MONOPOLY — Technology Decision Matrix
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 |
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.
