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name	description	risk	reports-to
tech-matrix	Reference document for monopoly tech-matrix.	safe	monopoly

MONOPOLY — Technology Decision Matrix

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	Complex JOINs (foreign keys removed by design)	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	Financial transactions requiring ACID	Petabyte-scale with sharding
DynamoDB	Key-value at massive scale, AWS-native, serverless, predictable latency	Complex queries, ad-hoc analytics, JOINs	Unlimited (AWS-managed)
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	Yes (Redis Cluster, Redis Sentinel)
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)	Up to 7 days
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 < 5 minutes guaranteed delivery, AWS-native, zero ops?
  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

Rule of thumb: Use PostgreSQL FTS under 1M documents. Move to Typesense or Elasticsearch above that.

6. Object Storage

Service	Best For	Egress Cost
AWS S3	AWS-native apps, de facto standard, massive ecosystem	$0.09/GB (expensive)
Cloudflare R2	S3-compatible, zero egress cost, global	$0.00 egress
GCS	GCP-native	$0.12/GB
Azure Blob	Azure-native	$0.087/GB
Backblaze B2	Cost-sensitive, S3-compatible	Free with Cloudflare
MinIO	Self-hosted S3-compatible	Self-managed

Cost optimization tip: Use Cloudflare R2 for user-facing media delivery (zero egress). Use S3 for internal/AWS-integrated storage.

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 default: EKS or GKE once team > 5 engineers or services > 10.

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).

Scale Benchmarks Quick Reference

Technology	Write Throughput	Read Throughput	Notes
PostgreSQL (single)	~10K writes/s	~50K reads/s	With connection pooling
PostgreSQL (replicas)	~10K writes/s	~200K reads/s	4 replicas
MySQL (single)	~15K writes/s	~60K reads/s
Cassandra	~1M writes/s	~500K reads/s	10-node cluster
Redis	~1M ops/s	~1M ops/s	Single node in-memory
Kafka	~1M msgs/s	~1M msgs/s	Per partition
Elasticsearch	~50K docs/s	~10K queries/s	Per node
MongoDB	~50K writes/s	~100K reads/s	Per replica set

All benchmarks are approximate and depend heavily on hardware, payload size, and query complexity.

Limitations

This is a reference document and may not cover all edge cases. Always verify architectures before production.

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