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AI Agent Stack Availability and Downtime Calculator

An AI assistant depends on a chain: gateway, model server, vector store, ERP API and network. Each link has its own uptime, and they multiply. This calculator gives the end-to-end figure and shows what a second inference node adds.

Your numbers

ERP API availability

LLM inference node availability

99.5 %

A single GPU server or a hosted API endpoint as you measure it.

Independent LLM nodes

Vector database availability

Gateway and authentication availability

Network and hosting availability

Your results

End-to-end availability

99.1522

Serial components multiply.

Expected downtime per year

74 hrs

Effective LLM tier availability

99.5

Redundant nodes in parallel: 1 - (1 - a) to the power of n.

Expected downtime per month

371.3 min

Downtime with a single LLM node

74 hrs

Hours per year saved by redundancy

0 hrs

Assumes independent failures and perfect failover. Real redundancy shares power, network and software, so treat results as an upper bound on availability.

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Save results:

Annual downtime vs LLM node availability

Sweeps LLM inference node availability from 95 to 99.9, holding your other inputs fixed.

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How to use it

Enter measured availability for each component, from your monitoring over the last quarter rather than vendor claims. For hosted APIs use observed success rate, not the contractual SLA. Pick how many independent LLM nodes serve traffic. The result is the expected uptime if every component must work for a request to succeed. The chart sweeps the model tier's availability to show how much it moves the total.

The math

For components in series, availability is the product of the individual values. For n identical independent nodes in parallel, tier availability = 1 - (1 - a) to the power n. Annual downtime = (1 - availability) x 8,760 hours. Five nines is about five minutes a year; three nines is almost nine hours.

The weakest serial link dominates.
Redundancy helps only the tier it duplicates.
Maintenance windows count unless you can drain traffic.

Practical notes

Decide what happens during an outage: graceful degradation to the ERP's native screens is better than a broken assistant. A single GPU server is often the weakest link, but a second node doubles hardware cost, so compare saved downtime hours against the business value of the assistant. Also test failover, including model reload times, since a node that takes minutes to load a large model is not instantly available.

Frequently Asked Questions

Why do components multiply?

If every part must work for a request to succeed, the chance all work together is the product of each chance, assuming failures are independent.

Is a second GPU node worth it?

It depends on how critical the assistant is. If it supports shift operations, yes. For occasional analytics, a documented manual fallback may be enough.

Do hosted LLM APIs have better uptime?

Not necessarily. Providers publish SLAs but incidents and rate limits still occur. Use your own measured success rate, and consider a second provider or local fallback model.