PostgreSQL vs MySQL vs MongoDB vs Redis vs Cassandra - Enterprise Scaling Reality Check

So your startup hit 10 million users and that database you picked is now melting under load. Surprise! Database choice matters more than your entire tech stack. I've fixed database disasters at everything from 50-person startups to Fortune 100 companies, and every failure follows the same predictable patterns. Here's how these databases actually behave when you scale them in the real world.

PostgreSQL 17.6: The Enterprise Workhorse That Demands Respect

Current Status: PostgreSQL 17.6 released August 14, 2025, with significant parallel query improvements that boost analytical performance by 40% over version 16.

PostgreSQL is what happens when database engineers build something that actually works for complex enterprise workloads. The EDB Postgres AI benchmarks from February 2025 show it consistently outperforming Oracle, SQL Server, MongoDB, and MySQL across transactional, analytical, and AI workloads. But this power comes with operational complexity that will bite you if you're not prepared.

What Actually Scales: PostgreSQL handles TPC-H benchmark queries that would make other databases cry. Complex analytical queries with window functions, CTEs, and multi-table JOINs perform exceptionally well. The TimescaleDB extension delivers 9,000x faster time-series ingestion when configured properly.

Where It Breaks: Connection limits will destroy your scaling plans. Each PostgreSQL connection eats 4-8MB of RAM and the default limit of 100 concurrent connections is a fucking joke. You'll hit walls with traffic that wouldn't stress a basic load test. PgBouncer connection pooling becomes mandatory, but configure it wrong and prepared statements randomly break in ways that'll consume your entire weekend. I learned this debugging production outages at 2am.

Real Enterprise Pain: Our PostgreSQL 17.5 to 17.6 upgrade failed catastrophically when the new parallel query features spiked memory usage beyond our allocated 32GB. Started seeing FATAL: out of memory errors at 9:15 AM every morning during financial reporting. Complex analytical queries that used to consume 2GB per worker suddenly needed 8GB, causing OOM kills during peak hours. The memory spike hit specifically with SELECT ... OVER (PARTITION BY date_trunc('month', created_at)) patterns that our reporting system was built around. Took 3 days to figure out the new parallel hash joins were the culprit.

MySQL 8.4.6 LTS: The Boring Choice That Keeps Working

Current Status: MySQL 8.4.6 LTS released July 2025 with 8-year support commitment until 2032, making it the most stable long-term option for enterprises that prioritize predictability over cutting-edge features.

MySQL's superpower is that it doesn't surprise you. After 25 years of battle-testing, it handles predictable enterprise workloads with boring reliability. The latest performance optimizations deliver 15-25% OLTP improvements while maintaining the operational simplicity that lets you sleep at night.

What Actually Scales: Horizontal read scaling through read replicas is battle-tested and well-understood. MySQL's InnoDB storage engine handles 100,000+ simple queries per second on decent hardware. The replication lag that killed Facebook's early scaling efforts has been largely solved through parallel replication improvements.

Where It Breaks: Complex analytical queries make MySQL give up. The query optimizer from the 1990s chokes on multi-table JOINs and subqueries that PostgreSQL handles gracefully. Binary log management becomes a nightmare at scale—I've seen MySQL instances crash because binary logs consumed all disk space during high-traffic periods.

Real Enterprise Pain: During Black Friday, MySQL 8.4's Enterprise Firewall started throwing ER_FIREWALL_ACCESS_DENIED errors and added 300ms latency to every product lookup. The firewall was in "learning mode" and suddenly decided our standard SELECT * FROM products WHERE category_id = ? queries looked suspicious. Started getting alerts around 6 AM EST with 50% of product page loads failing with Error 1045 (28000): Access denied for user 'app_user'@'10.0.1.23'. Had to disable enterprise security during peak sales because customers couldn't buy anything. Spent 4 hours digging through MySQL error logs before realizing the firewall was blocking legitimate queries.

MongoDB 8.0.9: Fast Development, Expensive Operations

Current Status: MongoDB 8.0.9 (May 2025) delivers 32% faster reads and 59% faster updates compared to version 7.0, with significant improvements to time-series workloads showing 200%+ performance gains.

MongoDB promises rapid development velocity by letting developers dump JSON objects into the database without thinking about schema design. This works great until you need to scale beyond a single replica set and discover that "schemaless" doesn't mean "schema-free"—it means "every document has a different broken schema."

What Actually Scales: MongoDB's horizontal sharding actually works when designed properly. The automatic balancer can distribute data across dozens of shards while maintaining query performance. Time-series collections with the improved bulk write operations handle IoT and logging workloads exceptionally well.

Where It Breaks: Shard key selection is a one-way decision that will haunt you forever. Choose poorly and you'll have hot shards that handle 90% of traffic while other shards idle. MongoDB's balancer will randomly decide to move chunks during peak traffic, causing 5-10 second query timeouts while data migrates between shards.

Real Enterprise Pain: Our e-commerce client's MongoDB cluster went into election mode every morning at 9 AM PST when East Coast traffic hit. Started getting MongoTimeoutError: No replica set members found at errors in the application logs. The balancer was moving product catalog chunks between shards during peak hours, causing 15-30 second timeouts. Customers couldn't view iPhones because the shard with product_id hashes 4000000-6000000 was temporarily unreachable during chunk migration. The whole thing happened because we chose product_id as the shard key instead of something that distributed load evenly like category_id + created_date.

Redis: The Speed Demon That Devours Memory

Current Status: Redis 7.2 with enhanced multi-threading and improved memory efficiency, but still fundamentally limited by single-threaded command processing and RAM consumption that scales linearly with dataset size.

Redis is pure speed. Sub-millisecond response times, 100,000+ operations per second, and data structures that make complex operations trivial. It's also a memory-hungry beast that will consume every byte of available RAM and then crash when you try to add one more key.

What Actually Scales: Redis Cluster provides horizontal scaling across multiple nodes with automatic failover. The newest versions handle billions of operations per day when configured properly. Redis Streams excel at high-throughput messaging and event processing.

Where It Breaks: Memory management becomes your full-time job. Every key consumes RAM and Redis will never release it back to the OS. Memory fragmentation can cause a 32GB Redis instance to crash even when only using 16GB of actual data because of how Redis allocates memory internally.

Real Enterprise Pain: Our analytics Redis instance was configured with 64GB RAM but kept crashing with (error) OOM command not allowed when used memory > 'maxmemory' during batch processing. Memory fragmentation from millions of short-lived keys like analytics:user:12345:session:20250826 meant Redis couldn't allocate contiguous 8MB blocks even with only 38GB actually used. The INFO memory output showed used_memory_rss:61437816832 while used_memory:40802189312 - massive fragmentation. Only fix was nightly Redis restarts to defragment memory, which is hardly enterprise-ready.

Cassandra 5.0.5: Infinite Scale, Finite Patience

Current Status: Apache Cassandra 5.0.5 released August 2025 with Storage-Attached Indexes (SAI) that finally enable multi-column queries without requiring perfect data modeling expertise.

Cassandra is engineered for massive scale. Linear scaling, no single points of failure, designed to run across multiple data centers. It's also a distributed systems PhD program disguised as a database that will teach you patience through suffering.

What Actually Scales: Cassandra's peer-to-peer architecture handles millions of writes per second across thousands of nodes. The new SAI indexes in version 5.0 eliminate the need for perfectly designed partition keys for every query pattern. Global replication across data centers works reliably once properly configured.

Where It Breaks: Everything related to time synchronization, garbage collection, and compaction strategies requires deep expertise. Clock drift between nodes causes mysterious data inconsistencies. JVM garbage collection pauses can trigger cascading failures across the entire cluster.

Real Enterprise Pain: After a data center power outage, our Cassandra cluster took 72 hours to return to normal because tombstone cleanup was fighting with repair operations. Started seeing ReadTimeoutException: Operation timed out - received only 1 responses errors in application logs. nodetool repair was consuming all I/O bandwidth - watching iostat showed 100% disk utilization while customer queries were timing out. The repair was processing 2.1TB of tombstones from our user activity table because we'd been soft-deleting records for 18 months without proper TTL settings. Learned that running repairs during business hours isn't just slow - it makes your app unusable.

These aren't theoretical scaling patterns—they're the 3am emergencies that turn database choice from an architectural decision into a career-defining moment. Each database scales in predictable ways and fails in equally predictable patterns. Understanding these patterns before you're debugging production disasters at 3am will save your sanity, your sleep, and possibly your job.

The next time your CTO asks "Why can't we just use MongoDB for everything?" you'll have real answers backed by production battle scars. Because knowing how databases break under load isn't just technical knowledge—it's career insurance.

Here's what nobody tells you about database scaling: it's not the traffic that kills you, it's the exponential cost growth. Every database scales your AWS bill differently, demands different expertise levels, and fails in ways that cost money, sleep, and sanity. Anyway, here's what actually happens when you go from 10,000 to 10 million users.

The PostgreSQL Tax: Performance Comes with Premium Operational Costs

PostgreSQL's analytical performance is unmatched, but this power requires expertise that costs serious money. A senior PostgreSQL DBA who understands vacuum tuning, connection pooling, and query optimization commands $140,000-200,000 annually in major tech markets. That's 40% more than a MySQL DBA with equivalent experience.

Real Enterprise Numbers:

• AWS RDS PostgreSQL (16 vCPU, 64GB): $2,400/month base cost
• Connection pooling (PgBouncer setup): $8,000 in consultant time
• Monitoring setup (pganalyze): $500/month + setup time
• Performance tuning: 40-80 hours of expert time = $20,000-40,000
• Training existing team: $15,000-30,000 in PostgreSQL bootcamps

The PostgreSQL Scaling Tax: Each read replica costs another $2,400/month. Most enterprises need 3-5 read replicas for high availability, pushing monthly costs to $10,000+ before you handle serious traffic. The Citus distributed PostgreSQL extension can reduce replica needs but requires rewriting application logic.

Hidden Gotcha: PostgreSQL's autovacuum can randomly consume 100% of available I/O during business hours. I've seen enterprise apps slow to a crawl because autovacuum decided to clean a 500GB table during peak trading hours. Brought down prod for 2 hours while I frantically googled "kill postgres autovacuum without breaking everything" - spoiler alert, there's no clean way. The fix requires understanding vacuum scheduling, table partitioning, and I/O priority tuning. Good luck finding someone on your team who knows that shit without paying consultant rates.

MySQL's Operational Simplicity Saves Money

MySQL's biggest advantage isn't performance—it's predictable operational costs. Any senior backend engineer can manage MySQL in production without specialized training. The tooling ecosystem is mature, documentation is practical, and when things break, the solutions are well-documented on Stack Overflow.

Real Enterprise Numbers:

• AWS RDS MySQL (16 vCPU, 64GB): $2,200/month base cost
• Monitoring (built-in Performance Schema): $0 additional cost
• High availability (Multi-AZ): doubles hosting cost but includes automated failover
• Operations team training: 2-3 weeks instead of 2-3 months
• Consultant emergency rates: $150-200/hour vs $250-350/hour for PostgreSQL

The MySQL Scaling Strategy: Read replicas are cheap and reliable. Facebook scaled to billions of users with MySQL read replicas before building their own distributed database. Most enterprises never outgrow well-configured MySQL with proper read scaling.

Cost Reality Check: We migrated a client from PostgreSQL to MySQL and reduced their database operational costs by 60%. Same performance for their OLTP workload, but MySQL's operational simplicity meant their existing team could manage it without hiring PostgreSQL specialists.

MongoDB's Auto-Scaling Promise vs. Hidden Operational Complexity

MongoDB Atlas markets itself as "auto-scaling" but the operational complexity of sharding at enterprise scale often requires specialized MongoDB DBAs who command premium salaries. The MongoDB certification program exists because MongoDB operations expertise is rare and expensive.

Real Enterprise Numbers:

• MongoDB Atlas (M40, 16 vCPU): $3,200/month base cost
• Sharding overhead: 3x cost multiplier for proper shard distribution
• Atlas data transfer: $0.10/GB (gets expensive with replica sets)
• MongoDB expert consultant: $300-400/hour for sharding architecture
• Application rewrite costs: $50,000-200,000 to optimize for MongoDB

The Sharding Tax: MongoDB's balancer looks free until you discover it's constantly moving data between shards, consuming bandwidth and causing query latency spikes. Chunk migration during business hours can impact customer-facing applications. The fix requires understanding shard key design, chunk splitting strategies, and balancer scheduling—expertise that costs consultant rates.

MongoDB's Dirty Secret: The 16MB document limit seems reasonable until your application needs to store user data that exceeds this limit. I've seen teams spend $100,000+ redesigning their data model because they hit document size limits during user import operations.

Redis: The Memory Tax That Scales Linearly with Your Dataset

Redis is simple until you need to store more data than fits in a single server's RAM. Every GB of data costs you direct RAM hosting fees, and Redis Cluster introduces complexity that most teams underestimate.

Real Enterprise Numbers:

• ElastiCache Redis (r6g.2xlarge, 64GB): $1,800/month per node
• Redis Cluster (6 nodes for high availability): $10,800/month minimum
• Memory overhead (replication, fragmentation): 40-60% additional RAM needed
• Data persistence (RDB snapshots): doubles storage costs
• Operations complexity: Redis Cluster requires distributed systems expertise

The Memory Scaling Wall: Redis performance degrades when memory usage exceeds 70-80% capacity due to fragmentation and GC pressure. This means your 64GB Redis node effectively stores 40-50GB of data. The linear cost scaling of RAM makes Redis extremely expensive for large datasets.

Hidden Redis Costs: Memory fragmentation from billions of small keys can cause Redis to crash even when total memory usage appears safe. Redis memory optimization requires understanding data structure internals and memory allocation patterns—specialist knowledge that costs consulting rates.

Cassandra's Infrastructure Complexity Tax

Cassandra scales infinitely in theory but requires infrastructure expertise that costs serious money. The learning curve is so steep that most enterprises hire DataStax for consulting rather than training internal teams.

Real Enterprise Numbers:

• DataStax Astra (enterprise): $0.50-1.00 per million operations
• Self-managed Cassandra: 3x hardware costs for proper redundancy
• DataStax consultant: $400-500/hour for architecture design
• Operations team training: 6-12 months to become productive
• Monitoring setup (OpsCenter): $10,000-50,000 annually

The Cassandra Operations Tax: Running Cassandra yourself requires understanding JVM garbage collection, compaction strategies, repair operations, and distributed systems theory. Most enterprises discover that managed Cassandra (DataStax Astra) is cheaper than hiring the expertise needed to run it properly.

Cassandra's Time Cost: Nodetool repair operations can take days to complete on large clusters, during which query performance degrades significantly. I've seen enterprise applications schedule maintenance windows around Cassandra repair operations because the performance impact is unavoidable.

The Total Cost Reality Matrix

5-Year TCO Estimates for 100,000 Daily Active Users:

• PostgreSQL: $500,000-800,000 (high expertise costs)
• MySQL: $300,000-500,000 (operational efficiency)
• MongoDB Atlas: $600,000-1,200,000 (scaling costs + expertise)
• Redis: $400,000-800,000 (memory scaling limitations)
• Cassandra: $800,000-1,500,000 (operational complexity + consulting)

The Enterprise Reality: Database choice isn't about features listed in comparison charts—it's about which operational model your team can afford to master and maintain. The cheapest database on paper often becomes the most expensive when you factor in the human costs of scaling it properly.

CFO Translation: PostgreSQL delivers the best performance per dollar for analytical workloads but requires expensive expertise. MySQL offers the best operational cost predictability. MongoDB Atlas abstracts complexity but at significant cost premiums. Redis excels for caching but hits memory scaling walls quickly. Cassandra handles unlimited scale but requires dedicated platform teams.

The Enterprise Reality Check: Every database scales your AWS bill differently. PostgreSQL taxes you through operational complexity. MySQL keeps costs predictable until you need advanced features. MongoDB inflates costs through convenience premiums. Redis burns money linearly with dataset growth. Cassandra demands a small army of distributed systems experts.

The database that scales your application might not scale your budget or your team's capabilities. Choose the operational model you can afford to maintain, not the theoretical performance you can't afford to optimize.