AI projects rarely fail because the model “isn’t smart enough.” They fail because the money meter spins where few teams are watching: GPU hours, token bills, data egress, and serving inefficiencies that quietly pile up after launch.

The real challenge for CTOs in 2025 and the foreseeable future is not choosing the right model, but owning the full cost surface—from inference and networking to evaluation pipelines and architectural trade-offs that balance latency, throughput, and cost.

Inference is the real bill, not training

Across the industry, inference dominates lifetime spend. Training is episodic; inference happens every time a user interacts with the system. According to research presented at OSDI 2024, the focus is on how serving architectures manage throughput–latency trade-offs in inference workloads. Improving one usually worsens the other unless the serving pipeline is rebuilt around adaptive batching and memory management.

Two practical consequences follow:

1. Capacity planning must be inference-first. Peak QPS, average output tokens, and tail latency SLOs dictate GPU needs far more than parameter count.
2. Serving design is an architectural decision, not a backend detail. Techniques like continuous batching, paged key-value caching, speculative decoding, and quantization can reduce costs by up to 4× on the same hardware (see vLLM and NVIDIA TensorRT-LLM documentation).

The four hidden lines on your AI cost sheet

1) Accelerator hours (and how you buy them)

On-demand GPU pricing differs drastically across clouds and regions. For example, AWS p5/H100, Azure NC H100, and GCP A3 instances vary roughly 40–60 % depending on region and availability. Google Cloud’s TPU families (v5e, v5p, Trillium) also post transparent per-chip-hour rates, which shift economics for certain workloads.

Spot or preemptible capacity cuts costs by 60–90 % but introduces interruption risk—two-minute notice on AWS, roughly thirty seconds on GCP. For stateless or resilient jobs, that trade is worth it if your orchestration and checkpointing can recover quickly.

Action for CTOs: separate training, batch/offline inference, and interactive inference pools. Use spot where safe, and pin business-critical tiers to on-demand or reserved capacity. Most pipelines aren’t interruption-tolerant until you explicitly test them.

2) Networking and data egress

Ingress is usually free; egress almost never is. Every cloud provider bills for data leaving the region, and cross-region flows multiply the cost. Even though AWS and Google introduced limited migration-related egress waiver programs in 2024 linked to specific regulatory and competition rulings, those programs are narrow, migration-focused, and conditional, and they do not apply to everyday cross-region or cross-cloud traffic.

Action: co-locate model servers, vector stores, and data pipelines in the same region. Avoid RAG architectures that pull embeddings across regions on every query—it’s a silent, repeating egress charge that remains billable because these programs do not cover operational traffic.

3) Token-metered API spend

When you use hosted model APIs, your unit economics depend on input/output tokens, context length, and caching. OpenAI, Anthropic, and others publish pricing by token and model tier.

Small design choices—prompt shaping, output limits, and cache reuse—change cost curves by multiples.

Action:

• Cap maximum output tokens per route.
• Reuse prompts with caching hints when supported.
• Implement multi-model routing: lightweight models for standard calls, larger models only when confidence or context requires it.
  This pattern alone often cuts API spend by 50 % without measurable quality loss.

4) Evaluation, guardrails, and “non-feature” workloads

Safety scanning, red-team tests, and automated evaluation loops have become continuous workloads. UK and EU institutions increasingly expect this kind of evaluation-centric assurance, especially for high-risk or regulated deployments, where post-market monitoring and logging obligations apply under frameworks such as the EU AI Act.

Action: treat evaluations as a first-class workload with a defined cadence and budget. Schedule them on cheaper, preemptible capacity rather than letting them overlap with latency-sensitive inference windows.

The serving blind spot: where DevOps playbooks fall short

Traditional DevOps optimizes for availability and latency. AI serving introduces a third variable—GPU utilization. Under-utilized accelerators waste budget; over-batched systems break latency SLOs.

Modern serving frameworks have matured around these challenges:

• Continuous batching keeps GPUs busy by merging incoming requests into active decoding loops (vLLM, DeepSpeed FastGen).
• Paged key-value caching reduces memory fragmentation and doubles throughput at constant latency.
• Custom attention kernels and inflight batching (TensorRT-LLM) push throughput further on NVLink-based servers.

Take action: choose a serving framework early, benchmark with your real traffic patterns, and track throughput at 95th percentile latency as your core cost-efficiency metric.

Design choices that quietly burn money

Long contexts that never pay back. Large context windows sound attractive, but they increase memory pressure and slow prefill. Unless you genuinely use them, shorter contexts plus RAG summaries are cheaper and faster.

Cross-region RAG. Pulling documents or embeddings across regions on every call is a textbook egress trap. Replicate data locally instead; these flows are not covered by the 2024 migration-fee waiver programs and remain fully billable.

Unmanaged spot usage in interactive paths. Spot is fine for batch or evaluation jobs, not for live traffic without failover logic.

Token-blind product features. Features like “show reasoning traces” inflate output tokens per user. Make token cost visible in dashboards so PMs see cost per interaction.

A cost control blueprint that actually works

1) Make cost an SLO alongside latency and quality

Publish three SLOs per route: latency, quality, and cost per successful call. If cost is invisible, it will drift.

2) Choose a serving stack that exposes the right levers

Use frameworks offering continuous batching, KV caching, quantization, and inflight batching. Benchmark before committing.

3) Separate pools by interruption tolerance

Interactive → on-demand or reserved.
Batch → spot with checkpointing.
Training/evaluation → preemptible with retries.

4) Keep the bytes local

Run the model, vector store, and document cache in the same region or availability zone. Measure egress precisely.

5) Introduce budget-aware decoding and routing

Implement token caps, prompt caching, and fallback models. Measure real cost per feature.

6) Adopt FinOps for AI with a real data model

Normalize billing using the emerging FinOps FOCUS schema so finance and engineering see the same metrics. Use FinOps Foundation’s guidance to forecast training versus inference spend.

7) Measure by model shape

Different architectures—Mixture-of-Experts, RAG, long-context—shift cost drivers differently. Instrument them separately.

A 30-60-90 plan for taking back control

💡 Days 0–30

• Instrument token in/out and cost per route.
• Replay traffic traces against vLLM and TensorRT-LLM.
• Map every data flow that crosses a billable boundary.

💡 Days 31–60

• Deploy continuous batching and quantization.
• Re-place RAG stores to remove cross-region flows.
• Move evaluation jobs to preemptible capacity.

💡 Days 61–90

• Centralize cost data under FOCUS or another unified cost schema.
• Secure reserved capacity for steady tiers.
• Publish a monthly AI cost review with finance and engineering jointly.

Watchlist: facts that move your budget in 2025 and 2026

👉 Accelerator pricing dispersion: H100 hourly rates differ by region and cloud. Benchmark quarterly.
👉 Token pricing revisions: Vendors adjust rates and caching discounts often—monitor official pages.
👉 Egress policy shifts: Regulatory updates under the EU Data Act and UK competition rulings may change regional transfer costs, but effects so far apply mainly to migration scenarios, not daily operational traffic.
👉 Serving breakthroughs: New batching and attention techniques can improve efficiency without new hardware.
👉 FinOps standardization: The FOCUS schema is gaining adoption but not yet universal, enabling unified cost visibility.

Shadow compute budgets aren’t a purely finance issue but an architecture issue. Treat cost as a first-class SLO, deploy serving frameworks with explicit utilization levers, keep data local, and instrument every token and gigabyte that leaves your stack.

The goal isn’t merely to shrink your bill, it’s to make cost predictable enough that you can scale AI usage confidently.

Want an outside pair of hands?

Blocshop designs and delivers custom software and AI integrations with measurable performance, quality, and cost targets.

If you’d like a second opinion on your serving stack, GPU plan, or token economics, we can help.
👉 Schedule a free consultation with Blocshop