AI Insights: Running LLMs in Production — What Breaks First?

Introduction:

Most teams discover the limits of LLMs not during experimentation, but after deployment. Demos work. Proofs-of-concept impress stakeholders. Early users are excited. Then reality arrives — usually in the form of latency spikes, unexpected costs, inconsistent outputs, or systems behaving in ways no one anticipated.

What breaks first in production is rarely the model itself. It’s the surrounding system: assumptions, workflows, and guardrails that were never designed for probabilistic behavior.

This blog walks through the first failure points teams encounter when running LLMs in production, and why these issues appear earlier than expected.

Latency Is the First User-Facing Failure:

Latency is often the earliest and most visible problem. What feels acceptable in a demo becomes frustrating under real usage.

Production traffic introduces:

• variable response times
• cold starts
• retries due to timeouts
• network hops across services

Users are far less tolerant of delays than teams anticipate. Even small increases in latency change how products feel. Teams respond by adding async flows, background tasks, and caching — which improves experience but adds architectural complexity very early.

Cost Spikes Before Anyone Notices:

Cost rarely breaks the system immediately, but it breaks confidence quickly.

Early estimates assume:

• stable prompt sizes
• predictable request volume
• limited context windows

Production reality introduces:

• prompt expansion as features grow
• higher-than-expected user engagement
• multiple model calls per interaction

Costs don’t fail gracefully. They jump. Teams often discover this only after billing alerts trigger, forcing rushed optimizations that could have been designed upfront.

Prompt Assumptions Start to Leak:

Prompts that worked well in isolation often degrade in production. Real user inputs are messier, longer, and more ambiguous than test cases.

Common issues include:

• prompts growing brittle as edge cases accumulate
• unintended instruction conflicts
• context windows filling up faster than expected

Prompt design becomes a maintenance problem, not a one-time task. Teams that treated prompts as static artifacts struggle once iteration begins.

Output Consistency Breaks Trust:

Inconsistent outputs are one of the fastest ways to lose user trust. LLMs may produce correct responses most of the time — but “most of the time” is not enough for many workflows.

In production, inconsistency shows up as:

• subtle changes in tone or structure
• unexpected hallucinations
• differing answers to similar inputs

Teams quickly realize that correctness is not binary. Consistency, predictability, and recoverability matter just as much as raw accuracy.

Observability Gaps Slow Down Debugging:

When something goes wrong, teams often lack the visibility needed to understand why.

Unlike traditional systems, failures may stem from:

• prompt changes
• context composition
• model updates
• tool-selection logic

Without structured logging of prompts, responses, and decisions, debugging becomes guesswork. Observability is usually added reactively — after incidents — rather than as a first-class design concern.

Tool Integration Fails in Subtle Ways:

LLMs rarely operate alone in production. They call tools, query databases, and trigger workflows.

Early failures often include:

• incorrect tool selection
• malformed inputs to downstream systems
• partial failures that leave systems in inconsistent states

These failures are harder to detect because the LLM appears to “work” while silently causing downstream issues. Tool misuse exposes why strict contracts and validation matter.

Human-in-the-Loop Arrives Earlier Than Expected:

Many teams plan to add human review later. In practice, they add it sooner than planned.

Human oversight becomes necessary for:

• high-risk actions
• customer-facing decisions
• edge cases the model cannot confidently resolve

This introduces new workflows, staffing needs, and response-time considerations. Human-in-the-loop is not a fallback — it becomes part of the core system.

Model Updates Introduce Regression Risk:

Models evolve. Providers ship updates. Behaviour changes.

In production, this creates:

• subtle regressions
• shifts in output quality
• broken assumptions baked into prompts

Teams that lack versioning strategies or controlled rollouts feel these changes immediately. Stability becomes an architectural responsibility, not something delegated to the model provider.

Security and Privacy Friction Emerges Quietly:

As systems mature, teams confront data movement and compliance concerns.

Issues surface around:

• logging sensitive inputs
• retaining prompts for debugging
• sharing context across services

Security and privacy rarely break functionality, but they break trust and compliance if ignored. Retrofitting controls later is always more expensive.

The Pattern Behind What Breaks First:

Across teams, a consistent pattern emerges: what breaks first is not intelligence, but assumptions.

Systems fail where:

• determinism was assumed
• costs were treated as linear
• observability was postponed
• guardrails were implicit, not explicit

LLMs amplify weak system design rather than replace it.

Conclusion:

Running LLMs in production exposes weaknesses faster than most technologies. Latency, cost, inconsistency, and observability issues surface early — not because LLMs are unreliable, but because they demand a different way of thinking about systems.

Teams that succeed anticipate these breakpoints and design around them. They treat LLMs as probabilistic components inside deterministic systems, and they invest early in guardrails, visibility, and cost awareness.

The lesson is simple but uncomfortable: the first thing to break in production is rarely the model. It’s the system around it.

References:

• OpenAI – Production Best Practices for LLM Applications (🔗 Link)
• Anthropic – Building Reliable and Cost-Aware AI Systems (🔗 Link)
• Stanford HAI – AI Index Report (Cost & Deployment Trends) (🔗 Link)