KI-Inferenzkosten senken, ohne Qualität einzubüßen

AI inference can become one of your largest variable costs. Without discipline, teams default to the biggest, most capable model for every request and wonder why the bill is six figures. Cost optimization isn't about cutting corners — it's about right-sizing and efficiency.

Here are the levers we use to cut AI costs without degrading user-facing quality.

Model selection and tiering

Not every task needs GPT-4. Use the smallest model that meets your accuracy and latency requirements. Classification, extraction, and simple summarization often work well with smaller or cheaper models. Reserve top-tier models for complex reasoning, long-context synthesis, or high-stakes decisions.

Implement a routing layer: classify the request first (e.g. with a tiny model or rules), then send to the appropriate model. You'll often cut cost by 50–70% while keeping quality for the hard cases.

Prompt and context optimization

Token count drives cost. Shorten system prompts to the minimum that preserves behavior. For RAG, retrieve only the passages you need and trim context to a fixed token budget. Use structured output formats that are concise; avoid asking for long prose when a short answer suffices.

Experiment with prompt compression techniques: summaries instead of raw context, or semantic compression. Measure the impact on quality; sometimes a 30% context reduction has negligible effect on output.

Caching and deduplication

Identical or near-identical requests are common — same question, same context. Cache responses keyed by (model, prompt hash, params). Set a TTL that matches how often your data or instructions change.

Cache embeddings separately. If you use the same documents across many queries, compute embeddings once and reuse. Embedding APIs are cheaper than completion APIs but still add up at scale.

Batching and async processing

Where latency allows, batch requests. Some APIs offer batch endpoints with lower per-token cost. For background jobs (summarization, classification over large sets), process in batches and use async APIs to avoid holding connections open.

Queue non-real-time work and process during off-peak or with lower-priority capacity. Smoothing load can reduce the need for over-provisioning and sometimes qualifies you for better pricing.

Distillation and smaller models

For repeated tasks with stable criteria, consider distilling a large model into a smaller one: generate training data from the large model, then fine-tune a small model to mimic it. Inference cost drops dramatically; quality often stays high for that specific task.

Evaluate open-weight models for fine-tuning. Many tasks can be handled by a 7B or 13B parameter model fine-tuned on your data, at a fraction of the cost of repeated API calls to a 70B+ model.