59 articles on architecture — what drives the cost, how it is priced, and where the savings actually are.
A monolith runs as one deployable with shared infrastructure; microservices split it into many services, each with its own baseline overhead. That per-service overhead multiplies, and it is the biggest infrastructure cost difference. Here is the comparison with numbers.
The classic three-tier architecture, web, application, and database, has a predictable cost shape once you know which tier dominates. Here is a breakdown of where spend concentrates and how to estimate it from Terraform.
Event-driven architectures trade steady servers for pay-per-event components like queues, functions, and streams, which changes the cost shape entirely. Here is where event-driven spend concentrates and how to estimate it.
A data pipeline spans ingestion, processing, and storage, and cost can concentrate in any stage depending on data volume and processing intensity. Here is how to break down pipeline cost and estimate it from Terraform.
A typical SaaS backend on AWS combines compute, a database, load balancing, storage, caching, and networking. An early-stage backend can run a few hundred dollars a month, scaling with users. Here is how the pieces add up and where to save.
Region choice affects compute price, data transfer, latency, and compliance all at once. The cheapest region is not always the right one. Here is a framework for choosing a region that minimizes total cost without hurting users.
Lift and shift moves workloads to the cloud as-is for speed, but often carries over-provisioned cost; refactoring for cloud-native services costs more up front but runs cheaper. Here is how to weigh the two.
Running in multiple regions cuts latency for distributed users and adds resilience, but it multiplies compute, replicates data, and adds cross-region transfer. Knowing when the latency and availability are worth the multiplied cost is the decision. Here is the framework.
Connecting workloads across clouds means egress on both sides plus interconnect or VPN cost, so data moving between clouds is billed twice and adds latency. Minimizing cross-cloud data movement is the main lever. Here is how.
Bandwidth (data transfer) cost spans egress, cross-region, cross-AZ, and CDN transfer, and it hides across many services. Compression, caching, CDNs, in-region traffic, and committed transfer pricing cut it. Here is a complete playbook.
Network topology, hub-and-spoke, mesh, flat, or segmented, determines data transfer paths and connectivity cost. A well-designed topology keeps traffic cheap and manageable; a poor one multiplies transfer and complexity. Here is how to design for cost.
A data pipeline's cost is the compute that ingests and transforms data, the storage it lands in, and the data transfer between stages. Processing only what changed, right-sizing compute, and efficient formats cut it. Here is how.
ETL transforms data on separate compute before loading it; ELT loads raw data into the warehouse and transforms there using warehouse compute. Which is cheaper depends on your warehouse pricing and transformation load. Here is the tradeoff.
Streaming processes data continuously for low-latency freshness, keeping compute always running; batch processes in scheduled bulk runs on transient compute. When data does not need to be fresh instantly, batch is far cheaper. Here is the tradeoff.
Cloud HPC trades a capital cluster purchase for pay-as-you-go compute, so cost is driven by node type, job runtime, and utilization. Spot, scale-to-zero, and efficient jobs make it economical. Here is how the cost works.
Spot instances (spare capacity at up to 90% off) can be reclaimed with little notice, so they suit fault-tolerant, interruptible, or checkpointed work. Diversifying pools and handling interruption make them safe. Here is the strategy.
Real-time inference keeps serving capacity always ready to respond instantly; batch inference processes predictions in bulk on transient compute. When predictions can wait, batch is far cheaper. Here is the tradeoff.
A RAG system's cost spans a vector database, embedding generation, the LLM API for generation, and the retrieval compute. Each scales differently, and the LLM and vector store usually dominate. Here is how to budget it.
Egress, data leaving a cloud to the internet or across regions, is billed per GB and is one of the most underestimated cloud costs. Architecture, caching, and keeping traffic in-region cut it. Here is how.
Running across regions buys resilience and lower latency for distributed users, but roughly multiplies infrastructure cost and adds cross-region data transfer. The tradeoff is availability and latency versus cost. Here is how to weigh it.
Polling for updates repeatedly hits your backend whether or not anything changed; WebSockets push updates over a persistent connection only when they occur. For frequent updates to many clients, the cost tradeoff is nuanced. Here it is.
Processing work synchronously means provisioning compute for peak request rate; processing it asynchronously via a queue lets workers drain at their own pace, so you provision for average, not peak. This decouples cost from spikes. Here is the tradeoff.
Rate limiting caps how many requests a client can make, protecting your backend from abuse, runaway clients, and traffic spikes that would drive up compute and downstream cost. It is a cheap control that prevents expensive incidents. Here is the case.
gRPC's binary encoding and HTTP/2 multiplexing send fewer bytes and use connections more efficiently than JSON over HTTP/1.1, which can cut egress and compute at high volume. But REST is simpler and universal. Here is the cost tradeoff.