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AI Economics

Why the real cost of enterprise AI is inefficient engineering, not just tokens.

The models are becoming cheaper. Poor AI engineering is what makes enterprise AI expensive.

Business valueApplications & workflowsAgentsRetrieval & contextInference engineModel layerCompute infrastructure
Higher throughputLower latencyLower infrastructure cost potentialGreater reliabilityMeasurable business value
01

The paradox

Model prices are falling fast, yet many enterprise AI bills are rising.

Cost of querying a model

At GPT-3.5-level MMLU performance

Price (US$ per million tokens)US$20.00US$0.07>280×cheaperNov 2022Apr 2023Sep 2023Feb 2024Jul 2024Oct 2024

Cost fell from about US$20 to US$0.07 per million tokens - more than a 280× decline. Source: Stanford HAI AI Index 2025.

Enterprise AI bills often rise because usage expands

More use casesLonger contextwindowsAgents & reasoningExpectations &growing use2023202420252026

Lower unit prices can coincide with higher total spend as workloads, reasoning depth, context and agent systems grow.

Model performance is converging

Leading providers cluster on Arena-style ratings

ABCDE

When model access is less differentiating, optimisation, reliability and economics matter more. Source: Stanford HAI AI Index 2026.

02

Where enterprise AI cost is created

Inference cost is influenced by decisions across the stack, not just the model or token price.

01

Model choice

  • Model size and architecture
  • Open vs closed models
02

Context length

  • More context increases compute and memory use
03

Retrieval quality

  • Relevant retrieved data reduces wasted compute
04

Batching & scheduling

  • Larger batches and smarter scheduling improve GPU utilisation
05

KV cache management

  • Critical for memory use and throughput
06

Decoding strategy

  • Speculative decoding and related techniques can reduce latency
07

GPU utilisation

  • Under-used GPUs increase cost per output
08

Observability & governance

  • Measurement, controls and workload routing sustain efficiency
03

Where the money disappears

Common sources of avoidable cost in enterprise AI.

1

Wrong model for the task

Using larger, more expensive models than needed.

2

Oversized context windows

Unnecessarily long context increases token usage and latency.

3

Inefficient RAG / retrieval

Poor retrieval quality produces more tokens and lower accuracy.

4

Repeated inference & uncontrolled experimentation

Iteration without guardrails can consume significant GPU time.

5

Idle or fragmented GPU capacity

Low utilisation without pooling and dynamic scaling.

6

Unoptimised serving stack

Inefficient settings, KV-cache, batching and routing increase latency.

04

AI FinOps: a continuous optimisation cycle

Maximise business value per dollar spent.

AI
FinOpsContinuous optimisation for higher ROI

Observe

Workloads, costs, quality, usage

Measure

Tokens, GPU utilisation, latency, cost/request

Route

Select the best model and inference path

Optimise

Tune batching, KV-cache and routing

Govern

Cost caps, policies and production readiness

Re-measure

Track improvement and business impact

05

The optimisation stack

Multiple layers to tune - from compute to business outcomes.

Business applications

User experiences and business outcomes

Agents & workflows

Task decomposition, tool use and orchestration

Retrieval / RAG

High-quality, relevant context

Inference layer

Model routing, speculative decoding, KV-cache and batching

Model layer

Model selection, quantisation and adapters

Compute / GPU layer

Efficient utilisation, pooling and autoscaling

Key optimisation techniques

  • Governance, observability and cost management
  • Workflow design, tool selection and step optimisation
  • Retrieval quality, re-ranking and context discipline
  • Speculative decoding, KV-cache management, batching and concurrency
  • Model routing, quantisation and adapters
  • Dynamic scaling, workload pooling and higher GPU utilisation
06

Solveworx + T-Flux Ultra

An AI optimisation service: expertise and platform working together.

Expertise

AI science & optimisation

  • AI & data science specialists
  • Computational mathematics
  • Model architecture
  • Optimisation algorithms
  • Continuous tuning
Expertise
× Platform
=
AI Economics

T-Flux Ultra

Orchestration platform

  • Multi-model orchestration
  • Governed retrieval
  • Inference controls
  • Observability & analytics
  • Workload routing
07

From token cost to business value

A governed, optimised path to measurable outcomes.

Raw usage

Unrestricted experimentation can drive unpredictable costs.

Governed workload

Policies, model routing and context discipline.

Optimised inference

Right model, right settings, efficient serving.

Predictable cost

Fixed monthly pricing and cost caps.

Measurable business outcome

Higher productivity, better service, clear ROI.

Typical outcomes

  • Lower GPU utilisation
  • Lower token consumption
  • Higher throughput
  • Faster response times
  • Predictable operating expenditure
  • Improved ROI

Evidence base (Download the white paper report for all citations & references)

1

Stanford HAI AI Index 2025Trends in AI model pricing and inference costs.

2

Stanford HAI AI Index 2026Model performance convergence across leading providers.

3

FinOps FoundationOptimizing GenAI Usage: industry guidance on inference spend and GPU utilisation.

4

Xia et al. (2024)Unlocking Efficiency in Large Language Model Inference.

5

Jiang et al. (2026)Towards Efficient Large Language Model Serving.

6

Li et al. (2024)A Survey on Large Language Model Acceleration based on KV Cache.