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LLM Cost Optimization: How to Reduce AI Infrastructure Costs
Piyush-Kalra
I've watched teams celebrate an AI demo, then panic a few months later when the bill arrives. A prototype costing $50 a month can become a five-figure line item once real traffic hits. That's not only a pricing problem, it's also a workload problem.
The instinct is to blame the model. But cost per token is only one variable. Cost per successful outcome also depends on how many tokens you use, how often you call the model, and how efficiently your infrastructure runs. As AI workloads become more agentic, a single request can trigger multiple model calls, making unnecessary inference a significant source of waste.
In this article, we'll look at what LLM cost optimization means, where AI infrastructure spend comes from, and practical ways to reduce it, from model routing and caching to GPU utilization and inference serving.
What Is LLM Cost Optimization?
LLM cost optimization reduces the costs of processing AI workloads without compromising the quality, reliability, or performance you need. It is more than just getting the lowest per-token cost. For API-based workloads, a simple cost approximation is:
LLM cost ≈ price per token × tokens per call × calls per task
Each variable is important and can affect the cost. A more expensive model may increase the cost per token, and prompts that are longer than necessary may increase the tokens per call and a multi-step process increases the calls per task.
This is critical for agentic workloads in which one user request may trigger several model calls. A workflow that needs eight calls may be far more expensive than a workflow that requires only one call to complete the same task.
Therefore, it is necessary to evaluate costs per successful outcome instead of just costs per million tokens. A more capable model that completes the task may be cheaper than a less capable model that requires several attempts and extra calls to complete the task.
LLM Costs vs. AI Infrastructure Costs: What's the Difference?
These are costs that are related but different layers:
LLM/API costs: These are the costs of usage for a model provider like OpenAI or Anthropic where the cost is determined by input and output tokens and other pricing tiers. For teams using Amazon Bedrock, understanding AWS Bedrock pricing can help identify the cost differences between models and pricing models.
AI inference costs: The costs related to generating model outputs, such as API inference costs and the compute costs incurred when models are directly hosted.
AI infrastructure costs: The underlying cloud and technology costs for supporting the AI workloads, including GPUs, compute resources, storage, networking, databases, and orchestration.
In the case of API-based inference, a significant portion of the underlying infrastructure costs gets passed on to the customer through the provider's pricing. For self-hosted models, those costs get passed on directly to the team.
If you optimize only the model's per-token price, you may miss other sources of inefficiencies, such as redundant model calls or significant underutilized GPU compute capacity, or infrastructure that is scaled to a much higher capacity than is actually required. Thus, managing the cost of LLMs effectively requires looking at the workload and cost infrastructure that supports the workload.
Where Does AI Infrastructure Spend Actually Come From?
Before you can reduce a cost, you have to know where it's coming from. There are five key contributors to the cost of AI infrastructure.
Model and API usage: Some models have input and output tokens that are priced differently, and some model workloads are unnecessarily routed to costlier models.
Repeated and unnecessary inference: There are AI inference loops, duplication of requests, context that is needlessly sent with each request, and repeated inferences that lead to many model calls to complete a simple task.
AI inference infrastructure: There are also costs associated with AI inference infrastructure that includes the cost of GPU, memory, accelerators, and compute capacity that can be expensive due to underutilisation or poor matching of the infrastructure to the workload demand.
Supporting infrastructure: There are also costs associated with supporting infrastructure that spans from vector databases to storage and even workload orchestration, as well as monitoring services, that all scale as AI usage grows.
Agentic workloads: Workloads that are multi-step and contain several model calls to fulfill a single user request, typically building context along the way.
Only one of these items relates directly to the model's per-token pricing. All the other items relate to the design, implementation, and scaling of the workload. Because of this, there are multiple levers you can pull to control the costs associated with an AI infrastructure workload.
How to Reduce LLM Costs
Route requests to the cheapest model that can do the job
Not every request needs your most capable model. Use the least expensive model that meets your quality requirements for a specific task rather than defaulting to the most capable model for everything.
Simple tasks: classification, extraction, and formatting can often use smaller, lower-cost models.
Medium tasks: summarization and general Q&A may be suitable for mid-tier models.
Complex tasks: difficult reasoning and higher-stakes workflows may require more capable models.
Model routing can significantly reduce average inference costs when a large share of requests can be handled by less expensive models. The actual savings depend on the workload, model mix, and routing strategy. Cascading architectures can take this further by starting with a lower-cost model and escalating requests when additional capability is needed.
Cache responses instead of regenerating them
Many applications handle repetitive requests, such as customer support questions, documentation queries, and FAQs. Exact-match caching returns a stored response for identical requests, while semantic caching can reuse responses for sufficiently similar requests.
The potential savings depend heavily on how repetitive the workload is. Applications with high levels of repeated queries can achieve substantial cache hit rates and reduce inference costs significantly.
Reduce token and context usage
Every unnecessary token adds to the cost of inference. Trim system prompts, summarize long conversation histories instead of resending them in full, and limit retrieved context to information the model actually needs.
Providers also offer prompt or prefix caching for repeated context. For example, Anthropic's prompt caching can significantly reduce the cost of eligible cached input tokens compared with processing the same cached content again. The exact savings depend on the model and pricing structure.
Control output tokens
Output tokens are often priced higher than input tokens, making response length an important cost lever. Set appropriate output limits, use structured formats such as JSON when suitable, and specify the expected response length. For example, if an application needs a short classification or three-sentence answer, there is little value in generating hundreds of additional tokens.
Eliminate unnecessary LLM calls
The cheapest request is the one you never make. Look for agent loops that use an LLM for deterministic tasks, unnecessary retries, duplicate processing, and workflow steps that don't actually require a language model. Replacing appropriate LLM calls with deterministic code can reduce both inference costs and latency.
Match reasoning effort to task complexity
Reasoning-capable models can provide controls over reasoning effort or computational budget. Use higher reasoning effort when the task genuinely benefits from it and lower settings for simpler workloads. Test these changes against representative evaluations to make sure lower reasoning effort delivers acceptable quality for the task.
Batch non-real-time workloads
If a task doesn't need an immediate response, consider processing it asynchronously. Document enrichment, classification, embeddings, evaluations, and reporting can be suitable candidates for batch processing. Some providers offer batch APIs with discounts of around 50% compared with standard processing. Availability, pricing, and processing windows vary by provider.
Ask: Does this task need an answer now, or does it just need an answer today?
How to Reduce AI Infrastructure Costs
If you are self-hosting models, the efficiency of your infrastructure can help you reduce AI costs.
Improve GPU utilization: Manage idle GPUs or poorly utilized GPUs by adjusting where you can consolidate like workloads.
Optimize inference serving: Use batching, KV-cache management, and prefix caching allow for both higher throughout and computation reduction.
Use quantization where appropriate: Formats like FP8 and INT8 can reduce computation and memory requirements, but a trade-off with model quality occurs and must be tested prior to use.
Consider speculative decoding: This is where a smaller model that can generate tokens faster to help a larger model work on decoding. This can help increase throughput and efficiency of the system without reducing any costs.
Right-size cloud capacity: Using cloud capacity that automatically scales to meet demand, employing Spot instances for appropriate workloads, and avoiding idle GPUs are some ways that help control costs.
Optimize commitments last: Once all optimization is done, you can commit to predictable capacity for large discounts of 30% to 70%. Committing without optimization just locks in that waste at a fixed rate.
Building an LLM Cost Optimization Strategy
Optimization should not be a one-time process. It is a dynamic cycle that should be revised whenever your models and the way you use them changes. Here is how you should approach the process:
Measure: Know what is used. To improve costs, you must have visibility into your usage across all dimensions (consumed requests, tokens, models used, all applications used, and all the infrastructures).
Attribute: Understand and break down your costs by teams, models, and the types of workloads and applications used. Without attribution, savings will be abstract and nebulous.
Identify: Understand what workloads are driving cost the most so you know what to target for optimization first. Typically, high volume workflows, frontier models, large context windows, and repeated requests are best to target first.
Eliminate waste first: Eliminate excessive API requests, context that is completely redundant, duplicate processing, and endless agent loops that result in excessive model requests that are not justified by the task.
Optimize the workload: Assign models based on the complexity of the task and adjust reasoning effort to a level that is adequate but not excessive by using model routing, caching, batch processing, and adjusting the reasoning effort where possible.
Optimize the infrastructure: Improve GPU utilization, serving layer, apply quantization, configure auto-scaling, and improve serving utilization.
Establish governance: Set budgets, configure alerts, define usage thresholds, and establish model policies so cost discipline continues as AI usage scales.
Re-evaluate continuously: What was the most efficient choice six months ago might no longer be. New models, pricing changes, shifting workload patterns, and advances in company infrastructure can affect the economics. Assume that optimization is an ongoing practice with regular reviews as opposed to a one-time project.
How Pump Helps With LLM Cost Optimization
LLM cost optimization starts with knowing where your AI spend is actually coming from. Pump integrates with OpenAI and Anthropic to track API usage and costs across models alongside your broader cloud spend, so you can see which models and usage patterns are contributing to your bill in one place instead of jumping between provider dashboards.
From there, general optimization strategies,model routing by task complexity, caching repeated inference, and reducing unnecessary context, can reduce the workload side of the cost equation. Pump complements this by optimizing the infrastructure underneath AI applications through automated commitment management across AWS, GCP, and Azure, along with infrastructure recommendations for rightsizing and idle resources.
Pump View also provides anomaly detection and forecasting to help teams spot unexpected spending changes and plan ahead.
Pump brings AI usage and cloud cost visibility together, helping teams understand AI spend while optimizing the infrastructure that supports it.
Measuring Whether Your Optimization Is Working
Track these metrics, not just your total bill:
Cost per request and cost per successful outcome: Compare prices of different models, applications, and workloads to determine how much it costs to perform meaningful work.
Cost per workflow or task: Evaluate how much it costs to complete a task that utilizes the AI workflow and includes several different model calls, tool calls, and other supporting infrastructure.
Cost by model, team, and application: See where most of the cost is distributed and if more expensive models are being utilized to add more value to the application.
Token efficiency: Determine if a prompt is unnecessary and if responses are too long by tracking input tokens, output tokens, and tokens per task.
Model-routing distribution: This measures how evenly requests are made and if model routing is shifting duties to less expensive options.
Cache hit rate: Measure how many requests are satisfied by a hit in the cache, meaning no new inference is needed.
GPU utilization: Along with tracking self-hosted workloads, this measures how much memory is used and how many requests were served per second.
Cost against quality and latency: Ensure cost optimizations do not negatively impact response quality, accuracy, and reliability, as well as positive user experience.
Common LLM Cost Optimization Mistakes
A few patterns show up again and again:
Using your most powerful model for every request, regardless of complexity.
Optimizing token price while ignoring total workload cost.
Ignoring output tokens, which are often priced higher than input tokens.
Sending too much context on every call.
Skipping caching for workloads with repeated queries.
Treating every AI task as real-time when batch processing could substantially reduce costs.
Letting agents make unlimited retries and tool calls without appropriate limits or circuit breakers.
Ignoring GPU utilization on self-hosted infrastructure.
Optimizing individual applications in isolation instead of looking at AI spend centrally.
Measuring spend without measuring the outcomes that spend produces.
Locking into infrastructure commitments before optimizing usage.
Conclusion
LLM cost optimization is not just about securing a better per-token rate. It is about systematically taking an end-to-end approach and optimizing the request, tokens, models, inference, infrastructure, and governance. The most cost effective AI workload is the one that is not even executed. The second most cost effective workload is the one that is executed most efficiently.
Pump does not mean eliminating important decisions such as the prompt or model architecture at the application level. Rather, it provides teams with the visibility they need to understand and manage their AI and cloud spend in order to understand where they should focus optimization efforts while providing them the tools to help them manage the infrastructure-related costs which counteract their efforts, including unutilized cloud resources and untended to commitments.
Frequently Asked Questions
What is the biggest driver of LLM costs?
It depends. For context, oversized inference, unnecessary multi-step agent workflows, and oversized context windows are some of the largest contributors to excess spend, but token pricing can also play an important role. The cost of a request is often determined by the number of calls needed as well as the number of tokens consumed, and not just by the per-token rate.
How does model routing reduce LLM costs?
Depending on requirement and complexity, model routing can help optimize costs by sending requests to the appropriate model (i.e. model sensing). This approach reserves the more sophisticated and capable models for truly sophisticated and complex tasks, thereby rationalizing cost, while sending less complex requests to lower cost alternatives. Model routing helps optimize cost savings on a workload-specific basis. For example, in Pump’s Daemo.ai case study, model routing was attributed for roughly saving 40% on Bedrock model spending.
Does response caching reduce LLM costs?
Yes. Caching can significantly reduce calls to LLMs on an application level. Exact-match caching can eliminate calls for identical requests. Semantic caching can optimize responses for similar requests. The benefit of both caching techniques is heavily dependent on the workload. HighLY repetitive workloads benefit from high cache hit ratios, while unique workloads incur less benefit.
How can token compression reduce AI costs?
Number of tokens processed per request can be reduced significantly by limiting the length of system instructions, summarizing conversation history, and limiting the context retrived. Since the output tokens are priced higher than the input tokens, limiting the length of the response can also provide a considerable savings.
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