Cutting your LLM bill without losing quality

Most organizations discover AI inference costs the hard way. A proof of concept runs on GPT-4 at comfortable volumes, leadership greenlights production, and the monthly bill quietly climbs to five or six figures. The instinct is to throw a cheaper model at the problem. But cheaper often means worse — and worse means the project gets killed.

There is a better path: keep the quality, cut the spend. Here are the levers that actually work.

Right-size the model first

The single biggest cost lever is choosing the right model for the job. A 70B-parameter model is not inherently better than a 7B one — it depends entirely on the task.

For structured extraction (pulling dates, amounts, or entities from documents), a well-prompted 7B model matches a 70B on accuracy while running 10× faster. For open-ended reasoning or nuanced generation, the larger model earns its keep.

The practical approach: benchmark two or three model sizes on your actual data, not on public benchmarks. Measure task-specific accuracy, not general capability. Most teams find that 60–80% of their inference volume can shift to a smaller model with no measurable quality loss.

Quantization: same model, less memory, faster inference

Quantization reduces the precision of model weights — from 16-bit floating point to 8-bit integers (INT8) or even 4-bit (INT4). The model stays the same; it just uses less memory and runs faster.

The quality trade-off is smaller than most people expect:

| Precision | Memory (7B model) | Relative speed | Quality loss | |---|---|---|---| | FP16 (baseline) | ~14 GB | 1× | — | | INT8 | ~7 GB | 1.5–2× | < 1% on most tasks | | INT4 | ~4 GB | 2–3× | 1–3%, task-dependent |

INT8 is almost always safe. INT4 is worth testing — on structured tasks it often works; on creative generation it can introduce subtle degradation.

The real win: quantization lets you run a larger model on the same hardware, or the same model on cheaper hardware. A quantized 13B on a single GPU often beats a full-precision 7B on quality while costing the same to run.

Batching: amortize the fixed costs

Every inference request pays a fixed cost: loading the model into memory, setting up the computation graph, allocating GPU resources. Processing one request at a time wastes most of that setup.

Continuous batching groups incoming requests and processes them together. A GPU that handles 10 requests per second individually can handle 40–80 batched — without any quality change.

This matters most for throughput-oriented workloads: batch processing documents overnight, running classification on a backlog, or serving internal tools where 200ms of latency is acceptable.

For real-time applications, dynamic batching with short time windows (5–20ms) captures most of the benefit with minimal added latency.

Cache prompt prefixes

Many applications send the same system prompt, few-shot examples, or document context with every request. Each time, the model re-processes those tokens — and you pay for it.

KV-cache reuse stores the intermediate computations for shared prefixes. When the next request arrives with the same prefix, the model skips straight to the new tokens. For applications with long system prompts (common in enterprise settings), this cuts per-request cost by 30–50%.

Most inference frameworks support this natively — vLLM, TGI, and TensorRT-LLM all implement some form of prefix caching. The key is structuring your prompts so the shared prefix comes first and varies last.

Own the infrastructure, own the cost curve

API pricing is simple: you pay per token, and the provider sets the price. When volumes are low, this is the right model. When volumes grow, the math changes.

A single NVIDIA A100 GPU costs roughly €15,000–20,000. Running a quantized 13B model on it, you can serve approximately 1 million requests per day. At typical API rates, that same volume would cost €3,000–10,000 per month.

The GPU pays for itself in two to six months. After that, inference is effectively free — just electricity and maintenance.

This is not about dogma. API access makes sense for experimentation, low-volume use cases, and tasks that genuinely need frontier models. But for the 80% of production inference that runs a predictable model on predictable data, owning the compute is dramatically cheaper.

Where to start

1. Audit your current spend. Break it down by model, by use case, by volume. Find the 20% of use cases that drive 80% of cost.
2. Benchmark smaller models on your highest-volume tasks. You will be surprised how often a 7B matches a 70B.
3. Quantize aggressively. Start with INT8 everywhere, test INT4 on structured tasks.
4. Batch everything that tolerates latency. Even 50ms of batching window makes a significant difference.
5. Model the break-even point for owned hardware. If your monthly API bill exceeds the cost of a GPU, the math is clear.

We help organizations run this playbook — from the initial audit to production deployment on owned infrastructure. The first conversation is free.

Book a call to scope your cost reduction roadmap.