Serverless costs explained: invocations, duration, requests, and downstream spend
Serverless estimates fail when you model only invocations. The reliable approach is: invocations + duration + logs + egress + downstream requests. This hub links the best checklists and calculators to avoid thin compute-only models.
1) Invocations and duration (compute time)
- Convert traffic to invocations per month.
- Model average duration and consider peak / incident scenarios separately.
- Hub: request-based pricing
2) Logs and metrics (often the surprise)
- Verbose logs can dominate for chatty functions.
- Metrics cost is a cardinality problem; label explosion is common in serverless apps.
- Hubs: log costs, metrics costs
3) Egress and downstream spend
- Outbound transfer scales with payload size and traffic.
- Downstream requests (DB/queue/storage) can dwarf compute charges.
- Hubs: networking costs, database costs, messaging costs
Related tools
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Related calculators
Data Egress Cost Calculator
Estimate monthly egress spend from GB transferred and $/GB pricing.
API Response Size Transfer Calculator
Estimate monthly transfer from request volume and average response size.
VPC Data Transfer Cost Calculator
Estimate data transfer spend from GB/month and $/GB assumptions.
Cross-region Transfer Cost Calculator
Estimate monthly cross-region transfer cost from GB transferred and $/GB pricing.
Log Cost Calculator
Estimate total log costs: ingestion, storage, and scan/search.
Log Ingestion Cost Calculator
Estimate monthly log ingestion cost from GB/day or from event rate and $/GB pricing.
FAQ
What usually drives serverless cost?
Invocation count and duration are the core drivers, but logs/metrics and networking/egress are common surprises. Downstream services (databases, queues, storage) often dominate the system cost even if compute is small.
How do I estimate quickly?
Estimate monthly invocations, average duration, and average payload size. Then add separate budgets for logs (GB/day) and egress (GB/month), and validate retries/timeouts.
What breaks estimates?
Retry storms, chatty calls to downstream services, and verbose logs during incidents. Also, cold starts and concurrency can change resource usage patterns.
Last updated: 2026-01-22