Most businesses spend $4,500–$4,900 per month on a single support agent, including salary and benefits. In contrast, AI support tools like Quidget start at just $16 per month for 500 responses. That’s a fraction of the cost – with 24/7 availability.
AI solutions also scale better. For example, Quidget’s $210/month plan handles up to 50,000 responses. Meanwhile, adding more human agents means higher salaries, benefits, and overhead costs.
Here’s the key takeaway: AI covers routine tasks at a much lower cost, while human agents focus on complex or emotional issues. The smartest approach? Combine both for maximum efficiency and savings.
AI vs Humans in Customer Support 2024
AI Support Agent Costs in 2025
The market for AI support agents has expanded significantly, offering businesses a variety of pricing models tailored to different needs. Understanding these options can help align your choice with your support demands and budget. Below are the most common pricing structures businesses are using today.
Common Pricing Models for AI Agents
– Per-conversation pricing: This model charges a fixed rate per interaction. For instance, one provider sets the cost at $2 per conversation. It’s a practical option for businesses with consistent support volumes.
– Usage-based pricing: Charges are based on computational resources like API calls or processing time. For example, one provider bills $4 per hour, while another uses a token-based system, charging $15 per 1 million input tokens and $60 per 1 million output tokens. This approach suits businesses with unpredictable support needs.
– Outcome-based pricing: Costs are tied to successful resolutions. A leading provider charges $0.99 per resolved issue. This model ensures you only pay for results, but it requires clear metrics to define success.
– Subscription-based pricing: Offers a flat monthly fee for predictable costs. One provider starts at $29 per agent per month. Quidget’s plans begin at $16 per month for 500 AI responses and go up to $210 per month for 50,000 responses.
"An AI Agent performs a certain amount of work, and you pay for [the] amount of time or units it took to do that work. […] It’s a fair trade for the customer and provider."
– Aaron Levie, CEO of Box
While pricing models vary, the overall cost of implementing AI support depends on several additional factors.
What Affects AI Costs
The volume of responses is the biggest driver of costs. Basic AI systems typically require an upfront investment ranging from $10,000 to $50,000, while more advanced generative AI solutions can cost between $1,000,000 and $5,000,000.
Ongoing expenses like maintenance, updates, and integrations add to the total investment. These can account for an additional 10–30% of the initial cost. If technical debt accumulates, it could increase costs by as much as 30%.
How AI Costs Scale with Business Growth
As your business expands, AI costs tend to grow in predictable steps. For instance, a company using Quidget’s Pro plan for $79 per month (10,000 responses) can upgrade to the Enterprise plan at $210 per month (50,000 responses). This allows businesses to scale their capacity without a proportional increase in expenses.
However, unexpected usage spikes can lead to budget overruns. In fact, 66.5% of IT leaders have reported exceeding their budgets due to unforeseen AI usage. Opting for plans with built-in capacity buffers or usage alerts can help avoid these surprises.
Despite the upfront and ongoing costs, many businesses find that AI systems deliver long-term savings compared to human support teams – a topic we’ll explore in the next section.
Human Support Team Costs in 2025
Building a human support team involves more than just paying salaries – it’s a mix of visible and hidden costs that can heavily impact a company’s overall strategy. These financial pressures are driving many businesses to explore AI-driven alternatives.
Average Salaries and Employment Costs
In the U.S., customer support agents typically earn between $40,000 and $55,000 per year. When you factor in benefits and other associated costs, the total rises by an additional 20–30% on average.
Geography plays a big role in determining salaries. For example: – In Barrow, Alaska, agents earn about $56,090 annually.
– In Berkeley, California, the average salary is $55,129.
– The District of Columbia leads with $56,140, followed by Washington at $52,690 and Massachusetts at $50,450.
And that’s just the starting point. There are plenty of hidden expenses that make human support even more costly.
Hidden Costs of Human Teams
Recruiting, onboarding, and dealing with high turnover rates are some of the biggest hidden expenses. In fact, turnover alone can account for up to 40% of an agent’s annual salary, especially in call centers where turnover rates hover between 30–45% each year.
"When an essential team member departs, it’s not just the intimate knowledge of our workflow we lose. We also face a decrease in productivity and employee morale until a fitting replacement is found." – Chris Estrada, CEO of Nationwide United Auto Transport
Beyond turnover, other overhead costs like office space, IT equipment, training, and administrative expenses can add thousands of dollars per agent each year. These costs vary widely depending on the size of the business and where it operates.
How Costs Vary by Business Size and Location
Large companies tend to keep customer service costs manageable, with 39% of them spending 5% or less of their Annual Recurring Revenue (ARR) on support. Medium-sized companies see more fluctuation – 35% manage to stay under the 5% mark, while 22% spend between 5–10% of their ARR.
Smaller businesses face the toughest financial challenges. Their customer service costs can range from under 5% to over 30% of ARR. Without the benefits of economies of scale, these companies also have to contend with location-specific expenses like office rents and local tax structures, which can significantly impact their bottom line.
AI vs Human Teams: Cost Comparison
When weighing the costs of AI support against traditional human teams, the difference is striking. Human agents can cost thousands of dollars per month, while AI solutions provide a much more budget-friendly option for handling similar workloads.
Monthly Cost Comparison Table
Here’s a breakdown of the monthly costs:
| Support Option | Monthly Cost | Annual Cost | Capacity |
|---|---|---|---|
| Single Human Agent | $3,752 + benefits ($4,500–$4,900 total) | $54,000–$58,800 | 40 hours/week, limited availability |
| Quidget Starter Plan | $16 | $192 | 500 AI responses, 24/7 availability |
| Quidget Pro Plan | $79 | $948 | 10,000 AI responses, 24/7 availability |
| Quidget Enterprise Plan | $210 | $2,520 | 50,000 AI responses, 24/7 availability |
| Small Human Team (3 agents) | $13,500–$14,700 | $162,000–$176,400 | Limited to business hours |
When factoring in benefits and overhead, the cost difference becomes even more pronounced.
How AI Handles Routine Questions
Cost is just one piece of the puzzle – AI also excels at managing routine tasks. It can handle up to 80% of common customer questions, such as password resets, order tracking, billing inquiries, and basic troubleshooting. This allows human agents to focus on more complex or nuanced issues, improving overall efficiency.
Long-Term Savings with AI
AI isn’t just a short-term fix; it’s a long-term money saver. Studies show that 95% of businesses report satisfaction with the returns they see from AI solutions. Beyond direct savings, AI reduces hidden costs like employee turnover, training expenses, and overtime.
As your business grows, scaling with AI is far more predictable and affordable. Adding capacity to handle more interactions doesn’t require hiring new staff, unlike human teams. This scalability, combined with lower operational costs, makes AI solutions an attractive, cost-effective choice for businesses looking to manage support efficiently.
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What to Consider When Choosing AI vs Human Teams
After evaluating costs, it’s essential to think about the distinct roles AI and human teams play. Your decision should reflect your business priorities, customer expectations, and long-term objectives. Beyond financial considerations, there are several key factors to weigh when deciding between AI and human support teams.
When You Need Human Support
Human agents shine in situations that demand empathy, critical thinking, and adaptability. Complex issues, emotional scenarios, and nuanced discussions often require the human touch that AI can’t replicate.
Salesforce reports that 59% of customers still prefer speaking to a human agent for intricate problems, even though 64% expect immediate responses. While speed is important, these numbers show that customers value human interaction when faced with challenging issues.
For B2B interactions, personalized support becomes even more critical. High-value clients and sensitive matters often require the expertise and care that only human agents can deliver.
Combining AI and Human Support
The best strategy isn’t choosing one over the other – it’s integrating both effectively. A hybrid approach allows AI to manage repetitive tasks, freeing up human agents to handle situations that need emotional intelligence and problem-solving skills. For example, AI can automate routine inquiries, and by 2028, 33% of enterprise software applications are expected to incorporate AI responses. Moreover, nearly 90% of businesses see AI as a tool to gain a competitive edge.
Seamless transitions between AI and human agents are critical. As Shashank Singh, CEO of Bakstage.AI, explains:
"AI should augment human agents rather than act as a barrier".
When AI reaches its limits, customers should be able to connect with a human agent quickly and easily. As Lars Nyman, CMO of CUDO Compute, advises:
"Don’t pretend the bot is a person. Customers can smell deception a mile away. AI should be an efficient concierge, not an imposter trying to mimic empathy. Transparency builds trust; deception erodes it".
Next, let’s explore how AI can deliver measurable returns and set clear benchmarks for success.
Return on Investment for AI Support
AI support offers tangible benefits beyond just cutting costs. To measure its impact, focus on metrics like improved response times, reduced agent workload, and customer satisfaction. Before rolling out AI, define what success looks like – whether it’s faster resolutions, lower expenses, or a smoother customer experience. Also, identify areas where human oversight is still necessary.
Start small by applying AI to a high-value use case. Use this as a test to demonstrate clear improvements before expanding to more complex workflows. For industries like healthcare, finance, or government, ensure any AI solution meets certification and security requirements before calculating its potential returns. As a guiding principle:
"The only thing that matters is whether customers feel heard, helped and human".
Summary and Key Takeaways
By 2025, AI support agents are expected to cut labor costs by as much as 90% and overall support expenses by about 30%. For comparison, a single human agent costs around $3,000 per month, plus an additional $1,500 in overhead. Meanwhile, AI can handle 1,000 interactions for only $500 a month.
AI agents operate 24/7, addressing thousands of queries almost instantly. In contrast, human agents are bound by working hours and capacity. However, while AI is unmatched in efficiency, human agents remain crucial for handling complex or emotionally sensitive issues.
The real advantage comes from combining both approaches. AI can handle routine questions and high volumes, freeing human agents to focus on relationship-building and solving intricate problems. By 2026, businesses are projected to save $80 billion in labor costs through AI, with the AI agent market expected to hit $7.6 billion by 2025. Companies adopting AI chatbots have already seen cost reductions of 30–40%, and AI could potentially manage up to 95% of customer interactions. These savings allow businesses to allocate more resources toward strengthening customer relationships.
When planning your support strategy, it’s essential to identify which interactions require a personal touch and which can be automated. Think about your customers’ expectations, the size of your business, and your long-term goals to make the right choice.
Want to see how AI-powered support can work for you? Try Quidget free for 7 days – no credit card needed.
FAQs
How can businesses decide the right mix of AI and human support agents for better efficiency and customer satisfaction?
Balancing AI and Human Support Agents
Striking the right mix between AI tools and human support agents starts with understanding your customers and the types of questions they ask. AI excels at handling straightforward, repetitive inquiries quickly, while human agents shine when dealing with more complex or sensitive situations.
To fine-tune this balance, keep an eye on customer feedback and performance data. If AI is successfully managing a large volume of requests and customers are satisfied, you might not need as many human agents. However, if you notice a rise in issues that require a personal touch, it could be time to shift the balance back toward human support. This strategy ensures you deliver quality service while staying mindful of costs.
What challenges might arise when switching from human support teams to AI-driven solutions?
Switching from human support teams to AI-based solutions isn’t always smooth sailing. System glitches or inaccuracies can disrupt the customer experience, and data security risks – such as breaches or improper handling of sensitive information – can’t be ignored. On top of that, AI often struggles to replicate the personal touch customers value, especially when dealing with complex issues or transitioning seamlessly to a human agent when required.
To tackle these challenges, thoughtful planning, rigorous testing, and ongoing performance tracking are key. These steps help ensure AI tools provide reliable support while maintaining customer trust and satisfaction.
How can businesses keep AI support systems cost-effective as they grow?
To manage costs as AI support systems expand, businesses should prioritize monitoring performance metrics and refining the AI’s abilities to align with customer needs. Avoiding feature bloat is key – focus on what adds real value. Regular updates and retraining keep the system effective and prevent it from becoming outdated or inefficient.
Opting for modular AI platforms is another practical strategy. These systems let companies adjust features as needed, meaning they only pay for tools that are genuinely useful. By staying adaptable, businesses can maximize automation benefits while keeping operational expenses in check.
