Token costs are unusual among infrastructure costs because they scale with usage in a way that is highly non-linear. Your cloud hosting cost is roughly proportional to your traffic. Your token cost is proportional to your traffic multiplied by your average context window size per request -- and that second multiplier is something you can control with engineering decisions.
At low volumes, token cost management feels academic. At 1M requests per month, a 30% reduction in average input tokens translates to a real budget line. These three strategies are ordered by implementation difficulty: the first is immediate, the second requires some refactoring, and the third is an architectural change that pays off at scale.
Strategy 1: Instrument before you optimize
The most common mistake in token cost management is optimizing before measuring. Teams decide to reduce token costs and immediately start editing prompts, cutting retrieval chunk counts, or truncating conversation history. Sometimes this helps. Sometimes it degrades output quality without meaningfully reducing costs because the reduction happened in the wrong place.
Before any optimization, instrument your system to record per-call token counts broken down by component: system prompt tokens, conversation history tokens, retrieved context tokens, and other inputs. Output tokens should also be tracked separately. This breakdown takes an hour to build and immediately reveals which component is the dominant contributor to your token bill.
In many applications, the answer is surprising. Teams that assume their system prompt is the cost driver often find that their conversation history is contributing 3x more tokens because it's appended without truncation. Teams that assume retrieval is the issue find their prompts have grown 5x over six months of iteration. You cannot optimize what you have not measured.
Strategy 2: Dynamic context assembly
Static context assembly -- always include the full system prompt, always include the last N turns, always include the top-K retrieved chunks -- is simple to implement and reliably wasteful at scale.
Dynamic context assembly adjusts the context based on what the specific request actually needs. For a short, direct question, you may not need 8 retrieved chunks or 10 turns of history. For a complex multi-part question, you may need more. The request type determines the context depth.
A simple implementation: classify each incoming query as simple, standard, or complex using a lightweight local classifier or a set of heuristics (query length, presence of multi-part structure, question type). Apply different context budgets to each class. Simple queries get a smaller history window and fewer retrieved chunks. Complex queries get the full budget. This alone typically reduces average token count by 20-35% across a realistic query distribution, while having minimal impact on output quality for the queries that actually needed a smaller context.
A more sophisticated version uses a context budget in tokens rather than component count: assign a fixed token budget to the context window and fill it greedily by component priority. System prompt first (non-negotiable), then recent history, then retrieved context ranked by relevance score. When the budget is exhausted, stop. This prevents any single component from consuming disproportionate budget regardless of how it grew.
Strategy 3: Tiered model routing
The third strategy addresses a different lever: which model is handling each request. Not all requests need your most capable (and most expensive) model. Simple queries -- format conversions, extraction from structured text, short factual lookups -- often produce identical output quality on smaller, cheaper models.
Tiered model routing directs requests to different models based on the complexity classification from Strategy 2, or based on request type. Simple and standard requests go to a smaller, cheaper model. Complex requests, requests that previously failed on the smaller model, and requests in domains where the capability gap is significant go to the full model.
The economics here are significant. If your primary model costs $5/M input tokens and a capable smaller model costs $0.15/M input tokens, routing 40% of your traffic to the smaller model reduces your blended input token cost by nearly 40% on that traffic segment. This is the highest-leverage optimization available to teams at scale.
The implementation challenge is the routing classifier. A bad classifier that routes complex requests to the cheap model is worse than no routing at all, because you'll see quality degradation in a subset of responses that is hard to diagnose. Build the routing logic conservatively: err toward routing to the capable model when the signal is ambiguous. A routing model that is 90% accurate at $0 cost per call is more valuable than one that is 99% accurate at $0.001 per call.
Putting it together
These three strategies compound. Instrumentation gives you the baseline. Dynamic context assembly reduces your average tokens per call. Tiered routing reduces your average cost per token. Applied together on a real high-volume application, the combined effect is typically a 40-60% reduction in total API token spend with no measurable regression in output quality across the majority of traffic.
The instrumentation comes first. Everything else follows from being able to see what you're actually spending and where.