Your AI proof of concept costs $1,500 a month. Leadership loves it. You get approval to roll it out to production. Six months later you’re staring at a bill for over a million dollars annually.
Welcome to the standard experience for teams moving AI from pilot to production. The problem? Proof-of-concept costs have almost nothing to do with production costs.
Research from Deloitte identifies a threshold where this becomes unavoidable: when your cloud costs hit 60-70% of what equivalent on-premises systems would cost, you’re at a tipping point. Past that point, you’re burning money staying in the cloud.
This cost spiral is a critical piece of why AI infrastructure investments aren’t delivering the expected returns. In this article, we’re going to walk through why inference economics matter, why PoC budgets lie to you, what the 60-70% threshold actually means, how to calculate total cost of ownership properly, and what you can do about spiralling costs without gutting your product.
What is Inference Economics and Why Does it Matter for Production AI?
Inference economics is the financial reality of running AI models in production. Every time your model generates a response, you pay. Unlike training costs—which happen once and you’re done—inference costs happen constantly, with every API call, and they multiply as your product gets used.
Training a model might cost you $100,000. That’s a one-time hit. But inference at $0.01 per query scales to $10,000 a month when you’re handling a million queries. At 10 million queries you’re at $100,000 monthly. The cost per inference has dropped 280-fold over the last two years, which sounds great until you realise usage has grown faster than the cost reduction.
GPT-4 class models cost around $0.03-0.06 per 1,000 input tokens and $0.06-0.12 per 1,000 output tokens. For a chatbot handling 5 million conversations a month with an average of 500 tokens per conversation, you’re looking at $150,000-300,000 monthly just for the inference calls.
This volume effect creates another problem: machine learning introduces non-linear cost behaviour. A model that costs $50 per day to serve 1,000 predictions doesn’t simply cost $5,000 to serve 100,000. It could cost far more because of bottlenecks in compute, memory, and data transfer. What you miss out on asking price can you make up on volume? Not in this game.
Why Do Proof-of-Concept Costs Fail to Predict Production Expenses?
Your PoC runs on free tiers, handles maybe 10 test users, and processes a few thousand carefully controlled queries. Production has thousands of real users, unpredictable traffic spikes, retry loops when things fail, and a constantly expanding feature set.
Recent data shows a 717x scaling factor between PoC costs ($1,500) and production costs ($1,075,786 monthly). That’s not unusual. It’s typical.
Free tier illusion. Vendors want you to use their product, so they offer generous PoC credits. Those disappear the moment you go to production pricing. What looked like a $500/month cost at pilot scale becomes $15,000/month at production pricing before you even account for volume increases.
Controlled usage vs organic usage. Your pilot has 10 testers who use the feature when asked. Production has thousands of users who bash on it whenever they want, creating traffic patterns you never saw in testing. Peak loads are often 10-20x average loads, and you have to provision infrastructure for peaks, not averages. That’s reality.
Single feature vs feature creep. The PoC tests one use case. Production demands more. Marketing wants personalised recommendations. Sales wants lead scoring. Each new feature adds inference load, and organisations frequently report AI costs increasing 5-10x within a few months as features multiply.
Error multiplication. Production has retry logic. When an API call fails, your code tries again. Maybe multiple times. A single user action can trigger 3-5 actual API calls once you account for error handling, and you never see this in controlled PoC environments.
The additional problem is that 88% of AI proof of concepts fail to reach widescale deployment. Many die in what gets called “proof-of-concept purgatory”—experiments that never graduate because the business case evaporates once real costs become clear.
When you understand why PoC costs don’t predict production reality, the next question becomes: at what point do you need to reconsider your infrastructure strategy entirely?
What is the 60-70% Threshold and When Should You Consider On-Premises?
Deloitte research based on interviews with more than 60 global client technology leaders identifies a threshold: when cloud costs hit 60-70% of what equivalent on-premises systems would cost, on-premises becomes more economical.
Cloud pricing models are designed for variable, unpredictable workloads. Once your AI workload becomes stable and predictable—production traffic you can forecast with reasonable accuracy—you’re paying a premium for elasticity you don’t need.
The threshold works like this: if on-premises infrastructure costs $1 million over five years and cloud costs $4 million over the same period, your usage ratio is 0.25. That translates to a daily threshold of 6 hours. If your system runs more than 6 hours per day, on-premises is cheaper. For 24/7 production workloads—which most AI applications are—you hit that threshold immediately.
Roughly a quarter of respondents say they’re ready to shift workloads as soon as cloud costs reach just 26-50% of alternatives. By the time cloud costs exceed 150% of alternatives, 91% are prepared to move. Those are people who’ve done the maths.
The threshold assumes a mature, stable workload. If you’re still experimenting, if usage is unpredictable, if you’re not consistently using more than 60% of your provisioned cloud capacity, you probably shouldn’t move yet. But for production systems with known usage patterns, the 60-70% threshold is where you need to start running the numbers on alternatives. Understanding the cloud vs on-premises decision becomes critical at this point.
How Do You Calculate Total Cost of Ownership for AI Infrastructure Options?
Total cost of ownership is where you account for everything, not just the monthly cloud bill or the hardware purchase price.
For cloud, that means compute instances, storage, network egress, API calls, and support contracts. For on-premises, that means hardware capital expenditure, power and cooling, data centre space, operations staff, and refresh cycle. For hybrid—which most organisations end up with—you get a combination of both plus a complexity premium for managing multiple environments.
The time horizon matters. A three-year analysis often favours cloud because you haven’t amortised the on-premises capital expenditure yet. A five-year analysis favours on-premises for consistent workloads because you’ve spread the hardware cost over enough usage to make it cheaper per unit.
What people miss are the hidden costs on both sides. Cloud infrastructure looks simple until you hit egress fees for moving data between regions or out to users. Those can be 15-30% of your total bill. Premium pricing for GPU instances means you’re paying 2-3x wholesale rates.
On-premises looks cheap until you account for the skilled staff you need to run it, the upgrade cycle every few years, and the idle capacity waste when you have to provision for peak load but run at average load most of the time.
The way to get this right is to start with your current cloud bills, project realistic three-year growth based on your product roadmap, then model what an equivalent on-premises setup would cost over the same period. Tools like AWS TCO Calculator, Google Cloud Pricing Calculator, and Azure Cost Management can help, though these vendor-provided tools may emphasise cloud benefits. They have a dog in the fight.
When documenting this, make your cost assumptions explicit. Stakeholders trust the analysis more when they can see comprehensive, realistic cost accounting rather than optimistic projections. Show your working. When building a business case for infrastructure investment, this TCO analysis forms the financial foundation.
What Cost Components Drive Inference Expenses in Production?
Inference costs break down into five components that multiply across millions of queries:
Compute resources. Larger models with 70B+ parameters cost more per token than smaller 7B parameter models. The difference can be 10x or more per query.
Model complexity. Transformer attention mechanisms scale quadratically with sequence length. Doubling the context window quadruples the compute cost. That’s not a typo.
Response latency. Streaming responses hold resources longer than batch processing. Time to first token measures latency from request submission to initial output. These add up.
Concurrent users. 100 simultaneous users require roughly 10x the infrastructure of 10 users. You can’t just queue them up because research on eCommerce sites shows that a site loading in one second has conversion rates 3-5x higher than one loading in five seconds. Speed matters.
Data transfer. Cloud providers charge for data movement between services and regions. Every single transfer.
What makes this expensive is that these components multiply. A complex model with high concurrency requirements and streaming responses can cost 50-100x more than a simple model serving batch requests to a small user base.
Understanding these cost drivers reveals where you have the most leverage to optimise spending.
How Can You Reduce Inference Costs Without Sacrificing Performance?
The fastest way to cut inference costs is quantisation. This technique reduces the numerical precision used to represent model parameters. Instead of using FP32 (32-bit floating-point numbers), you can use INT8 (8-bit integers) or INT4 (4-bit integers). Reducing model precision from FP32 to INT8 or INT4 cuts compute requirements by 4-8x with minimal accuracy loss. For most tasks, accuracy drops less than 5%. That translates directly to 60-80% cost reduction without changing your architecture.
The strategy should be incremental: start with FP8 (8-bit floating-point) and validate quality on your benchmarks. If degradation is acceptable, push to FP4 (4-bit floating-point) and validate again. Chatbots with casual conversation often see minimal impact even at FP4, but code generation and mathematical reasoning are more sensitive to quantisation. Test before you commit.
You also have several operational optimisations available:
Caching. Store responses to frequent queries to avoid re-inference. Cache hit rates of 50-70% are common, which means you’re cutting inference calls in half for no accuracy cost. Free money.
Batching. Process multiple queries together for GPU efficiency. Instead of running 100 sequential inferences, batch them into groups of 10-20 and process them in parallel.
Routing. Send simple queries to small models and complex queries to large models. Many applications find that 70% of queries can be handled by a smaller, cheaper model with the expensive model reserved for the 30% of queries that actually need it.
Tiered service. Free tier users get small model responses. Paid tier users get large model responses. This aligns costs with revenue while maintaining acceptable quality for users at every tier.
Hybrid placement. Move stable, predictable workloads to cheaper on-premises infrastructure while keeping variable loads in cloud.
The combination of quantisation, caching, and routing can cut costs 40-60% without touching your model architecture. That’s a lot.
What Signals Indicate Your Inference Costs are About to Spiral?
Cloud budgets already exceed limits by 17% on average. Here are the signals that your inference costs are heading for trouble:
Cost velocity above 40% month-over-month. Some growth is expected as usage increases, but month-over-month growth above 40% suggests unsustainable trajectory.
Sustained utilisation above 60-70%. If you’re consistently using more than 60-70% of your provisioned cloud GPU capacity, you’re approaching the threshold for evaluating on-premises alternatives.
Monthly cost variance exceeding 30%. Only 23% of organisations see less than 5% cloud cost variance. Variance above 30% indicates lack of control and visibility into what’s driving costs. You’re flying blind.
Egress fees above 15% of total costs. Data egress fees hit when data moves out of the provider’s network. AWS charges $0.09 per GB for the first 10TB transferred to internet. For AI applications moving embeddings and results between services, this adds up quickly. If egress fees represent more than 15% of your total cloud costs, you have inefficient data movement patterns. Fix them.
Engineers self-censoring features due to cost anxiety. When your team starts avoiding new features because they’re worried about the cost impact, you’ve created an environment where cost concerns override product decisions. That’s a problem.
Real-time alerts for anomalies, budget thresholds, or sudden usage spikes help you prevent runaway bills before they become end-of-month surprises. A daily spending cap of $1,000 per project can prevent accidental infinite loop scenarios that generate bills overnight. Ask us how we know this.
How Does Hybrid Infrastructure Help Manage Inference Economics?
Leading organisations are implementing three-tier hybrid architectures that leverage the strengths of each infrastructure option rather than trying to pick a single winner.
Cloud handles variable and experimental workloads. New features, geographic expansion, seasonal demand, burst capacity needs, and experimentation all go in cloud. You’re paying for elasticity, which makes sense when you need elasticity.
On-premises runs production inference for stable workloads. High-volume, continuous workloads with predictable traffic patterns move to private infrastructure. You gain control over performance, security, and cost management. Once you hit the 60-70% threshold and your workload is mature enough to forecast accurately, on-premises delivers better unit economics.
Edge handles ultra-low latency and data sovereignty requirements. Some workloads can’t tolerate round-trip latency to cloud or have regulatory constraints requiring local processing.
42% of respondents favour a balanced approach between on-premises and cloud infrastructure, driven by latency, availability, and performance requirements. IDC predicts that by 2027, 75% of enterprises will adopt hybrid approaches to optimise AI workload placement, cost, and performance.
The benefit is that you’re optimising for different objectives with different infrastructure. Production workloads with predictable traffic get the cost efficiency of on-premises. New features and experiments get the flexibility of cloud without requiring you to build spare capacity into your on-premises environment. Best of both worlds. Learn more about infrastructure architecture choices and when each deployment model makes sense.
FAQ Section
What’s the difference between training costs and inference costs?
Training is a one-time capital expense to develop the model—compute plus data. Inference is the recurring operational cost every time the model generates a response. Training might cost $100,000 once. Inference costs $0.01 per query but multiplies to $10,000 per month at a million queries. One’s a lump sum. The other bleeds you dry monthly.
How do I estimate production inference costs during the pilot phase?
Multiply your pilot API usage by expected production scale—users times queries per user times days. Add a 40% buffer for retry logic and errors that you don’t see in controlled testing. Check current pricing, noting that vendor pricing changes frequently. If you’ll exceed 60% utilisation, compare to on-premises TCO because you might be at the threshold where on-premises becomes cheaper. Do the maths early.
Why are my inference costs higher in the cloud than expected?
Cloud costs reflect markup on GPU compute—often 2-3x wholesale rates. You’re also paying egress fees for data transfer between services, API call volume including hidden retries and errors, and premium pricing for GPU instances. Production usage reveals these multipliers that PoCs hide because PoCs run on free tiers with controlled traffic. The free lunch is over.
At what scale does on-premises infrastructure become more economical?
Deloitte research suggests 60-70% sustained cloud GPU utilisation as the tipping point. This typically corresponds to 5-10 million daily queries for enterprise workloads, though the threshold varies by model size, latency requirements, and team capabilities. The key is consistent utilisation, not just volume. Predictability matters.
Can I reduce inference costs without changing my model?
Yes, through operational optimisations. Implement response caching for frequent queries—50-70% cache hit rates are common. Batch requests together. Route simple queries to smaller models. Optimise prompt engineering to reduce token counts. These can cut costs 40-60% without touching the model. Low-hanging fruit.
What are egress fees and why do they matter for inference costs?
Egress fees are charges cloud providers levy when data leaves their network. For AI, this includes moving embeddings between services, transferring results to users, and syncing data across regions. These can represent 15-30% of total cloud AI costs and often surprise teams because they’re not part of the obvious compute bill. Death by a thousand cuts.
How do I know if my workload is predictable enough for on-premises?
Analyse three months of production usage. If weekly query volume variance is less than 30% and you’re consistently using more than 60% of your cloud capacity, the workload is stable enough to justify on-premises evaluation. Seasonal businesses may need hybrid approaches that combine on-premises base capacity with cloud burst capacity. Know your patterns.
What’s the role of model quantisation in cost management?
Quantisation reduces model precision from FP32 to INT8 or INT4, cutting inference compute requirements by 4-8x with minimal accuracy loss—less than 5% for most tasks. This translates directly to 60-80% cost reduction, making it the fastest way to control expenses without changing architecture. It’s your first move.
Should startups worry about inference economics or just use cloud?
Early-stage startups should use cloud for flexibility during product-market fit exploration. Once you have consistent daily usage above 1 million queries and predictable growth, evaluate TCO. Some startups hit the on-premises inflection point within 18 months of launch, particularly if they’re in high-usage categories like developer tools or business automation. Don’t wait too long.
How does agentic AI affect inference economics?
Agentic AI multiplies inference costs through chain-of-thought reasoning—multiple LLM calls per user query—tool calling loops, and longer context windows. A single user request can trigger 5-20 model inferences, making agentic systems 10-20x more expensive than simple chatbots. This is the biggest cost contributor in modern AI applications. Plan accordingly.
What metrics should I track to monitor inference cost health?
Track cost per query—monthly bill divided by total inferences. Track cost per user—monthly bill divided by active users. Track cost velocity—month-over-month change percentage. Track utilisation rate—actual usage divided by provisioned capacity. Track cache hit rate—cached responses divided by total queries. These five metrics give you early warning when costs are heading for trouble. Watch them.
How do vector databases affect inference TCO?
Vector databases add storage costs—embeddings are large—and query costs because similarity search is compute-intensive. You often get a separate vendor bill. However, good vector database architecture enables semantic caching that reduces LLM calls by 40-60%, which can offset the additional infrastructure cost. The net effect depends on your cache hit rate. Do the maths.
Understanding inference economics is just one piece of the AI infrastructure ROI gap. To translate this cost understanding into actionable planning, explore building an AI infrastructure modernisation roadmap that prioritises investments based on actual TCO analysis.
