best For |
Edge Locations |
| Cloudflare |
DDoS protection + CDN + DNS, best free tier, edge workers |
300+ |
| AWS CloudFront |
AWS-native, deep S3 and API GW integration |
450+ |
| Akamai |
Enterprise, highest performance, expensive |
4000+ |
| Fastly |
Real-time purging, streaming, VCL customization |
90+ |
| Vercel Edge / Netlify |
Jamstack, frontend-first, zero config |
100+ |
Default recommendation: Cloudflare for most use cases (best value, DDoS included, free SSL, Workers for edge compute).
Capacity evidence
Use the bundled capacity planning reference at skills/monopoly/scale-benchmarks/SKILL.md to define workload measurements. No fixed product throughput benchmark is established here.
Limitations
- This is a reference document and may not cover all edge cases. Always verify architectures before production.
