Taming LLM Spend Without Tanking Quality
Managing LLM costs while maintaining output quality remains one of the biggest challenges for teams deploying AI at scale. This article breaks down four practical strategies that leading practitioners use to control expenses without sacrificing performance. Industry experts share specific techniques for compression, caching, budget enforcement, and value-based token allocation that deliver measurable results.
Compress Visual State to Cut Cost
To control LLM spend in our production multimodal creative assistant, we implemented a Context Budgeting framework that replaced expensive raw image prompting with a compact Graphical Design Representation (GDR), effectively compressing visual state into a structured JSON to minimize token overhead. We enforced strict token budgets through dynamic policies like Two-Stage Hierarchical Processing, which routed simple requests to zero-shot context-lean prompts while reserving retrieval-augmented or few-shot examples only for complex queries where they demonstrably improved routing success. To optimize our hit strategies, we utilized Retrieval-Augmented Context to dynamically select relevant few-shot examples via embedding similarity, ensuring we only consumed tokens on contextually pertinent history rather than static, voluminous banks. The defining metric that shifted our production behavior was the relative token cost per successful intent; by correlating token consumption with stage-wise failure rates, we discovered that Context Compression achieved a 93.3% intent accuracy, outperforming token-heavy Chain-of-Thought strategies, while reducing input tokens by nearly eight times and cutting p95 latency by 45%, proving that strategic summarization offers a superior cost-performance ratio than maximizing context.
Cache Decisions and Track Individual Spend
We stopped caching responses and started caching decisions. When a user asks how to reset their password, we cache the decision to pull docs 45 and 67. Then someone asks "I forgot my login" and we get a cache hit because it is the same retrieval decision even though the wording is different. Fresh response every time but the expensive lookup already happened.
That got our cache hit rate from around 20 percent to over 85 percent. The metric that changed behavior was surfacing per-user daily token spend to team leads. Once people could see which workflows were burning through context windows they started truncating conversation history and using summarization instead of raw threads.
Enforce Daily Caps for Accountability
Account-level token limits sound smart until nobody owns them. Budget runs dry, teams point fingers. Nothing changes.
The fix: per-feature token budgets. Each feature got its own ceiling—autocomplete, summarization, chat—tracked separately. When one torched 80% by week two, that team felt it. They fixed it.
Caching: semantic over exact-match. Exact-match gives maybe 15% hit rate. Semantic—matching by meaning, not characters—pushed us to 63%. Cut API costs 58%.
Context window: hard cap at 4K tokens per request. Gateway-enforced. Anything over gets auto-summarized before the model sees it. Teams howled. Quality held. Costs cratered.
Before: $47K monthly, zero visibility. After: $15K with per-feature dashboards and alerts at 70% burn.
The quota that actually worked: daily ceiling per feature. Not monthly. Monthly lets teams coast. Daily forces the fix.
Link Tokens to User Value
When HeyOz put generative AI features into production, the biggest breakthrough was treating tokens as a product constraint rather than an infrastructure detail.
Initially, we set strict token budgets for each request, linking them to the feature's value instead of the model's limitations. For instance, a caption generator and a video concept generator had distinct ceilings, even when using the same model. Context windows were explicitly capped, and anything exceeding that had to justify its inclusion. Older messages, lengthy system prompts, and low-value instructions were aggressively shortened or summarized before each interaction.
Caching proved more crucial than anticipated. We implemented semantic caching at the prompt-output level for common user intentions and template-based workflows. When two users requested similar outputs with comparable inputs, we reused existing results or partial completions. Even a moderate cache hit rate significantly reduced costs without compromising quality.
The single metric that truly altered behavior was tokens per successful output. This was not tokens per request, but tokens divided by the outputs users actually accepted or published. We displayed this metric on dashboards, similar to how teams track latency or error rates.
Once engineers realized that retries, overly large prompts, or unnecessary context directly increased this number, behavior changed rapidly. Prompts became more concise. Defaults became smaller. Features were designed to reach a conclusion faster rather than "thinking longer."
Crucially, we did not penalize teams for increased spending if quality improved. We only flagged instances where token usage rose without a corresponding increase in acceptance or engagement. This approach fostered healthy experimentation while quietly enforcing discipline.
The key insight is that cost control is most effective when linked to user value. When teams focus on achieving efficient outcomes rather than just raw token counts, spending decreases as a natural consequence, not a primary objective.
