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A Year of LLM Inference: Lessons Learned
Year-end retrospectives are about patterns, not events. The individual incidents fade, but the lessons compound. Looking back at a year of LLM inference, certain themes repeat: the gap between benchmarks and production, the importance of measurement, the value of simplicity.
Patterns That Emerged
def patterns_that_emerged():
return {
"benchmarks_lie": {
"lesson": """
Benchmark performance rarely predicts production performance.
Models that looked great in testing failed under load.
Models that seemed slow had better tail latency.
The benchmark distribution never matched real traffic.
""",
"what_we_do_now": "Shadow production traffic for evaluation",
},
"cost_sneaks_up": {
"lesson": """
LLM costs compound faster than expected.
Started at $5K/month, ended at $50K/month.
Not because pricing changed, but because usage patterns shifted.
Features that seemed cheap per-request became expensive at scale.
""",
"what_we_do_now": "Cost attribution by feature, weekly reviews",
},
"simple_beats_clever": {
"lesson": """
Clever optimizations often aren't worth the complexity.
Complex caching logic saved 10% but added bugs.
Simple batch size tuning saved 30% with no bugs.
The boring solution usually wins.
""",
"what_we_do_now": "Prefer simple, measure everything",
},
"users_find_edge_cases": {
"lesson": """
Users will find every edge case you didn't anticipate.
Prompts longer than we imagined.
Languages we didn't test.
Use patterns that broke assumptions.
""",
"what_we_do_now": "Defensive limits, comprehensive monitoring",
},
}What We Got Right
def what_we_got_right():
return {
"early_monitoring": {
"decision": "Invested in monitoring before scale",
"payoff": "Caught problems early, avoided incidents",
"recommendation": "Monitor from day one, not day 100",
},
"gradual_rollouts": {
"decision": "Always canary new models",
"payoff": "Caught quality regressions before widespread impact",
"recommendation": "Never ship 100% immediately",
},
"fallback_planning": {
"decision": "Built fallback paths from the start",
"payoff": "Survived multiple provider outages gracefully",
"recommendation": "Plan for failure before you need to",
},
"cost_tracking": {
"decision": "Attributed costs to features early",
"payoff": "Identified expensive features, made informed tradeoffs",
"recommendation": "Know where your money goes",
},
}What We Got Wrong
def what_we_got_wrong():
return {
"over_engineering_caching": {
"mistake": "Built complex semantic caching before we needed it",
"result": "Complexity without proportional benefit",
"lesson": "Start simple, add complexity only when measured need",
},
"underestimating_context_growth": {
"mistake": "Designed for 4K context, users wanted 32K",
"result": "Major refactoring mid-project",
"lesson": "Plan for context growth from the start",
},
"ignoring_tail_latency": {
"mistake": "Optimized for P50, ignored P99",
"result": "Happy average users, frustrated edge cases",
"lesson": "P99 is the user experience metric",
},
"trusting_provider_docs": {
"mistake": "Assumed documented behavior was actual behavior",
"result": "Surprised by undocumented rate limits, quirks",
"lesson": "Verify critical assumptions with testing",
},
}What Changed in 2025
def what_changed():
return {
"pricing_dropped": {
"observation": "Same capability, 3-5x cheaper than early 2025",
"implication": "Projects that were cost-prohibitive became viable",
"action": "Re-evaluate cost-benefit of shelved projects",
},
"context_windows_grew": {
"observation": "32K to 128K became standard",
"implication": "Less need for retrieval, more need for context management",
"action": "Rethink context strategies",
},
"quantization_matured": {
"observation": "INT8/INT4 quality gap narrowed significantly",
"implication": "Quantization is default, not optimization",
"action": "Start with quantized, prove you need full precision",
},
"serving_standardized": {
"observation": "vLLM, TGI became de facto standards",
"implication": "Less DIY, more leverage of community work",
"action": "Contribute to standards rather than reinventing",
},
}Advice for Next Year
def advice_for_next_year():
return {
"start_with_monitoring": {
"why": "You can't improve what you can't measure",
"minimum": [
"Latency percentiles (P50, P95, P99)",
"Error rates by type",
"Cost per request, per feature",
"Quality metrics for your task",
],
},
"embrace_routing": {
"why": "One model doesn't fit all tasks",
"approach": """
Classify requests by complexity.
Route simple to small, complex to large.
Measure quality at each tier.
""",
},
"plan_for_failure": {
"why": "Providers fail, models regress, traffic spikes",
"minimum": [
"Fallback to alternative provider",
"Fallback to smaller model",
"Rate limiting per user/feature",
"Graceful degradation under load",
],
},
"keep_it_simple": {
"why": "Complexity compounds, simplicity scales",
"heuristic": """
If an optimization requires explanation, question if it's worth it.
Boring solutions that work beat clever solutions that might.
""",
},
}The Metrics That Mattered
def metrics_that_mattered():
return {
"not_just_latency": {
"learned": "Latency is table stakes, not differentiator",
"matters_more": [
"Quality consistency",
"Error rate under load",
"Cost per successful request",
],
},
"not_just_cost": {
"learned": "Cheapest isn't best if quality suffers",
"matters_more": [
"Quality per dollar",
"User satisfaction at cost point",
"Long-term retention impact",
],
},
"not_just_throughput": {
"learned": "High throughput with bad tail latency frustrates users",
"matters_more": [
"Consistent experience",
"Graceful degradation",
"Fair queuing under load",
],
},
}Looking Forward
def looking_forward():
return {
"what_stays_true": [
"Measure before you optimize",
"Simple beats clever",
"Plan for failure",
"Monitor continuously",
],
"what_will_change": [
"Models will get better and cheaper",
"New techniques will emerge",
"Today's optimizations may become obsolete",
"The fundamentals of good engineering won't",
],
"parting_thought": """
The specific techniques evolve.
The principles remain.
Measure. Iterate. Ship. Learn.
Repeat for another year.
""",
}A year of LLM inference taught us that the fundamentals matter more than the specifics. Models will change, techniques will evolve, but measurement, simplicity, and preparation for failure are always right. Happy new year. May your P99 be low and your quality high.