database
70 articles on database — what drives the cost, how it is priced, and where the savings actually are.
Azure SQL serverless cost: when auto-pause beats provisioned
Azure SQL Database serverless bills compute per second and pauses when idle, which can slash the bill for intermittent workloads but cost more for steady ones. Here is where serverless wins and where provisioned vCore is cheaper.
Azure SQL elastic pools cost: sharing capacity across databases
Azure SQL elastic pools let many databases share a single pool of DTUs or vCores, which is far cheaper than provisioning each database on its own when their peaks do not coincide. Here is how the math works.
Azure Cosmos DB autoscale cost: when the 50% premium pays off
Cosmos DB autoscale raises the per-RU/s rate by 50 percent but scales throughput automatically between 10 percent and 100 percent of a maximum. For variable traffic it can be cheaper than standard provisioned; for flat traffic it is a needless premium. Here is the math.
Self-hosted database on EC2 vs RDS cost: is running your own worth it?
Running Postgres or MySQL yourself on EC2 is cheaper per hour than RDS, but RDS handles backups, failover, and patching. The premium is real, and so is the operational burden it removes. Here is when self-hosting a database pays off, with numbers.
Serverless database vs provisioned cost: pay per use or reserve capacity?
Serverless databases scale capacity automatically and bill per use, costing nothing when idle; provisioned databases reserve fixed capacity at a flat rate. The crossover is utilization, and it decides which is cheaper. Here is the math with numbers.
DynamoDB on-demand vs provisioned: which costs less
DynamoDB bills two ways: on-demand (pay per request, zero planning) and provisioned (pay for reserved capacity, cheaper at steady load). The right choice hinges on how predictable and spiky your traffic is. Here is how to decide.
EBS io2 vs gp3: paying for guaranteed IOPS
io2 and io2 Block Express are the high-end EBS volume types built for demanding databases, with high durability and very high provisioned IOPS. gp3 covers most needs far more cheaply. Here is when io2's premium is worth it.
ElastiCache node sizing: paying for the right amount of memory
ElastiCache (Redis and Memcached) is priced per node by instance type, so node sizing and count drive the bill. Oversized memory, too many replicas, and the wrong node family are the common overspends. Here is how to size for cost.
RDS Multi-AZ cost explained: paying for high availability
RDS Multi-AZ roughly doubles instance and storage cost by running a standby in a second Availability Zone for automatic failover. Here is what you actually pay for, and when the availability is worth the premium.
RDS read replica cost: scaling reads and what it adds
Each RDS read replica is a full additional instance with its own compute and storage cost, plus data transfer for cross-region replicas. Here is how read replica pricing works and how to scale reads without overpaying.
RDS vs Aurora cost: which managed database is cheaper
Standard RDS bills for provisioned instances and storage; Aurora bills for compute plus storage and I/O that scale with usage, and offers a serverless option. Which is cheaper depends on your scale, I/O pattern, and how many replicas you run. Here is the comparison.
Right-size RDS instances guide: databases without the bloat
RDS instances are often provisioned larger than the workload needs, and unlike stateless compute, resizing a database takes care. Here is how to right-size RDS instance class, storage, and IOPS to real usage without risking the database.
Aurora Serverless v2 cost explained: ACUs and when it pays off
Aurora Serverless v2 scales database capacity in fine-grained Aurora Capacity Units and bills per ACU-hour. It can save money on variable workloads but cost more on steady ones. Here is how the pricing works and when to use it.
The cost of running Redis in the cloud
Managed Redis (ElastiCache, Memorystore, Azure Cache) bills mostly for the node size and count you run, per hour, plus data transfer and optional replicas. Here is how Redis pricing works and how to size it without overpaying.
Database connection pooling cost impact: smaller databases, lower bills
Connection pooling lets a database serve more clients with fewer resources, so you can run a smaller, cheaper instance. The cost impact comes from avoiding over-provisioning driven by connection limits. Here is how pooling saves money.
DocumentDB cost explained: what you pay for a managed document database
Amazon DocumentDB bills for instance hours, storage consumed, I/O operations, and backups, with a separate compute-and-storage model. Understanding each meter, and how I/O can dominate, is key to controlling DocumentDB cost. Here is the breakdown.
DynamoDB capacity planning cost: on-demand vs provisioned
DynamoDB cost hinges on capacity mode: on-demand bills per request with no planning, provisioned bills for reserved throughput you must size. Picking the right mode, and sizing provisioned capacity, is the core cost decision. Here is how.
Database indexing: the cost-performance trade you control with SQL
The right index turns a full scan into a fast lookup, cutting both query latency and the compute or I/O you pay for. But every index adds write overhead and storage. Here is how indexing trades cost against performance and how to get it right.
Right-sizing databases: matching the instance to the load
Databases are often the biggest single line on the bill and the most over-provisioned, sized for a peak that rarely comes or copied from another environment. Right-sizing to real CPU, memory, and IO need cuts cost without hurting performance. Here is how.
Vector database cost: what powers similarity search
Vector database cost is storage of embeddings plus query compute, whether managed (Pinecone, managed pgvector) or self-hosted. Dimension count, vector count, and query volume drive the bill. Here is the model.
Database partitioning: cutting query cost by scanning less
Partitioning splits a large table into segments by a key (often date), so queries with a matching filter scan only relevant partitions instead of the whole table. On per-scan and IO-billed systems, that directly cuts cost. Here is the tradeoff.
Database sharding: the cost of scaling writes beyond one node
When a single database cannot handle the write load, sharding splits data across nodes to scale horizontally. It adds infrastructure and significant operational complexity, so it is a last resort after cheaper scaling. Here is the cost tradeoff.
Materialized views: trading storage and refresh for query savings
A materialized view precomputes an expensive query and stores the result, so reads are fast and cheap, at the cost of storage and refresh compute. For frequently-run expensive queries, the trade pays off. Here is the cost math.
NoSQL database cost compared: DynamoDB vs Firestore vs Cosmos DB
DynamoDB, Firestore, and Cosmos DB price NoSQL differently, DynamoDB per capacity or request, Firestore per operation, Cosmos per request unit or serverless. The cheapest depends on read/write mix and traffic shape. This compares the models.