The most reliable way to reduce LLM API spend is to find which production workflows drive it, then change one cost lever at a time while checking task quality, latency, retries, and reliability. Start with provider-reported usage by request; next test prompt caching for repeated context, batch processing for work that can wait, and less expensive models for tasks they can handle. A lower token price or shorter prompt is not a saving if it leads to more calls or worse outcomes.
Measure the workload before changing it
LLM cost is shaped by how much billable usage an application sends and generates, and by the applicable price for that usage. OpenAI describes this as a function of token volume and cost per token in its production best practices. The practical first step is to capture what the provider reports for each generation, rather than estimating a bill from prompt length alone.
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Record, where available, input, output, cached input, and other billable usage alongside the model identifier and the application context for the call. Attribute requests to a feature, workflow, and user or tenant when that is appropriate for your privacy and product requirements. Keep request counts, provider charges, latency, retries, and a relevant quality measure together; otherwise, a change that lowers tokens but increases calls or failed tasks can look better than it is.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsBuild a baseline by workflow, not just for the application as a whole. Averages can hide a small number of expensive workflows, or a high-volume feature with a modest cost per request. Compare like with like over a representative period and workload, and use provider pricing pages for the model and usage types actually in use. OpenAI and Anthropic publish provider-specific, changeable schedules in their respective OpenAI API pricing and Claude pricing documentation.
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Choose the lever that fits the expensive work
| Lever | Best fit | What to verify |
|---|---|---|
| Prompt caching | Calls that reuse a stable, sufficiently long prompt prefix | Eligible cached usage, cache hits, write charges, and cache duration |
| Batch processing | Large volumes of work that do not need an immediate response | Provider-specific batch terms, completion time, and suitability for the workflow |
| Model routing | Well-defined tasks where a less capable model may meet requirements | Quality, latency, retries, escalations, and total cost per successful task |
| Input and output reduction | Requests carrying duplicate context or generating more than the feature needs | Quality and retry behavior after each reduction |
Use prompt caching for repeated context
Caching is useful when requests reuse a stable prompt prefix and the provider can serve that content from cache within its cache rules. It is not a general discount on every input token: eligibility thresholds, cache windows, cache reads, and cache writes all affect whether it pays off. Measure actual cached usage and hits on the production-like workload before estimating the effect on spend.
The pricing terms are provider- and model-specific. Anthropic’s pricing documentation, accessed October 7, 2026, lists standard cache reads at 0.1x the base input price, with model-specific exceptions and separate cache-write multipliers and durations; consult the current Claude pricing documentation for the model in use. OpenAI’s October 1, 2024 prompt-caching announcement reported a 50% cached-input discount for the models listed in that announcement. That dated figure is not a current universal rate: check the applicable model’s current OpenAI pricing before making a budget assumption.
To evaluate caching, identify the repeated prefix, check the provider’s current eligibility and cache behavior, then compare the provider-reported cached reads and writes with uncached usage. A low hit rate, frequent prompt changes, or write costs can reduce or eliminate the expected benefit.
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Batch processing can suit large volumes of requests when results do not need to arrive in an interactive response. It changes the processing mode and timing, so it is not a fit for every user-facing call; consider jobs such as offline processing only when their completion requirements allow asynchronous handling.
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Google’s Gemini API optimization documentation says its Batch API is designed for large request volumes processed asynchronously at 50% of standard cost. This is Google’s documented term for eligible Gemini batch processing, not a guaranteed reduction in an application’s total bill or a discount that applies across other providers. Check the current Gemini API optimization documentation for applicable conditions, and measure the workflow’s completion time and operational fit as well as its charge.
Route tasks to the least costly model that meets the requirement
Model choice should follow task performance, not a price table alone. Separate workflows by their actual requirements, then evaluate candidate models on representative examples. The cheaper candidate is useful only if it continues to meet the feature’s quality threshold without creating enough retries, escalation to another model, manual review, or downstream failure to erase the price difference.
Compare end-to-end cost per successful task, not just cost per call. Include retries and any escalation or review path in the comparison, and assess latency and reliability against product requirements. Provider pricing and model availability change, so verify current terms and evaluate the model versions you intend to deploy. Anthropic describes model selection as a cost-and-intelligence trade-off in its guide to optimizing for cost and intelligence; use provider guidance as input, then validate your own task mix.
Trim inputs and outputs without damaging results
Remove duplicate context, omit conversation history the current task does not need, and avoid sending irrelevant documents or instructions. Set output limits to what the feature requires rather than allowing unnecessarily long responses. Make these changes incrementally: shorter context or constrained output can alter answer quality, and the amount saved depends on the requests and applicable prices. There is no universal saving percentage for prompt trimming.
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For each change, compare a representative quality set and monitor live retry behavior after rollout. If the task requires a minimum level of completeness or correctness, make that requirement explicit in the evaluation rather than treating fewer generated tokens as success by itself.
Run a controlled cost-reduction cycle
- Instrument calls. Capture provider-returned usage and cost where available, model identifiers, and application tags for feature or workflow. Preserve the granularity needed to investigate expensive request patterns.
- Set a baseline. For each important workflow, record request volume, input and output usage, cached usage when available, provider charges, latency, retries, and a quality measure.
- Find the dominant component. Determine whether spend is concentrated in repeated context, work that can run asynchronously, a model more capable than the task needs, or unnecessary input and output.
- Change one lever. Test it on a representative workload and compare total cost per successful outcome against the baseline, alongside quality, latency, and reliability.
- Roll out and recheck. Watch the same measures after deployment. Revisit provider documentation and invoices as price schedules, model availability, and your workload change.
Make cost telemetry match the bill
Cost dashboards are only as useful as their usage data and pricing configuration. Langfuse documents generation-level usage and cost records, dashboards, alerts, and metrics queries in its token and cost tracking documentation. It can use provider-supplied usage and cost or infer cost from configured model prices. For some reasoning models, inference may not be accurate without usage counts, so capture provider-reported counts when exact billing matters.
You do not need a particular observability product to do the analysis: provider usage data and your own telemetry can support the same workflow. Whichever approach you use, keep configured prices aligned with the relevant model and terms, account for any negotiated rates where applicable, and compare telemetry with provider invoices. Tracking reveals where to investigate; it does not itself lower costs.
Quick Recap
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