Cost Control: Scaling AI Without Going Broke

Modern large-language models (LLMs) deliver remarkable capabilities, but their per-token pricing can quickly escalate operational costs, especially as usage scales. Many teams struggle to manage these expenses effectively, risking budget strain in production. This guide provides AI leaders with eight practical levers—from model selection and prompt engineering to commercial strategies—and outlines a phased optimisation plan to strategically reduce AI operational costs without sacrificing quality or development velocity.

In this guide, you will learn:

1. Key strategies to control AI operational costs.
2. Practical levers for reducing expenses without sacrificing quality.
3. A phased approach to implement cost optimisation effectively.

Why Cost Matters Now: A New Economic Reality

For most of software history, the cost of running software was so incredibly low it was often considered a marginal expense, barely factoring into financial calculations compared to the high cost of development. GenAI, and particularly LLMs, fundamentally reverses that ratio. The economics of working with the latest AI models are starkly different:

• Per-Token Pricing: Most powerful models charge based on the amount of data processed (input and output tokens), meaning each request has a tangible cost.
• Premium Models: State-of-the-art “frontier” models can be relatively expensive per request.
• Scaling Usage: As AI features become integral, the volume of requests can grow rapidly.

These factors combine to make AI operational costs a significant, board-level concern. Engineering leaders who can effectively navigate this new financial landscape—steering spending prudently while delivering innovative features—earn strategic credibility. Those who cannot risk inviting budget freezes or project cancellations.

A Disciplined Approach to Cost Optimisation

The most significant mistake teams make is attempting cost control prematurely or haphazardly, intermingling it with feature development and quality assurance. A disciplined, sequential approach is paramount:

1. Prove Value and Quality First: Before focusing heavily on cost, develop your AI feature to a point where it demonstrably meets functional requirements and achieves a stable, acceptable quality level. Establish robust evaluation suites and metrics to quantify this quality. While you don’t want to build something that will bankrupt you from day one, initial efforts should confirm you’re heading in the right direction functionally.

2. Hold Functionality and Quality Constant: Once your feature is stable and its quality benchmarked, then begin cost optimisation. Crucially, any cost-reduction measure must be evaluated against its impact on the established functionality and quality baselines. If an optimisation degrades these, it’s not a true win.

3. Optimise Iteratively: Treat cost reduction like performance tuning. Formulate a hypothesis (e.g., “Using a smaller model for this task will reduce cost without impacting quality”), change one variable at a time, measure the impact on both cost and quality, and then decide whether to commit or revert the change.

4. Instrument Everything: You cannot control what you cannot measure. Implement comprehensive logging and monitoring to track token counts (input and output), latency, model versions used, and associated costs for every AI request. This data is vital for identifying cost drivers and verifying the impact of optimisations.

No Silver Bullet: A Multi-faceted Strategy for Cost Reduction

Teams new to building with AI often hope for a single, magical solution to high costs—a cheaper model to swap in, or one new technique to adopt. In reality, effective AI cost control rarely comes from a single change. Instead, it’s the result of systematically exploring and applying a range of strategies. Each offers a lever to potentially reduce expenses, and often, the most significant savings come from combining several approaches. The key is to experiment with all available options, always measuring against your constant quality and functionality baseline.

Eight Cost Levers to Pull

1. Model Selection

It’s often wise to start development with a top-tier, powerful (and potentially expensive) model to establish that your use case is feasible and to achieve baseline quality quickly. Once you have robust evaluations confirming quality:

• Systematically test progressively smaller, faster, or cheaper models (e.g., from GPT-4.1 to GPT-4.1-mini, or Gemini-2.5-Pro to Gemini-2.5-Flash).
• For each model, run your full evaluation suite.
• Identify the least expensive model that still meets your quality threshold.
• If there’s a small quality gap with a cheaper model, explore if targeted prompt engineering, fine-tuning, or improvements in your Retrieval Augmented Generation (RAG) system can bridge it, rather than defaulting back to the more expensive model.

Rule of thumb: A significant percentage of use-cases do not require the absolute largest or most expensive model once prompts, data, and retrieval mechanisms are well-tuned.

Read more about how to approach model selection methodically: A Structured Approach to Selecting AI Models for Business-Critical Applications.

2. Prompt Engineering

LLMs can process vast amounts of information—contexts of up to a million tokens in some cases. While convenient during development, this can lead to unnecessary expense. Scrutinise both your prompts and the data you send:

• Prompt Pruning:
  • Delete boilerplate instructions or examples the model likely “knows” from its training.
  • Convert verbose background text in prompts into concise bullet points or summaries.
  • Replace static reference data within the prompt with a URL or an identifier if your architecture allows server-side expansion or retrieval.
• Data Reduction:
  • Analyse if all the data being sent with each request is strictly necessary for the desired output.
  • Refine retrieval methods (see next point) to be more selective.
  • If you’re sending extensive user history or document content, can it be summarised or truncated without losing essential context?

A reduction from, for example, a 100,000-token context to a 10,000-token context by being more selective can yield a 90% cost saving on that portion of the request, multiplying significantly at scale.

3. Retrieval Precision

In RAG systems, sending excessive or irrelevant context to the LLM is a common source of inflated costs and can also degrade response quality.

• Tune similarity search thresholds and the number of retrieved chunks (top-k) to fetch only the most relevant information.
• Consider hybrid search (combining dense vector search with keyword-based search) to improve relevance and reduce noise.
• Optimise chunking strategies for your documents. Very large chunks might pull in irrelevant material, while very small chunks might miss necessary context.
• Aim to narrow down retrieved information to only what is essential for the LLM to perform its task, not everything remotely available.

Strive for focused retrieval that provides just enough context, typically aiming for well under a few thousand tokens of retrieved material per request for many knowledge base applications.

4. Workflow Decomposition

Complex tasks often don’t require the most powerful (and expensive) model for every single sub-component. Monolithic prompts that try to do everything at once force you to pay premium rates for even simple parts of the workflow. Instead:

1. Break Down the Task: Identify distinct steps in your AI process (e.g., intent recognition, data extraction, summarisation, sentiment analysis, final response generation).
2. Use Appropriate Models per Step:
   • Employ smaller, faster, cheaper models for simpler sub-tasks (e.g., a small classification model to route a query).
   • Reserve the most powerful models only for the steps that genuinely require their advanced reasoning capabilities.
3. Cache Intermediate Results: If a sub-task’s output can be reused (e.g., a summary of a document that’s queried multiple times), cache it. This avoids reprocessing and saves costs on subsequent, related requests. Store these results in your database or a cache layer.

This “surgical” routing and caching approach can significantly reduce overall costs, often by half or more, while potentially improving latency for simpler parts of the workflow.

5. Pre-processing

Many AI-driven computations don’t need to happen in real-time when a user makes a request.

• Ahead-of-Time Processing: Identify any data processing or content generation that can be done offline or in advance. For example, compute embeddings for all your documents, generate summaries of static content, or pre-calculate sentiment scores during periods of low system load or using cheaper batch processing (see next point).
• Store and Serve: Store these pre-processed artefacts in your database or a suitable cache. At runtime, your application can then retrieve these results instantly, avoiding expensive LLM calls.
• Cache Deterministic Chains: If parts of your AI workflow are deterministic (e.g., formatting output, simple data transformations), cache their results aggressively. These should run once per unique input, not per user query.

If a large portion of your AI interactions can leverage pre-processed or cached data, the overall cost per user-facing request can plummet.

6. Batched Asynchronous Calls

Many LLM providers, including OpenAI, offer significant discounts (e.g., OpenAI’s Batch API offers a 50% discount) for API calls that can be submitted in large batches and processed asynchronously, typically with a commitment to return results within a longer timeframe (e.g., 24 hours).

This is ideal for:

• Indexing document backlogs or generating initial embeddings.
• Generating training data for fine-tuning.
• Periodic analysis, reporting, or compliance checks that are not time-sensitive.
• Any pre-processing tasks that can be queued and handled offline.

If your workflow can be structured to accommodate this latency, batching can be a straightforward way to halve costs for applicable tasks. This might sometimes require decomposing workflows to isolate parts that can be batched.

7. Context Caching

Some LLM providers offer features to cache parts of the prompt (the “context”) that remain constant across multiple, consecutive queries. If the initial part of your prompt is identical in a sequence of calls, the provider might only charge you fully for it on the first call, and then offer reduced pricing for the cached tokens on subsequent calls.

To leverage this:

• Ensure the large, immutable part of your prompt (e.g., a system message, a large document being queried) is exactly the same across these calls.
• Typically, this constant part needs to be at the very beginning of your prompt. Some providers may offer ways to explicitly mark sections for caching.
• Check your provider’s documentation for specifics on how their context caching works and how to enable it.
• Monitor your billing dashboards for “cached tokens” or similar metrics to validate the savings.

This is particularly effective for scenarios like repeated Q&A over a single, large document or maintaining conversational context where a significant portion of the prompt history is repeated.

8. Commercial Strategy

Beyond technical optimisations, your commercial arrangements can also yield savings:

• Commit and Save: Many providers offer discounts for committed usage levels. If you can forecast your AI spend with reasonable accuracy, explore pre-paying or committing to a certain volume to unlock lower per-token rates.
• Run Open Models: For very high volumes or when data residency/privacy is paramount, hosting openly-licensed models (e.g., Llama 3, Mixtral) on your own dedicated GPUs (on-premise or reserved cloud instances) can become more economical than paying per-API call, especially if your monthly API spend exceeds certain thresholds (e.g., $10k-$20k). This requires infrastructure and MLOps expertise.
• Shop Around & Stay Flexible: The AI model market is highly competitive, with new models and pricing structures emerging frequently. Design your systems with a lightweight abstraction layer around model calls, making it easier to switch providers or models to take advantage of new price cuts or capability improvements.

Putting It All Together: A Phased Optimisation Plan

1. Instrument & Baseline: Implement thorough monitoring to understand your current AI costs and performance metrics.
2. Freeze Quality Bar: Establish and freeze your evaluation suite to ensure quality doesn’t regress during optimisation.
3. Quick Wins (Levers 1-3, 7): Start by right-sizing models, pruning prompts and data, improving retrieval precision, and implementing provider context caching. These often yield immediate savings with relatively minor code changes.
4. Deeper Refactoring (Levers 4-6): For more substantial savings, refactor monolithic workflows into decomposed steps. Implement pre-processing, strategic caching of intermediate and final results, and leverage batch/asynchronous APIs where appropriate.
5. Strategic Sourcing (Lever 8): Regularly review your commercial agreements. Negotiate committed use discounts or evaluate the ROI of hosting open models as your usage scales.
6. Review and Iterate Periodically: AI model capabilities are increasing, and costs per capability are decreasing rapidly. The optimal cost configuration today might be suboptimal in a few months. Revisit your choices and assumptions regularly.

Looking Ahead: Riding the Cost Curve Down

A consistent trend in the AI space over the past few years has been a rapid decrease in cost per unit of capability—roughly a 10x improvement every 12 to 18 months for comparable tasks. This doesn’t mean you should build systems that are unprofitable today, counting entirely on future cost drops; that’s generally not a sound strategy.

However, you can factor this trend into your planning:

• Aim to build AI features that are at least marginally profitable, or provide clear strategic value, with current model costs.
• Be reasonably confident that, over time (e.g., within a year or so), your margins will improve simply due to falling underlying model costs, even if you make no other changes.
• Structure your code and architecture to be flexible, allowing you to easily adopt newer, more efficient models or dial down aggressive cost control measures as they become less critical.

Key Take-aways for AI Leaders

• Own the Cost Metric: Treat “cost per successful AI interaction” as a key performance indicator, alongside latency and quality.
• Separate Concerns Rigorously: Complete feature development and establish quality benchmarks before diving deep into cost optimisation. Use your frozen test suite to guard against regressions.
• Embrace Incremental Gains: There’s rarely a single silver bullet. Significant cost savings usually come from methodically applying and stacking several of the levers discussed.
• Architect for Agility: The AI landscape (models, pricing, capabilities) is evolving rapidly. Design your systems to allow for easy model swapping and adaptation to new provider offerings or cost structures.
• Communicate in Business Terms: Translate token counts and technical optimisations into tangible financial impact (e.g., “This change reduced the cost of X feature by Y%, saving Z dollars per month”). Executives care about margin and ROI.

By applying these principles with discipline, AI leaders can ensure their projects are not only innovative and impactful but also financially sustainable, earning the trust and confidence of both their technical teams and the broader business.

• Communicate in Business Terms: Translate token counts and technical optimisations into tangible financial impact (e.g., “This change reduced the cost of X feature by Y%, saving Z dollars per month”). Executives care about margin and ROI.

By applying these principles with discipline, AI leaders can ensure their projects are not only innovative and impactful but also financially sustainable, earning the trust and confidence of both their technical teams and the broader business.