AI inference costs compound fast. At 1M daily inferences, a 20% cost reduction is worth $300k+ annually. The companies we work with have cut inference spend by 78% on average using five strategies — none of which require changing their model.
Strategy 1: Dynamic Batching
Most teams either don't batch (serving one request at a time, wasting 60–80% of GPU capacity) or use fixed batching (waiting for a full batch, adding 50–200ms latency). Dynamic batching solves both: batch requests together when they arrive within a configurable window (2–8ms), then process immediately.
Improvement: 3–4x GPU utilization increase, translating directly to 3–4x cost reduction per inference. This is the single highest-ROI optimization for most workloads.
Strategy 2: INT8/INT4 Quantization
Already covered in depth in our quantization guide. The cost angle: quantized models run 2–4x faster, meaning you can serve the same throughput with half the hardware. On an A100 cluster at $3/GPU-hour, halving hardware needs = $1.5/GPU-hour saved, continuously.
Combined with our GPTQ pipeline: 4x compression, less than 0.3% accuracy loss, 2.8x throughput improvement.
Strategy 3: Right-Size Your Hardware
Most teams default to A100 or H100 for all inference. For models under 7B parameters at low concurrency, this is 3–10x overpaying. A 2-socket Intel Xeon Platinum costs $0.28/hour vs. $3.20/hour for an A100 — and can serve Llama 3 8B at 18ms P99.
Segment your workloads: use GPU for latency-critical or large-model requests, CPU for asynchronous or small-model requests. Hybrid routing can cut your average cost per inference by 40–60%.
Strategy 4: KV-Cache Reuse
If your application has repeating system prompts (e.g., "You are a helpful assistant. Here are your instructions..."), you're paying to compute the same KV values millions of times. Prefix caching stores the KV state for fixed prompt prefixes and reuses them. Cost savings: 20–40% for typical LLM chat applications, 60%+ for applications with long fixed system prompts.
Strategy 5: Spot / Preemptible Instances with Checkpoint Routing
Inference is stateless. If a worker is preempted, the request can be re-routed to another worker with no data loss (just a retry). Using spot instances (70% cheaper than on-demand) with Inferex's fault-tolerant routing layer delivers on-demand reliability at spot pricing.
Combined with preemption-aware auto-scaling (proactively spinning up on-demand capacity when spot interruption rate exceeds 15%), this approach achieves 95%+ spot usage with 99.9%+ request success rate.
Putting It Together
We've seen teams combine all five strategies and reduce cost per 1M inferences from $4.20 to $0.94 — a 78% reduction. The strategies are additive and independently implementable. Start with dynamic batching (highest ROI, lowest risk), then add quantization, then evaluate hardware right-sizing.