Should I Build or Buy AI Voice Agents? A Guide

You see the potential. AI voice agents can handle routine calls, free up your team, and improve customer interactions 24/7. But one big question can derail the entire project before it starts: should I build or buy AI voice agents? This classic build vs. buy AI voice agents dilemma pits a fully custom solution against a powerful, pre-built tool. The right answer depends entirely on your resources, timeline, and long-term goals. We'll help you compare the costs, performance, and technical needs of each approach so you can make the best choice for your business.

Key takeaways

• Treat building vs. buying as a core business decision: Building an AI agent from scratch is a massive investment of time, money, and specialized talent. For most businesses, buying a ready-made solution is the smarter move to get to market faster and keep your team focused on what they do best.
• Look for a partner, not just a product: The best solutions come from providers who manage the entire technical stack, from infrastructure to integrations. This ensures your agent is built on a solid, scalable foundation and frees you from handling complex maintenance and updates down the line.
• Demand a solution that fits your unique needs: A generic agent won't cut it. Your final choice should integrate smoothly with your existing tools, offer customization for your industry, and provide clear analytics to measure performance and improve the customer experience.

So, what is an AI voice agent?

Think of an AI voice agent as a smart, automated assistant for your business. These aren't just simple chatbots; they are sophisticated systems designed to handle conversations and complete tasks that would otherwise require a human. From answering customer calls to analyzing data, these automated systems are designed to make your operations more efficient, reduce costs, and free up your team for more important work.

Many businesses see them as a gateway to adopting AI because they are intuitive and deliver immediate value. An AI voice agent can act as the first point of contact for your customers, providing instant, helpful responses 24/7. This not only improves the customer experience but also gives your business a serious competitive edge. The core idea is to automate repetitive communication, allowing you to scale your customer interactions without scaling your headcount. Whether you're a startup or a large enterprise, integrating a voice agent can fundamentally change how you engage with your audience.

BLOG: What are AI voice agents? A guide for businesses.

How do AI voice agents actually work?

At their heart, AI voice agents use a combination of natural language processing (NLP) and machine learning to understand and respond to human speech. When a customer calls, the agent listens, deciphers the intent behind their words, and provides a relevant, conversational answer or performs a specific action. The goal is to make the interaction feel as natural and helpful as a conversation with a human agent.

The capabilities of any voice agent are directly tied to the conversational AI platform it’s built on. These platforms provide the underlying technology and tools that determine how smart, responsive, and customizable your agent can be. This is a critical point to understand, as the platform dictates everything from the agent's voice and personality to its ability to integrate with your existing software.

What can you do with an AI voice agent?

You can find AI voice agents working across a wide range of industries, handling tasks that are crucial but often repetitive. In customer service, they can manage up to 80% of routine inquiries, like questions about order status, store hours, or basic product information. This frees up human agents to focus on solving more complex and sensitive customer issues that require a personal touch.

In healthcare, AI voice agents are transforming administrative workflows by handling appointment scheduling, sending patient reminders, and answering frequently asked questions about services. For startups and small businesses, they can be a game-changer, acting as a virtual receptionist that ensures no call goes unanswered. The applications are incredibly versatile, making them one of the most powerful and accessible AI tools for businesses today.

BLOG: Top voice agent use cases across industries

Sales and lead qualification

Your sales team’s time is best spent building relationships and closing deals, not sifting through endless lists of cold leads. This is where an AI voice agent can completely change your sales motion. These agents can handle the top-of-funnel outreach, making initial calls to qualify prospects automatically. They are designed to hold natural conversations, ask targeted questions to gauge interest, and determine if a lead meets your criteria. This automated lead qualification process ensures that only the most promising opportunities are handed off to your human sales reps, allowing them to focus their expertise on high-value interactions with genuinely interested buyers.

Internal employee support

AI voice agents can also be a game-changer for your internal operations. Imagine an instant, 24/7 help desk for your employees. Instead of filing a ticket for a simple IT question or waiting on an email from HR about benefits, your team can get immediate answers from a voice agent. This is perfect for handling frequently asked questions about company policies, password resets, or basic software troubleshooting. Implementing an agent for internal support creates an efficient employee self-service model, which reduces the administrative load on your HR and IT departments and empowers your team with the information they need, right when they need it.

Should you build or buy your AI voice agent?

Deciding whether to create your own AI voice agent from scratch or purchase a ready-made solution is one of the first major hurdles you'll face. This isn't just a technical choice—it's a strategic one that impacts your budget, timeline, and team's focus for years to come. Let's break down what "build" and "buy" really mean and the key factors that will guide you to the right decision for your business.

What does it mean to "build" vs. "buy"?

When we talk about "building," we mean more than just a one-and-done coding project. Building an AI agent from the ground up means assembling a specialized team, managing complex infrastructure, and committing to continuous maintenance and updates. It’s a significant undertaking that requires a dedicated engineering team to keep the system running smoothly and effectively over time.

On the other hand, "buying" means partnering with a provider to implement a pre-built AI solution. This approach gives you access to a powerful, tested tool without the long development cycles. It’s about leveraging expert technology so you can get your voice agent up and running quickly and focus on what your business does best.

What to consider before you decide

Your decision will come down to a few critical factors: cost, time, and resources. Building an AI agent in-house is often incredibly expensive and time-consuming. The total cost of ownership goes far beyond initial development, including ongoing expenses for maintenance, model upgrades, and security. You also need to factor in the high salaries for specialized AI talent.

If speed and efficiency are your priorities, buying a solution is almost always the better path. It allows you to deploy an agent quickly and frees up your internal teams to concentrate on core business goals instead of getting bogged down in complex AI infrastructure management. For most companies, this means getting to market faster with a more reliable product.

Deciding whether to create your own AI voice agent from scratch or purchase a ready-made solution is one of the first major hurdles you'll face. This isn't just a technical choice—it's a strategic one that impacts your budget, timeline, and team's focus for years to come.

Let's talk about the cost: build vs. buy

When you compare building an AI voice agent from scratch to buying a pre-built solution, the costs go far beyond the initial price tag. To make a smart decision, you need to look at the total cost of ownership. This includes the upfront investment, the long-term expenses to keep it running, and the hidden costs that often catch teams by surprise. Let's break down what you can expect financially from each path.

Breaking down upfront vs. ongoing costs

Building an AI agent in-house requires a major upfront investment. You’re funding a significant internal project that includes high salaries for specialized AI talent, which can exceed $150,000 per person annually. Add in hardware, software, and a development timeline that can stretch for months. The expenses don't stop at launch. Maintaining and improving the agent requires a dedicated engineering team, which can cost an estimated $50,000 per month. In contrast, buying a solution typically involves a predictable subscription fee, making it much easier to budget for.

Comparing the numbers: build vs. buy

Let's get straight to the numbers because they tell a clear story. Building an AI agent in-house is a serious financial commitment. You're not just paying for software; you're funding a whole internal project with hefty upfront costs for hardware and software. This also includes the high salaries of specialized AI talent, which can easily exceed $150,000 per person annually. The expenses don't stop once the agent is live, either—ongoing maintenance and improvements can run an estimated $50,000 per month. In contrast, buying a solution changes the financial picture completely. Instead of a massive, unpredictable capital expenditure, you get a clear, predictable subscription fee that makes budgeting straightforward. This isn't just about saving money; it's about making a smarter investment that allows you to get to market faster and keeps your team focused on what they do best.

Don't forget about the hidden costs

The initial development budget for an in-house AI agent is often just the start. Building a solution introduces significant hidden costs that strain resources over time. These include ongoing maintenance, regular model upgrades, constant prompt tuning, and ensuring data privacy. These operational tasks aren't minor expenses; they can add an extra 15-25% of the initial build cost every year. When you buy a pre-built solution, these responsibilities fall on the vendor. Your subscription fee covers all maintenance and platform improvements, protecting you from unexpected budget hits and letting your team focus on core business goals.

Ongoing API usage fees

Even when you choose to "build," you're rarely creating everything from the ground up. Your team will likely rely on third-party APIs for core components like speech-to-text, text-to-speech, and the underlying large language models. These services operate on a pay-as-you-go model, which can make budgeting a nightmare. Your costs will fluctuate directly with call volume, and as your agent becomes more popular, these fees can quickly spiral out of control. This creates a variable, unpredictable operational expense that can be difficult to manage, turning a successful launch into a financial liability if not planned for carefully.

A managed solution often bundles these costs into a more predictable subscription fee. At Cake, we focus on production-ready open source AI solutions, which can help insulate you from the volatile pricing of proprietary APIs. This approach gives you a clearer picture of your total cost of ownership and ensures that your expenses don't scale unexpectedly as your usage grows. It’s about creating a sustainable financial model for your AI tools from day one, rather than reacting to surprise bills every month.

The opportunity cost of using your engineers

One of the most significant hidden costs of building in-house is the opportunity cost. When your most talented engineers are spending months building and maintaining an AI voice agent, they aren't working on your core product. They aren't developing the new features your customers are asking for or improving the services that generate revenue. This internal focus on building infrastructure can slow down your business and cause you to fall behind competitors who are moving faster on their primary offerings.

Think about the true value of your engineering team. Is their time best spent reinventing AI infrastructure, or should they be focused on solving the unique problems of your industry? Partnering with a specialist like Cake allows you to offload the entire AI stack—from compute infrastructure to platform management. This frees up your internal team to do what they do best: innovate and drive your business forward. You get a powerful, production-ready AI solution without sacrificing your product roadmap.

How to calculate your potential ROI

The goal of any business investment is a positive return. With a self-built AI agent, the path to breaking even can be long. For a custom solution to be financially viable, you might need to handle around one million conversations per month. For businesses operating below that massive scale, the numbers often don't add up. Buying a pre-built agent allows you to see a return much faster, since the initial and ongoing costs are lower. Instead of waiting months to recoup your investment, you can realize value almost immediately. For most companies, buying a ready-made solution is the more direct and cost-effective path to achieving your goals.

OP-ED: The hidden costs of sticking with closed systems

What level of performance and customization do you need?

When you're deciding between building and buying an AI voice agent, it’s not just about getting a tool that works. It’s about getting a tool that works for you. This is where the balance between performance and customization comes into play. A pre-built solution might offer incredible performance on standard tasks right out of the box, but it might not handle the unique quirks of your customer inquiries. On the other hand, a custom-built agent can be tailored to your exact specifications, but achieving that same level of polished performance requires significant time, data, and expertise.

Your goal is to find the sweet spot. You need an agent that not only meets a high standard of quality but also fits seamlessly into your existing operations and speaks the language of your business. Think about the specific problems you’re trying to solve. Are you handling common, repetitive questions, or do you need an agent that can manage complex, multi-step conversations that are unique to your industry? Answering this will help you determine how much control you really need and what level of performance is non-negotiable for your team and your customers. It's a critical step that shapes whether you invest in a ready-made tool or a bespoke creation.

How to measure performance

You can’t improve what you don’t measure. To understand if your voice agent is truly effective, you need to track the right metrics. A great place to start is with First Call Resolution (FCR), which tells you how often the agent successfully resolves a customer's issue on the first try without needing to escalate to a human. High FCR scores are a strong indicator of an effective agent. Beyond that, establishing a solid quality assurance (QA) process is essential for ensuring every interaction is accurate and reliable. By consistently monitoring key performance metrics and using that data to refine your agent’s capabilities, you can systematically improve its effectiveness and deliver a better customer experience.

How much can you customize?

The level of customization you can achieve depends entirely on the path you choose. Off-the-shelf solutions and no-code platforms offer speed and simplicity, but their features are often limited to what the provider offers. If you need your agent to handle smart interruptions, support multiple languages, or integrate deeply with your proprietary backend systems, a pre-built tool might fall short. In contrast, building your own agent or working with a flexible platform gives you the freedom to tailor solutions to your exact business needs. This allows you to create a truly unique experience that aligns perfectly with your brand voice and operational workflows, giving you a distinct advantage.

Making sure it fits your unique business needs

Ultimately, the best AI voice agent is the one that solves your specific challenges. With the ability to handle up to 80% of routine customer questions, a well-implemented AI agent can free up your human team to focus on more complex and strategic work. The key is choosing a solution that truly matches your business needs. Whether you’re in real estate and need an agent to schedule property viewings or in retail and want one to track orders, the right tool should feel like a natural extension of your team. This alignment is what improves the customer journey, builds satisfaction, and fosters the kind of long-term loyalty that helps your business grow.

Think about your timeline and future growth

In a competitive market, speed is everything. The faster you can launch your AI voice agent, the sooner you can start improving customer experiences and streamlining your operations. Your choice to build or buy directly impacts your time to market and your ability to grow without friction. While the idea of building a completely custom solution is appealing, it’s a path that requires significant time, resources, and patience. You’re not just building a product; you’re building a team, an infrastructure, and a development process from the ground up.

On the other hand, opting for a pre-built solution is about leveraging expertise to get ahead. It means you can bypass the lengthy and expensive research and development phase and move straight to implementation. This is where finding the right partner becomes critical. Companies like Cake focus on providing production-ready AI solutions that manage the entire tech stack for you. This approach helps you launch faster and gives you a solid foundation to scale intelligently. The question isn't just about getting a voice agent live; it's about how quickly you can deliver value and adapt to future demands.

While the idea of building a completely custom solution is appealing, it’s a path that requires significant time, resources, and patience. You’re not just building a product; you’re building a team, an infrastructure, and a development process from the ground up.

How long does it take to build in-house?

Let's be realistic: building an AI voice agent from scratch is a marathon, not a sprint. The timeline can stretch anywhere from a few months to over two years, depending on the complexity you’re aiming for. This lengthy process isn't just about writing code. It starts with recruiting highly specialized—and expensive—AI talent. Then, you have to source, purchase, and configure the necessary hardware and software. Only after that does the actual development, testing, and debugging begin. This entire journey is a significant investment, and a detailed cost-benefit analysis often reveals that time is one of the most substantial costs.

Factoring in security and compliance build time

Security isn't something you can bolt on at the end of an AI project. It has to be woven into the fabric of your system from day one, and this process is far more complex than most teams anticipate. Building a secure AI agent involves creating sophisticated protocols for how it accesses company data, defining special security rules, and ensuring it can search internal knowledge bases without compromising sensitive information. Teams often underestimate this phase by a staggering six to twelve months. This delay not only pushes back your launch but also leaves your company vulnerable, especially if employees start using unsecured personal AI tools with company data in the meantime.

The challenge of keeping up with AI advancements

The world of AI moves at lightning speed, with meaningful advancements happening almost every month. If you build your own solution, you’re also signing up for the relentless job of maintaining it. This means you need a dedicated team just to keep updating it constantly, integrating new features, and ensuring it doesn’t become obsolete. This creates a significant opportunity cost: when your engineers are focused on maintaining an internal AI tool, they aren’t working on your core business products. Partnering with a provider like Cake, which manages the entire stack, offloads this burden. We handle the infrastructure and platform updates so your solution stays current and your team can focus on using AI to drive real business value.

How quickly can you deploy a pre-built agent?

If building is a marathon, buying is like taking a high-speed train. With a pre-built AI voice agent, you can get up and running in a fraction of the time. Because the core technology is already developed, tested, and refined, you get to skip the most time-consuming steps. Your focus shifts from foundational development to configuration and integration with your existing systems. This means you can deploy your voice agent and start interacting with customers much sooner. This speed gives you a powerful advantage, allowing you to gather real-world data, iterate quickly, and begin seeing a return on your investment while competitors are still in the planning phase.

Which option scales better for future growth?

Your launch day is just the beginning. As your business grows, your call volume will increase, and customer needs will evolve. A solution that works for you today must also be able to support you tomorrow. When you build in-house, the responsibility for scaling the infrastructure, pushing updates, and fixing bugs falls entirely on your team. When you buy, you’re also investing in a partner’s expertise in maintenance and scalability. A robust Quality Assurance strategy becomes crucial to ensure your agent’s performance remains high as you grow. Choosing a pre-built solution from a dedicated provider means you have a team of experts working to future-proof your technology.

Let's get technical: what do you need to get started?

Beyond the budget and timeline, the technical side is where the build-versus-buy decision really takes shape. Your AI voice agent needs the right team, seamless integration with your existing tools, and a rock-solid security framework. Getting any of these pieces wrong can stop your project in its tracks. Let’s walk through what you need to consider to make sure your technical foundation is sound, whether you decide to build from scratch or partner with a provider.

What expertise does your team need?

Building an AI voice agent in-house requires a highly specialized—and expensive—team. You’ll need AI and machine learning engineers, data scientists, and developers who understand conversational AI. The costs add up quickly, with specialized talent commanding high salaries and significant recruitment expenses. One analysis shows that building in-house is expensive, factoring in salaries, infrastructure, and a development timeline that can stretch for months. Before committing to build, ask yourself if you have this talent on hand or if you have the resources to recruit, train, and retain a dedicated AI team for the long haul.

How will it integrate with your current tech?

Your AI voice agent can’t operate in a silo. To be effective, it must communicate seamlessly with the tools you already use, like your CRM and helpdesk software. This integration allows the agent to pull customer history, create support tickets, or process orders. When you build, your team is responsible for creating these custom connections. If you buy, you need to verify that the solution offers pre-built integrations for your key systems. A well-integrated agent is key to improving the customer experience, creating a smooth journey without forcing customers to repeat information or wait for a human to handle simple tasks.

What about security, compliance, and privacy?

When your AI voice agent interacts with customers, it handles sensitive data. Protecting that information isn’t just good practice; it’s a requirement. Managing security and compliance means adhering to regulations like GDPR or HIPAA and implementing strong data privacy controls from day one. It also involves continuous monitoring and rigorous testing. As your voice application grows, the need for detailed component evaluation becomes even more critical to prevent vulnerabilities. If you build, your team owns this entire process. If you buy, you must thoroughly vet your vendor’s security certifications to ensure they protect your customers.

BLOG: Cake's security and compliance commitment

What tools are available if you decide to build?

If you've decided to take on the challenge of building your own AI voice agent, you won't be starting with a blank slate. The AI development landscape is full of powerful tools designed to give your team a head start. These resources range from flexible open-source frameworks that offer deep customization to large-scale enterprise platforms that provide a more structured environment. Understanding the options available is the first step in mapping out your technical strategy. Your choice of tool will heavily influence your development process, the skills your team will need, and how quickly you can move from a concept to a functioning prototype.

Frameworks like LangChain and Crew AI

For teams that are comfortable with coding and want maximum control, open-source frameworks are a popular starting point. Tools like LangChain provide a structured way to connect large language models (LLMs) with other data sources and APIs, giving you the essential building blocks for a sophisticated agent. This approach is ideal if you need to create highly customized logic and have the in-house expertise to manage the development lifecycle. While frameworks offer incredible flexibility, they also place the full responsibility of building, deploying, and maintaining the application squarely on your team's shoulders. It's a powerful but demanding path that requires deep technical knowledge.

Prototyping tools like AutoGPT and FlowiseAI

If your goal is to test ideas and iterate quickly, prototyping tools can be incredibly valuable. Options like AutoGPT are designed for rapid experimentation, allowing you to see what's possible without committing to a full-scale development project from day one. Many of these tools are also open-source, offering a high degree of customizability for your initial tests. However, it's important to remember the gap between a prototype and a production-ready system. While these tools are great for building a proof of concept, scaling that prototype into a secure, reliable, and robust agent that can handle real customer interactions is a significant leap that requires a more comprehensive engineering effort.

Enterprise platforms like Google Dialogflow

On the other end of the spectrum are the major enterprise platforms from tech giants. Services like Google Dialogflow and Microsoft Azure offer comprehensive suites of tools for creating conversational AI. These platforms often come with more built-in infrastructure, support, and a more guided development experience compared to open-source frameworks. While they can simplify some aspects of the build process, they can also introduce complexity in other areas, such as pricing models and the risk of vendor lock-in. This is where the complexity of managing the AI stack becomes clear—a challenge that managed solutions like Cake are designed to solve by providing production-ready open-source alternatives.

How different industries are using voice AI

Voice AI is incredibly versatile, but its real power comes from applying it to specific industry challenges. Seeing how other businesses in your field use this technology can spark ideas and show you what’s possible. From streamlining sales to improving patient care, AI voice agents are becoming essential tools for getting work done more efficiently and creating better customer experiences. The key is to move beyond generic applications and find the use cases that solve the most pressing problems for your business and your customers. A well-designed voice agent can feel less like a robot and more like a helpful, capable assistant, no matter the industry.

Examples from real estate, healthcare, and retail

In real estate, agents are using voice AI for everything from initial lead nurturing to post-sales engagement. Instead of spending hours on the phone, an AI agent can handle initial inquiries, schedule viewings, and follow up with potential buyers, freeing up human agents to focus on closing deals. The healthcare industry is also seeing a major shift, with AI voice agents helping to automate appointment scheduling, send out medication reminders, and answer common patient questions. This improves administrative efficiency and gives patients faster access to information. Meanwhile, retail businesses are using voice AI to provide seamless customer support, assisting with product questions, order tracking, and returns management around the clock.

Success Story: How Ping established ML-based leadership in commercial property insurance

How to find a solution for your specific industry

No matter your industry, simply plugging in a generic voice agent won’t cut it. Selecting the right tool that aligns with your specific business needs is what ultimately enhances the customer experience and builds lasting relationships. As you evaluate your options, whether building or buying, think about the features that matter most for your sector. Look for capabilities like multilingual support if you serve a diverse customer base, or smart interruption handling for more natural conversations. It's also critical to consider integration capabilities with your existing CRM and other systems, along with robust analytics that give you insight into performance. The goal is to find or build a solution that feels like a natural extension of your team.

How to evaluate pre-built solutions

So, you're leaning toward buying a pre-built solution. That’s a smart move for getting to market quickly, but now comes the important part: sifting through the options to find the one that truly fits your business. It’s not just about picking the flashiest tool. It’s about finding a solution that solves your specific problems and a partner you can grow with. Let's break down what you should be looking for on both fronts so you can make a choice with confidence.

What are the must-have features to look for?

When you start comparing AI voice agents, it’s easy to get lost in the feature lists. To cut through the noise, focus on the capabilities that directly impact customer experience and your team's efficiency. Your agent should sound natural and offer human-like speech, not a robotic monotone. It needs to handle interruptions gracefully, just like a real person would in conversation. Also, look for the ability to execute a seamless hand-off to a human agent when a call gets too complex. Behind the scenes, strong integration with your backend systems, customization options, and clear analytics tools are non-negotiable for making the agent a core part of your operations.

How to choose the right partner

The software is only half of the equation; the partner you choose is just as critical. Some providers offer a simple, plug-and-play SaaS platform, which can be great for speed. However, a true partner will offer a solution that gives you more control and long-term value. Look for a provider that lets you train the agent on your own knowledge base—this is how you transform a generic tool into a specialist with deep contextual knowledge of your business. A great partner provides a comprehensive solution that manages the entire stack, from infrastructure to integrations, giving you a production-ready asset that you can truly own and adapt as you grow.

How to create strategic value with your AI agent

Choosing an AI voice agent is a great first step, but the real value comes from how you use it. A truly strategic agent isn't just a piece of software; it's a core part of your business operations that drives real results. It should make your team more effective, your customers happier, and your processes smoother. To get there, you need to think beyond the initial setup and focus on deep integration, measurable business outcomes, and getting your whole team on board. This is how you turn a cool piece of tech into a genuine competitive advantage.

Focus on deep integration with your workflows

Your AI voice agent can’t operate in a silo. To be effective, it must communicate seamlessly with the tools you already use, like your CRM and helpdesk software. This integration is what allows the agent to move beyond simple Q&A and perform valuable tasks, like looking up a customer's order history or creating a new support ticket. When your agent is fully connected to your tech stack, you create a smooth, consistent journey for your customers, so they never have to repeat information. This level of integration is crucial for improving the customer experience, all while upholding your company's commitment to security and compliance.

Deliver business results, not just tech

The goal of any business investment is a positive return. With a self-built AI agent, the path to breaking even can be long and uncertain, filled with unexpected costs and development delays. Buying a pre-built agent allows you to see a return much faster, since the initial and ongoing costs are lower and more predictable. Instead of focusing on the technology itself, you can concentrate on the business results it delivers: lower operational costs, higher first-call resolution rates, and a support team that's free to handle high-value customer interactions. The best AI solution is one that solves real problems and contributes directly to your bottom line.

Prioritize user adoption across your company

An AI agent is only successful if people actually use it—and that includes both your customers and your internal team. Your choice to build or buy directly impacts your time to market and your ability to grow without friction. Deploying a pre-built solution quickly means you can start getting it into the hands of your team and customers sooner. This speed gives you a powerful advantage, allowing you to gather real-world data, iterate quickly, and show your team how the agent can make their jobs easier. When your employees see the agent as a helpful tool that handles repetitive tasks, they're more likely to embrace it, leading to smoother operations and a better experience for everyone.

So, which path is right for your business?

Deciding between building and buying an AI voice agent is a major strategic move. The best path depends on your resources, goals, and how central AI is to your business. Let's walk through the scenarios to help you find the approach that fits your company.

When does it make sense to build?

Building an AI voice agent from the ground up is a serious commitment, but it can be the right call in specific situations. If AI is your core product or main competitive advantage, you'll want full control over its development. This path also makes sense if you handle highly sensitive data or need deep integrations with internal systems. While building can offer long-term cost efficiency, the upfront investment is steep. You're looking at high salaries for specialized AI talent, expensive infrastructure, and a development timeline that can stretch for months or even years. This is a strategic decision with major financial implications.

When should you buy a solution?

For most companies, buying a ready-made AI solution is the most efficient path forward. This approach allows your engineering team to stay focused on your core business functions and innovate where it matters most, rather than reinventing the wheel. Opting for a pre-built agent lets you bypass the high upfront costs and long development cycles of an in-house build. You can get to market faster and start seeing a return on your investment sooner. The key is to choose a solution that delivers high automation rates without sacrificing quality or customer satisfaction. The goal is to improve your operations, not create new problems.

Using a managed platform to accelerate development

What if you could get the speed of a pre-built solution without giving up the power to customize? That’s the idea behind using a managed platform. This approach gives you a powerful middle ground, where a partner handles the entire technical stack—from the compute infrastructure to the core integrations and maintenance. This frees your team from getting bogged down in complex AI infrastructure and allows them to focus on what truly matters: tailoring the agent to your specific business needs. Choosing a provider that offers production-ready open source AI solutions means you get a scalable, future-proof foundation, letting you launch faster and adapt as you grow, giving you the best of both building and buying.

Could a hybrid approach be the answer?

You don't have to be stuck between building from scratch and buying a rigid, off-the-shelf product. A hybrid approach, where you partner with an expert to manage the technical heavy lifting, offers a powerful middle ground. This lets you tap into proven expertise and scalable infrastructure while still getting a solution tailored to your needs. By outsourcing the AI stack's development and management, you get to market faster and reduce risk, freeing your team to focus on what they do best. Finding a partner that offers a comprehensive solution can give you the best of both worlds: the speed of a pre-built system with the flexibility of a custom build.

The world of AI moves fast, and the choice you make now will set the foundation for your customer experience for years to come. To make sure your investment pays off long-term, you need a strategy that can grow and change right along with the technology and your customers' expectations.

Common mistakes to avoid when getting started

The world of AI moves fast, and the choice you make now will set the foundation for your customer experience for years to come. To make sure your investment pays off long-term, you need a strategy that can grow and change right along with the technology and your customers' expectations. While the potential is exciting, it's easy to stumble if you rush in. Let's cover some of the most common mistakes businesses make when getting started with AI voice agents so you can avoid them.

Launching without a clear plan

It’s tempting to jump straight into development, but launching without a clear strategy is one of the fastest ways to derail your project. Before you write a single line of code or sign a contract, you need a plan that outlines your goals, expected outcomes, and the resources required. Are you trying to reduce customer wait times, lower operational costs, or handle more inquiries after hours? Having specific, measurable goals will guide every decision you make, from choosing the right features to defining what success looks like. A well-defined strategy is your roadmap, ensuring your AI agent solves real business problems instead of just becoming a costly experiment.

Automating too much, too soon

The power of automation can be alluring, but trying to automate everything at once can seriously backfire. When your engineers are focused on building complex AI tools, they aren't working on your company's core products or services, which can slow down your business. Instead of a massive, all-or-nothing launch, start small. Identify the most common, repetitive customer inquiries and automate those first. This phased approach allows you to learn, gather data, and demonstrate value quickly without pulling your entire team away from their primary responsibilities. It’s a smarter way to scale your AI initiative and ensure it supports your business goals, rather than distracting from them.

Forgetting post-launch improvements

An AI voice agent isn't a crockpot—you can't just set it and forget it. The initial launch is just the beginning of the journey. Your agent needs continuous monitoring, maintenance, and optimization to stay effective. Hidden costs like model upgrades, security patches, and performance tuning can quickly strain your resources if you aren't prepared for them. This is why ongoing support is so critical. Whether you build in-house or partner with a provider, you need a plan for post-launch improvements. Working with a partner like Cake, which manages the entire stack, can free you from these ongoing technical burdens, allowing you to focus on refining the customer experience instead of just keeping the lights on.

Making it difficult to reach a human agent

Your AI voice agent should be a helpful guide, not a frustrating gatekeeper. One of the biggest mistakes you can make is designing a system that traps customers in an automated loop with no clear path to a human. Some problems simply require the empathy and complex problem-solving skills of a person. Your agent must be able to recognize its limits and seamlessly hand off the conversation to a human team member when needed. This collaboration between AI and human support is what creates a truly great customer experience. It ensures customers get the right help, from the right source, without any unnecessary friction.

How to plan for the future of your AI strategy

Making a decision about your AI voice agent isn't just about solving today's problems. The world of AI moves fast, and the choice you make now will set the foundation for your customer experience for years to come. To make sure your investment pays off long-term, you need a strategy that can grow and change right along with the technology and your customers' expectations. This means looking beyond the immediate build-versus-buy question and thinking about how your choice will support your business in the future. A forward-thinking approach focuses on two key areas: staying current with the direction of the industry and ensuring your technical solution is built to last.

Staying on top of industry trends

The role of AI in business is expanding at an incredible pace. Voice agents, in particular, are quickly becoming essential tools for customer interaction. Some experts even call them the "gateway to AI" because they're so intuitive and effective. With predictions suggesting that the vast majority of customer service conversations will soon be handled by AI, it's clear that voice is not just a trend—it's a fundamental shift in how we connect with customers. Staying aware of these changes helps you understand the stakes and make a choice that positions you for what's next, not just what's happening now.

The commoditization of base-level AI tech

Just a few years ago, launching an AI voice agent was a massive technical achievement. Today, that's no longer the case. The core technology is quickly becoming a commodity—it's widely available and easier than ever to use. While this accessibility is great, it also means the bar for success is much higher. Business models that only focus on a simple setup are becoming outdated because the real strategic advantage no longer comes from just having an agent. Instead, value comes from how well that agent is integrated, customized, and managed for the long haul. It’s about moving beyond the basics to deploy a solution that is truly production-ready and tailored to solve your specific business challenges.

Why flexibility and adaptability matter

A future-proof strategy hinges on flexibility. Whether you decide to build your own agent or buy a pre-built one, the solution must be able to adapt. Look for core capabilities like multilingual support, smart interruption handling, and seamless hand-offs to human agents when needed. Your agent should also integrate smoothly with your existing backend systems and provide deep analytics. This adaptability is what allows you to refine your customer experience over time and incorporate new features as they emerge. Choosing the right AI voice agent isn't a one-time setup; it's about selecting a partner or building a foundation that can evolve with your business needs and help you build lasting customer relationships.

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• Voice Agents, Built With Cake
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• How to Build an AI Voice Agent: A Practical Guide
• What Are AI Voice Agents? A Guide for Business

Frequently asked questions

What's the biggest difference between building our own AI voice agent and buying one? 

The main difference comes down to focus and long-term responsibility. When you build, you're not just creating a tool; you're launching an entire internal project that requires a dedicated team for development, ongoing maintenance, and constant updates. Buying a solution means you're partnering with experts who handle all that technical heavy lifting. This allows your team to stay focused on your core business while still getting the benefits of a powerful, professionally managed AI tool.

We're not a huge company. Is an AI voice agent still a good investment for us? 

Absolutely. In fact, buying a pre-built solution makes this technology incredibly accessible for businesses of all sizes. Building an agent from scratch often only makes financial sense at a massive scale, but a ready-made solution allows you to see a return on your investment much faster. It can act as a virtual receptionist or handle routine customer questions, giving you the power of a larger support team without the associated costs.

If we buy a pre-built agent, will it sound robotic and generic? 

That’s a common concern, but modern AI voice agents are far from the robotic voices of the past. The key is choosing the right solution. A quality agent can be trained on your company’s specific knowledge base, allowing it to provide accurate, contextual answers. Look for features like human-like speech and the ability to handle conversational interruptions, which make the interaction feel natural and helpful, not generic.

How much technical work is involved in getting a pre-built agent running? 

Getting a pre-built agent live is much more straightforward than building one yourself. The core technology is already developed, so your main task is configuration and integration. A good provider will offer seamless connections to your existing tools, like your CRM or helpdesk software. While some technical input is needed, it's a far cry from the months or years of development required for an in-house build.

How do we know if the voice agent is actually working well? 

You measure its effectiveness just like you would a human team member. A key metric to watch is First Call Resolution, which tracks how often the agent solves a customer's problem on the first try without needing to pass the call to a person. By monitoring performance data and establishing a quality assurance process, you can get a clear picture of what’s working and continuously refine the agent to better serve your customers.