LLM API costs are one of the fastest-growing line items in engineering budgets: as teams deploy more AI agents, add use cases and serve more users, token consumption grows and the bill grows with it. The biggest levers are right-sizing the model for each task, not paying twice for repeated requests, sending less context, capping each key with maxSpend while watching per-request cost, and paying a lower posted rate for the same model. Here are those five levers, and what Router One does and does not do for each.
1. Right-Size the Model for Each Task
Not every request requires a frontier model. Intent classification, data extraction, simple formatting or a straightforward summary can be handled just as well by a smaller, cheaper model at a fraction of the cost. In practice most teams still default to one model for everything: they prototype on the flagship, it works, and it ships to production unchanged.
Take inventory of your AI workloads and categorize them by complexity:
- Low complexity (classification, extraction, simple formatting): Use the smallest model that passes your checks.
- Medium complexity (summarization, standard Q&A, content generation): Mid-tier models handle these well at moderate cost.
- High complexity (multi-step reasoning, code generation, nuanced analysis): This is where frontier models earn their price.
Choose the model in your application, per task. If your application is built on a framework, the model name is usually one field away — LlamaIndex, for example, takes it on its OpenAI-compatible LLM class pointed at the gateway (LlamaIndex setup guide).
How Router One helps: One key and one endpoint reach every model in the catalog, so moving a workload to a cheaper model is a one-line change. A request that names a model is served by that model, and a retryable failure can retry another healthy route for the same model. Only model="auto" leaves the choice to the gateway, which sorts each request into a low, medium or high tier (rules plus a lightweight classifier) and serves it with a model from that tier's server-owned list; the lists are not published, so name the model yourself when a workload's cost has to be predictable. Router One does not expose project-defined quality weights (smart routing). The dashboard shows usage and cost per model and per API key, and the cost calculator prices a swap before you make it. Two live small-tier pairings to start from: Claude Haiku 4.5 vs Gemini 3.5 Flash and DeepSeek V4.1 Flash vs Gemini 3.8 Flash, both rendered from current catalog rates.
2. Cache Repeated Requests in Your Application
In many production systems, a surprising share of LLM requests are near-duplicates. FAQ bots answer the same questions repeatedly. Data pipelines process records with identical schemas. Internal tools generate the same boilerplate over and over.
Caching these responses in your application removes the repeat calls entirely, and a cache hit returns in milliseconds instead of seconds.
How Router One helps: Router One exposes per-request cost metadata and model-level usage data, so teams can identify which models and API keys create the most spend. Your application must detect repeated inputs and decide what is safe to cache and for how long. Request metadata does not provide a reusable answer cache. Router One does not retain prompt or model-response bodies from direct API calls. It records request metadata for billing, usage tracking, and troubleshooting. Playground saves conversation history so users can revisit and continue their chats.
3. Send Less Context
In chat, agent and RAG workloads, input tokens often dominate the bill, because every turn resends the system prompt, the conversation so far and whatever was retrieved. Trim what each request carries: keep system prompts short and stable, summarize or drop old turns instead of replaying the whole history, retrieve fewer and smaller chunks, and set max_tokens on requests whose answers should be short.
How Router One helps: Every request in Dashboard → Logs shows its input and output tokens next to its cost, so the requests with oversized inputs are easy to find, and the API key filter shows which workload sends them.
4. Cap Spend per Key and Watch Usage
Cost overruns in LLM usage are rarely caused by steady, predictable growth. They come from sudden spikes: a bug that triggers an infinite loop of API calls, an agent that enters a retry spiral, or a new feature that unexpectedly generates 10x more tokens than estimated.
Budget controls act as guardrails. Give each application, environment, project, or agent a separate API key, then set a hard maxSpend cap on that key. This caps the blast radius of a runaway process without claiming an aggregate organization or project budget. Then track spend per model, per key and per time window, so a sudden spike on one workload key or an unexpected shift in model mix shows up long before the monthly invoice.
How Router One helps: Every Router One API key can carry a maxSpend cap, and runs under default rateLimit and tokenLimitTpm limits that support can raise on request. When a key reaches its spend cap, requests on that key stop even if the account wallet still has balance, and the console sends an in-app notification when a key reaches 80% of its maxSpend and when it hits the cap. Dashboard → Usage ranks your keys by spend, tokens and requests for the chosen range; Dashboard → Logs filters by model, API key, status and date range; logs and the wallet ledger export to CSV. Project- or agent-level isolation comes from issuing separate keys; Router One does not currently provide aggregate budgets or role hierarchy at those levels, so project-level rollups and any email or webhook notification workflow stay in your own monitoring stack. Per-key cost tracking shows what each key actually spent, and LLM observability lists the exact fields a trace carries.
5. Compare Posted Rates and Plans
The same model can cost less depending on where you buy it. Router One's own posted rate — up to 90% off official prices on select models — is listed per model on the model catalog, and the Claude API and GPT API rate pages show where each family sits today. Compare the posted rate of each model you call with what you pay now, and price candidate swaps on the cost calculator.
If your usage is steady, check on pricing whether a Pro, Max or Ultra plan covers the models you call most: plan quota applies only to the model tiers each plan lists, and every other call bills per token from the wallet. Image and video models are priced per image or per clip, so the discount claim does not apply to them.
Putting It All Together
These five levers compound. Right-sizing the model reduces your baseline cost; same-model fallback protects reliability while you do it. Application-layer caching removes redundant spend on top of that, and trimmed context shrinks every request that remains. Per-key caps and usage monitoring stop a regression before it becomes expensive, and comparing posted rates and plans lowers what each remaining token costs.
How much you save depends on your workload mix, so measure it: price each change on the cost calculator first, then compare spend per key week over week.
FAQ
How much can I reduce my LLM API costs? It depends on your workload mix: how much traffic can move to a smaller model, how many requests repeat, and how much context each request carries. Measure it rather than assume it — take a week of requests from the request log, price the swap on the cost calculator, then compare the following weeks' spend per key.
Does Router One automatically pick a cheaper model for each request?
Not when the request names a model: that model serves it, and when a route fails with a retryable error, the gateway can retry another healthy route for the same model — it never swaps in a different model. Only model="auto" leaves the choice to the gateway: it sorts each request into a low, medium or high tier (rules plus a lightweight classifier) and serves it with a model from that tier's server-owned list, which is not published. See smart routing.
Does Router One cache my prompts and responses? No. Your application must detect repeated inputs and decide what is safe to cache and for how long. Request metadata does not provide a reusable answer cache. Router One does not retain prompt or model-response bodies from direct API calls. It records request metadata for billing, usage tracking, and troubleshooting. Playground saves conversation history so users can revisit and continue their chats.
How do I stop a runaway agent or bug from draining my budget? Give each application, environment, project, or agent a separate API key, then set a hard maxSpend cap on that key. When a key reaches its spend cap, requests on that key stop even if the account wallet still has balance, which caps the blast radius of a runaway process; the console sends an in-app notification at 80% of the cap and when it is reached. Per-key cost tracking shows what each key actually spent.
What does Router One record for each request? Router One's observability layer captures request metadata including the model, the API key that sent it, tokens, cost, request ID, latency, and status; Dashboard → Logs shows those traces, and Dashboard → Usage totals them by day, model and API key. See LLM observability for the exact fields a trace carries.
Start Optimizing Today
Router One gives you one OpenAI-compatible API for the whole catalog, same-model provider fallback, per-model and per-key usage analytics, per-key spend caps, and request monitoring, plus the traces teams need to build safe caching in their own application layer. Sign up at router.one and start reducing your AI spend in minutes, not weeks.