An RPC usage audit establishes what your application actually consumes before a provider migration. Separate the number of users from the calls each feature generates, and distinguish foreground requests from background polling and historical imports. This guide focuses on the workload audit. Afterward, use the Alchemy alternatives forecast to compare current pricing models with measured inputs.
Build an inventory by feature, chain and method
Capture the method, network, result classification, elapsed time and an internal feature label for each request. Include calls generated by background jobs, dashboards and scheduled imports. Keep credentials and sensitive request data out of logs. Group requests into user journeys so you can explain why a balance screen or payment-status page creates a particular workload. A total call count without that context is difficult to optimize.
Find repeated reads and unnecessary polling
Look for multiple components fetching the same block or balance, timers that continue when a page is inactive and jobs that poll faster than the application needs. Immutable block data can be cached with the correct chain and block context. Recent balances need an explicit freshness policy. Measure the requests removed by each change and verify that the user-facing state still updates correctly.
Separate retries from successful application work
Classify transport failures, throttling, unsupported methods and application errors. A retry of an invalid request is not productive work, and immediate repetition can make throttling worse. Bound retries and add backoff where appropriate. Unknown transaction submissions require status checks against the original hash before constructing another transfer; a lower error count is not useful if recovery creates duplicates.
Map batching to actual accounting
A JSON-RPC batch can reduce network round trips while still containing several independently executed operations. At Crypto Chief, batch elements are accounted for individually. Compare a usage export for the same method set before and after batching instead of assuming fewer HTTP requests means a smaller bill. The Geth batch documentation explains its network-delay benefit.
Build a before-and-after audit table
For each feature, record calls, retries, success rates, freshness and measured duration over comparable periods. Keep temporary migrations and imports out of the steady-state baseline. Add expected events per user journey and the remaining background refreshes. This produces an explainable workload forecast rather than a generic estimate of requests per user.
Decide whether migration solves the remaining problem
After removing avoidable requests, identify the constraints that remain: method coverage, historical depth, rate limits, latency or cost. Compare candidates against those requirements using current terms. Keep provider migration effort separate from usage optimization so the same saving is not counted twice. Use the RPC cutover guide when there is a demonstrated reason to switch.
Frequently Asked Questions
Does batching reduce the number of billable RPC operations?
Not necessarily. It groups requests into one network exchange, while the provider may meter every element or method weight separately. Check current accounting rules and verify with usage data.
Should every repeated read be cached?
Cache only when the application can tolerate the resulting freshness and when the cache key includes the required chain and block context. A recent balance and an immutable old block have different caching requirements.
When is an audit complete?
You should be able to attribute the major request sources to features, explain retries and produce a repeatable baseline. Open coverage or error questions should remain visible rather than being absorbed into one total request count.