How to avoid agentic sticker shock

AI agents may seem inexpensive until the CFO sees the first bill. The true cost of agentic AI spans far beyond tokens. If you don’t estimate the full costs and control the sprawl, it could blow your budget. Compute, memory, orchestration, APIs, security, and operational staffing add up quickly and aren’t routinely included in ROI spreadsheets.

Understanding all the costs of agentic solutions is essential for forecasting expenses, controlling sprawl, and negotiating contracts with providers and hyperscalers. If you don’t know how AI agents work under the hood and focus solely on tokens, your first invoice might be the last in your career.

The myth of ‘cheap tokens’

Token pricing is a fundamental component of any agentic solution. A token is a unit of text that large language models (LLMs), such as GPT, Claude, and Gemini, use to read and generate language. It’s not the same as a word or a character; it’s more like a ‘word chunk,’ roughly equivalent to 75% of a word. ‘Cat’ is a single token; ‘fantastically’ is 2-3 tokens; and all punctuations, spaces, and numbers count. Models process these word chunks in two ways:

• Input: The text sent to the model, which may include images, sound, and documents
• Output: The text returned by the model, often called ‘inference’ tokens

OpenAI’s GPT-4 Turbo API model costs $0.01 per 1,000 input tokens and $0.03 per 1,000 output tokens. This can quickly rack up high invoices. That said, it’s clear that token pricing will never be more expensive than it is today. The combination of competition, improved GPU processing capability, and enhanced model design (such as smaller, more efficient models) will continue to drive token pricing. Enterprises should exercise caution when entering multi-year deals given the declining price curve.

Token pricing is only one piece of agentic cost solutions. When agents chain complex requests, maintain memory, attempt to improve RAG results, and collaborate with multiple agents to digest various inputs and produce outputs, costs could easily escalate 10 times or more. You’ve got to dig into all the cost components to estimate the TCO of agentic solutions.

Exhibit 1: Key Components of an AI agentic solution

Source: HFS Research, 2025

Where hidden agentic AI costs actually are

There are five key components of your core agentic AI infrastructure:

1. Model access: As discussed, input and inference tokens represent most of these costs, regardless of using public APIs or hyperscaler-provided options. However, there are often additional charges for reserving model capacity ($/hour) and for fine-tuning custom models (professional services fees, plus additional token and compute costs). Buyers should be wary of high-output models (for example, ‘write code that will interface system A and B’ could create large outputs) and paying for provisioned capacity that goes under-used.
2. Inference compute: For enterprises that use self-hosted or dedicated hyperscaler environments, inference computing costs are calculated on a per GPU per hour basis (an H100 GPU costs $8–12/hour). Providers are containerizing model run times and offering ‘inference-as-a-service’ pricing. However, base costs can escalate quickly when multiple agents are running simultaneously or during idle compute times.
3. Embedding and vector search infrastructure: Embedding is the numerical representation of text. Vectors power semantic understanding of systems, driving RAG, memory, personalization, understanding intent, and structuring unstructured data. Think of it like you’re the knowledge of your system. When users ask questions, those get turned into vectors, the system searches for similar vectors, and results are returned. Providers of vector infrastructure, such as Pinecone, Weaviate, and Qdrant, charge for storage, query speed rate (per look-up), and throughput, including concurrent throughputs. Text is stored in vector databases, passed to models, and then returned to store. Embedding operations cost ~$0.00002 per token, vector storage is charged on a per GB basis, and there are throughput charges.
4. Prompt orchestration and automation: Agentic solutions don’t just call LLMs once. They will chain steps, call APIs multiple times per step, analyze outputs, and retry to improve results. Orchestration platforms, such as LangChain, CrewAI, AutoGen, manage this effort, though there are server-less and custom Python code options. These solutions all add compute time, quickly exacerbated by concurrency, retries, and loops.
5. Monitoring, observability, and logging: Given enterprises’ control obligations, operating without tracking system behavior would be foolhardy. Providers such as LangSmith and PromptLayer provide tools that track every token, API call, and decision made by agents. Given the amount of processing, logs get large fast and every company will need to establish the granularity of logging. This can result in substantial storage costs and require additional compute time to provide dashboard analytics on system performance.

All this can add up quickly as shown in Exhibit 2.

Exhibit 2: Agentic cost components

Source: HFS Research, 2025

You’re not buying a tool—but building a team of processing agents

Agents behave like distributed microservices but with less transparency and more cost volatility. Enterprises must treat agent chains as operational assets requiring SLAs, auditability, and version control. Agents behave like teams and their costs should be modeled that way, adding to operational complexity.

• Tokens: Each agent will have its own multi-step logic, decision trees, and thresholds for retries, which will exponentially drive token usage.
• Application usage: Unlike humans, agentic solutions would likely use tools and APIs more often. Pounding your CRM and financial systems with more user requests will increase application-related usage. This may require additional infrastructure costs for legacy systems used by agents.
• Memory: Your organization will require substantial re-embedding efforts, especially when developing its first agentic solutions. As agents begin to run and the results require tweaking, more re-embedding costs will be required.

“Ask your provider these five cost questions before you sign”

1. What’s the average number of steps and agents per use case?
2. How do retry loops impact token volume and how are they governed?
3. How do you simulate worst-case usage across real workflows?
4. What are the charges for observability and reporting?
5. How does your orchestration handle toxicity detection and guardrails?

Operational guardrails must match the spend

No enterprise AI system is safe without its own operations and security costs. Enterprises must consider several factors:

A real-world use case of customer service

Let’s use a real-world example of an enterprise-class solution for a customer service assistant handling chat, email, and mobile texting channels. Although fewer unique contacts exist, when broken into various tasks per contact, customers place around five million requests per month for order tracking, FAQs, returns, password resets, and product information. Leveraging an agentic architecture with RAG, memory, and API calls for various client systems and an equivalent GPT-4 Turbo model to drive reason, search, response, and escalations. We expect the following cost components to total $92,500 per month (see Exhibit 3).

Exhibit 3: Example of agentic costs of customer service

Source: HFS Research, 2025

At just under $1.2 million per year, this may seem expensive. However, a 100-human offshore agent team would easily cost $6 million annually. If the agentic solution can reduce volumes by just 20%, the payback is just 12 months. This excludes all the additional costs of hiring, training, and retaining a human workforce. More importantly, the cost of human labor only increases with inflation, while AI technology pricing continues to decline as GPUs become more efficient, model design improves, and competition creates downward pressure.

The Bottom Line: You don’t architect for agentic cost transparency—you’re setting a cost time bomb.

Enterprise and procurement leaders can easily see the risks of escalating agentic AI, even with its solid ROI opportunities. Enterprises must stop evaluating AI solely through the lens of token pricing or innovation pilots—they should get to the bottom of true costs.

Agentic architectures are substantially more complex to price than outsourced FTEs. If you don’t break down the cost components and fully architect for transparency, cost control, and security from the beginning, provider solutions can slip under the radar as low-cost pilots until they become a whoppingly large IT budget line and, potentially, a regulatory red flag. Enterprise leaders must mandate TCO modeling of all AI use cases, establish required AI governance frameworks, and push providers to show complete cost transparency.