Illustrating cost estimations in large-scale system designs

In large-scale system designs—particularly those involving cloud infrastructure—estimating and articulating costs is just as important as ensuring performance and reliability. Whether you’re discussing a solution in a system design interview or planning a real-world product, showing how resources scale and how expenses might be minimized is a valuable part of your solution. Below, we’ll outline why cost estimations matter, key factors to consider, and how to integrate cost discussions effectively.

1. Why Cost Estimations Matter

1. Business Constraints

   • Even if a design meets concurrency or latency requirements, overspending on infrastructure can make it unsustainable.
   • Teams (and interviewers) look for solutions that balance performance with budget reality.

2. Scalability & Growth

   • Predicting usage growth (e.g., doubling users or data yearly) means you can proactively account for how costs increase over time.
   • Early awareness prevents last-minute panics or expensive redesigns when usage spikes.

3. Trade-Off Decision-Making

   • In system design, you might consider a microservices approach or a single monolith. Microservices can scale more granularly but come with overhead.
   • Putting a price tag on these choices clarifies which approach is more cost-effective at different usage levels.

4. Impressing Interviewers

   • Mentioning cost considerations signals maturity and real-world perspective.
   • You’re not just building a “perfect” architecture in theory—you’re delivering a solution feasible under budget constraints.

2. Key Factors Influencing Cost

1. Compute & Instances

   • Type & Size of Instances: For example, AWS EC2 instance families (general-purpose vs. compute-optimized). Larger or specialized instances cost more.
   • Auto-Scaling: Might save on baseline costs if you scale down during low traffic, but can lead to spikes if scaling triggers frequently.

2. Storage

   • Database: Managed SQL (RDS) or NoSQL (DynamoDB, Cosmos DB) might charge per GB stored plus read/write costs.
   • Object Storage: S3 or Blob storage typically cost per GB and per request. Reducing data or using infrequent-access tiers can yield savings.

3. Data Transfer

   • Outbound data often has a charge, especially cross-region egress.
   • Using CDNs or caching reduces origin data transfers.

4. Networking & Load Balancers

   • Each load balancer or NAT gateway might impose hourly or data-processed fees.
   • Architectural decisions that route large data volumes through multiple layers can inflate network costs.

5. Third-Party Services

   • Queue systems (SQS, RabbitMQ) or streaming frameworks (Kinesis, Kafka) may charge for API calls, message volume, or retention.
   • Monitoring and logging (Datadog, Splunk) also accumulate costs based on data ingestion.

6. Over-Provisioning vs. Auto-Scaling

   • Some teams prefer static provisioning to maintain consistent performance. This can cause underutilization if the load isn’t always high.
   • Pay-as-you-go or serverless approaches (AWS Lambda, Azure Functions) might minimize idle costs but can rise quickly under high loads.

3. Practical Approach to Estimating Costs

1. Estimate Workload & Traffic

   • Start with daily or monthly active users and convert to requests per second (RPS) or data processed.
   • Example: “We handle ~500 RPS in normal load, potentially 2× during peak hours.”

2. Map to Resource Usage

   • If each request needs a certain CPU/memory footprint, approximate how many instances you need.
   • For storage, compute how many GB/TB are generated monthly (e.g., logs, user data).
   • For data transfer, approximate average outbound data per request or monthly totals.

3. Lookup Basic Cloud Pricing

   • You don’t need exact rates, but a rough sense: e.g., an m5.large in AWS might cost ~0.096/hour in us-east. S3 standard is ~0.023/GB/month.
   • Multiply your usage (like instance hours or stored GB) by unit costs for a ballpark.

4. Add Buffer & Summarize

   • Because usage can fluctuate, incorporate a margin (like 1.25× or 1.5× the expected usage).
   • Express results in monthly or annual terms: “We anticipate ~2k/month in compute, 300 in storage, plus ~$150 in data transfer fees.”

5. Compare Alternatives

   • If an alternative architecture halves data processing or memory usage, state the new cost difference.
   • Helps justify more complex solutions if the cost savings justify the overhead.

4. Example Cost Estimation in an Interview

Scenario: E-Commerce Checkout System

• Assumptions:

  • 10k RPS peak traffic, each request ~1kB data.
  • 100 GB total user data monthly.
  • Using AWS as an example.

• Compute:

  • Possibly 10–15 medium-size EC2 instances behind a load balancer at peak. If each instance costs ~50/month, that’s 750 monthly for main compute.
  • Auto-scaling might scale down to ~5 instances in low traffic for 250 monthly. So total ~500 monthly average.

• Storage:

  • A relational DB storing order records (RDS with ~100GB usage). Base cost might be ~0.10/GB-month plus DB instance cost. Suppose the DB instance cost is 200/month, plus 10 for storage = 210 monthly.
  • S3 for logs or backups: ~200GB with standard storage ~0.023/GB-month → ~4.60.

• Data Transfer:

  • Each request ~1kB outbound, 10k RPS is 10MB/s in the busiest hour (~36GB/hour). This might be $0.09/GB egress for some providers.
  • If traffic is busy only a fraction of the time, estimate ~200GB monthly egress → $18/month.

• Total:

  • Compute: ~$500 (average)
  • DB: ~$210
  • S3: ~$5
  • Data Transfer: ~$18
  • Grand Total: ~$733 monthly (plus overhead for region differences, messaging services, or caching).

• Articulation:

  • Summarize “We need around 700–800 monthly on AWS for the main pipeline. If usage grows 2×, we’d scale compute or DB horizontally, potentially doubling cost to 1500.”

5. Presenting Cost Info in Interviews

1. Keep it Approximate & Clear

   • Interviewers won’t expect exact decimals. Show you know the magnitude (e.g., “~100s for compute, a few 10s of for storage.”).
   • Ensure it’s consistent with your design’s usage model.

2. Tie to Constraints

   • “Given we have 10k RPS, we can’t run everything on a single small instance. So we scale horizontally, incurring more instance costs but ensuring reliability.”

3. Highlight Potential Savings

   • “Using a serverless approach might cut idle costs, but each function invocation has overhead. This is beneficial if load is spiky.”
   • “If we push static content to a CDN, we offload egress from our servers, potentially lowering compute costs but raising minimal CDN fees.”

4. Discuss Potential Future Growth

   • “If we expect a 2× user increase, we can spin up additional nodes with minimal friction, doubling costs but preserving performance. Alternatively, employing caching might flatten cost curves significantly.”

Conclusion

Illustrating cost estimations in large-scale system designs transforms abstract throughput or memory usage into tangible financial impacts. By understanding typical cloud pricing, estimating usage patterns, and multiplying them for a ballpark figure, you demonstrate real-world practicality. In interviews, it:

• Underscores engineering maturity—you’re building solutions that can realistically run at scale.
• Guides trade-offs around design complexity, performance, and reliability.
• Impresses stakeholders or interviewers who appreciate a solution that’s not just technically sound but also cost-aware.

Pair these cost estimation steps with robust design skills—like those taught in Grokking the System Design Interview—and real-time practice in Mock Interviews. You’ll solidify your ability to evaluate and communicate the financial feasibility of your designs, a key factor in modern software engineering.