Tokens, Throughput, and Cost Discipline

As large language models transition from experimental demos to production systems serving millions of users, performance engineering has emerged as a critical discipline. The difference between a sustainable LLM application and one that burns through budgets lies in understanding the intricate relationship between tokens, throughput, and cost optimization.

The Token Economy

Every interaction with an LLM is fundamentally a transaction in tokens. Input tokens flow in, output tokens flow out, and each carries a cost. The first principle of LLM performance engineering is ruthless token awareness. Modern applications often leak tokens through verbose system prompts, redundant context, and poorly structured conversations that repeat information across turns.

Prompt compression has become an essential technique for token efficiency. Rather than sending full conversation histories with every request, successful implementations employ summarization strategies. After several exchanges, earlier messages are compressed into concise summaries that preserve essential context while dramatically reducing token counts. Some teams report 60-70% reductions in context size without meaningful degradation in model performance.

Dynamic prompt assembly takes this further. Instead of maintaining static, bloated system prompts, sophisticated applications construct prompts on-demand, including only the instructions and examples relevant to the specific user request. A customer service bot, for instance, might have dozens of specialized capabilities but only loads the relevant subset for each query.

Caching: The Multiplier Effect

Prompt caching represents one of the most impactful optimizations available today. Leading providers now offer mechanisms to cache portions of prompts that remain stable across requests, charging only for new tokens in subsequent calls. For applications with consistent system instructions or reference documentation, caching can reduce costs by 90% or more for the cached portion.

The strategic placement of cacheable content matters enormously. System prompts, few-shot examples, and knowledge bases should be positioned to maximize cache hits. Some engineering teams structure their prompts specifically to optimize cache boundaries, separating stable context from dynamic content.

Semantic caching extends this concept further. By maintaining a vector database of previous queries and responses, applications can identify sufficiently similar requests and return cached results without calling the LLM at all. This approach requires careful similarity threshold tuning but can eliminate 20-40% of LLM calls in domains with repetitive queries.

Batching and Throughput Management

For high-volume applications, batching transforms economics. Rather than processing requests individually, batching accumulates multiple queries and submits them together, dramatically improving throughput per dollar. The tradeoff is latency, applications must balance user experience expectations against cost efficiency.

Intelligent batching systems employ dynamic windows that close based on either time thresholds or request accumulation. During peak hours, batches might close after 50 milliseconds or 10 requests, whichever comes first. During quiet periods, larger batches optimize for cost over latency.

Streaming responses have become table stakes for user experience, but they also enable sophisticated cost controls. By streaming tokens as they generate, applications can implement early stopping mechanisms that halt generation when sufficient information has been provided. This prevents over-generation and reduces output token costs.

Architectural Cost Discipline

The choice of model tier profoundly impacts economics. Many applications default to flagship models when smaller, faster alternatives would suffice. A well-designed system employs model routing, directing simple queries to efficient models while reserving powerful models for complex reasoning tasks. This hybrid approach can reduce costs by 50% or more while maintaining quality.

Rate limiting and quota management protect against runaway costs. Per-user token budgets, request throttling, and circuit breakers prevent individual users or attackers from consuming excessive resources. These controls should operate at multiple levels, per request, per user, per hour, and per day.

Monitoring and observability complete the performance engineering toolkit. Successful teams instrument every LLM interaction, tracking token usage, latency, cache hit rates, and cost per request. This telemetry enables continuous optimization and quickly surfaces regressions or abuse patterns.

Scaling with Discipline

As LLM applications scale from hundreds to millions of users, the compounding effect of small inefficiencies becomes existential. A wasteful prompt that costs an extra 500 tokens per request becomes $50,000 per month at scale. Performance engineering isn't optional anymore, it's the difference between sustainable growth and unsustainable burn rates.

The teams winning at LLM performance engineering treat it as a continuous practice, not a one-time optimization. They measure relentlessly, experiment constantly, and maintain strict discipline around token budgets. In an era where LLM capabilities continue to improve, the competitive advantage increasingly lies not in what models can do, but in how efficiently they can be deployed at scale.