Playground
Fallback Chain Simulator
LLM fallback chain simulator: Monte Carlo a primary + two fallbacks across Anthropic, OpenAI, Google. Success rate, p50/p95/p99 latency, cost, degradations.
- Inputs
- Paste + configure
- Runtime
- 1–15 s
- Privacy
- Client-side · no upload
- API key
- Not required
- Methodology
- Open →
1 · Configure the chain
ref p50 = 900ms
ref p50 = 700ms
Success rate
70.0%
700 / 1000 trials
p50 latency
571ms
p95 latency
1.00s
p99 latency
1.00s
Total cost
$22.906
avg $0.023/call
Degradations
60
F1:55 · F2:5
2 · Provider utilization (share of successful trials)
Recommendation
Gemini 2.5 Flash has a better cost-per-successful-call than the current primary (0.0316 → 0.0030). Consider swapping.
How the trial is simulated
for each trial:
elapsed = 0
for leg in [primary, fallback1, fallback2?]:
if uniform() < rate_429: # throttled
elapsed += 50ms; continue
latency ~ Exponential(mean = max(p50, p99 / ln(100)))
elapsed += latency
if elapsed <= deadline: # success on this leg
return success
return failureFailure modes modeled independently per leg (rate-limit + latency tail). See methodology for assumptions and limits — real outages are correlated and bursty.
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