How to Choose the Best AI Platform for Your Business

The rise of AI is transforming how businesses operate—from personalized customer experiences to intelligent automation and faster decision-making. But turning that potential into real-world impact requires more than experimentation—it demands the right platform.

Today’s AI platforms aren’t just technical tools. They provide the infrastructure, governance, and automation needed to scale AI across teams and environments. Whether you’re just getting started or expanding your AI footprint, choosing the right platform is key to turning ambition into results.

Key takeaways

• Pinpoint your AI objectives: Clearly define what you want AI to accomplish for your business; this clarity helps you select a platform with the right tools.
  
  
• Prioritize practicality and protection: Assess how easily your team can use a platform and how well it integrates with your current setup, always ensuring robust security and data management are in place.
  
  
• Plan for today and tomorrow: Address potential hurdles, such as data quality and costs, early on, and choose a scalable AI platform that can adapt to your growing needs and new AI advancements.
  
  

What are AI platforms (and why do they matter for your business)?

So, what exactly are AI platforms? Think of them as your all-in-one toolkit for bringing AI into your business. These platforms are integrated sets of technologies designed to help you develop, test, deploy, and even refresh your machine learning (ML) and deep learning models. Essentially, they make the complex world of AI much more accessible, allowing your business to tap into its power without needing to build everything from scratch or have a massive team of specialized engineers.

Now, why should this matter to your business? Well, AI platforms are game-changers for efficiency and innovation. They empower you to automate tasks that humans usually handle, using AI, ML, and natural language processing to streamline operations. Imagine freeing up your team from repetitive work so they can focus on more strategic initiatives! 

Beyond just saving time, adopting AI can significantly impact your bottom line. Investing in an AI platform isn't just about keeping up with technology; it's about driving real growth and maintaining a competitive edge.

Essential AI platform features 

When you're exploring AI platforms, it can feel like trying to learn a whole new language filled with technical terms and impressive-sounding capabilities. But here’s a little secret: you don’t need to be a seasoned data scientist to figure out what makes an AI platform a real asset for your business. The trick is to focus on the core features that will genuinely help you build, launch, and manage your AI projects smoothly. Think of these features as the must-haves in your AI toolkit—each one is vital for transforming raw data into informed actions and intelligent applications that can truly drive results.

Choosing the right platform isn't just about ticking off a list of features; it's about understanding how these capabilities line up with what you want to achieve.

Choosing the right platform isn't just about ticking off a list of features; it's about understanding how these capabilities line up with what you want to achieve. Are you looking to automate repetitive tasks, get a deeper understanding of your customers, or perhaps make more accurate predictions about future market trends? The right combination of features will set you up for success. So, let's walk through some of the most important ones you should keep an eye out for. Knowing these will help you ask the important questions and pick a platform, like Cake, which focuses on streamlining your entire AI stack, that not only fits your needs today but can also grow with your business down the road.

1. Modular, cloud-neutral architecture

Your AI platform shouldn’t box you into a single vendor or rigid toolset. A modular architecture lets you pick the best tools for each job—whether it’s using open-source model serving, proprietary LLM APIs, or your preferred data lake format. Cloud-neutral platforms give you the freedom to deploy across AWS, GCP, Azure, or even on-prem environments. This flexibility not only reduces vendor lock-in but also makes your infrastructure more resilient, portable, and adaptable to future needs. Cake is designed with modularity at its core, allowing you to scale AI workloads without being tied to any single ecosystem. Cake works with any cloud service, or even on-prem, giving you greater flexibility.

2. Integrated model orchestration

Successful AI development isn’t just about training a good model—it’s about managing the entire workflow. That includes ingesting and transforming data, tracking experiments, scheduling retraining, and deploying models to production. A strong AI platform will tie all these components together in a reproducible, version-controlled pipeline that can scale across teams. Cake gives you access to best-in-class orchestration tools like Langflow, and LlamaIndex, enabling fast iteration without sacrificing governance.

3. Secure and compliant by design

As AI becomes more central to business operations, security and compliance can’t be bolted on—they have to be built in. Your platform should support role-based access controls, secrets management, audit logging, and data privacy protections out of the box. Especially in regulated industries, this is what makes the difference between proof-of-concept and production readiness. Cake centralizes policy enforcement and compliance across the AI stack, helping teams meet security standards without slowing down development.

4. Built-in scalability and compute abstraction

AI workloads are resource-intensive, and your platform needs to handle that complexity gracefully. Look for infrastructure that can scale up and down dynamically, using the right GPU, TPU, or CPU for the task at hand. Ideally, you shouldn’t have to think too much about infrastructure at all. Cake abstracts compute provisioning across clouds and environments, helping teams save time and money while ensuring consistent performance. This kind of automation becomes essential as models get larger and applications grow.

5. First-class support for GenAI and agents

The AI landscape is evolving fast—from basic classification models to full-blown agents that reason, plan, and act using LLMs. Your platform should support this shift with native tools for GenAI development, including prompt orchestration, tool usage, context management, and memory. Cake integrates with popular tools like LangGraph, CrewAI, Pipecat, and AutoGen and provides a secure, scalable foundation for deploying agentic AI in the real world, whether you’re building chatbots, retrieval-augmented generation (RAG) apps, or autonomous workflows. 

The AI landscape is evolving fast—from basic classification models to full-blown agents that reason, plan, and act using LLMs. Your platform should support this shift with native tools for GenAI development...

6. Unified observability and monitoring

When AI systems go into production, you need real-time visibility into what they’re doing and how they’re performing. That means tracking not just system-level metrics, but also model-level insights like drift, latency, failure rates, and cost per inference. A strong AI platform will give you observability across the entire stack—from data pipelines to model endpoints—so you can detect issues early and keep your applications reliable. Cake builds observability into every layer, giving teams a clear view of both infrastructure health and model behavior.

These aren’t just “nice-to-haves”—they’re what make modern AI development scalable, secure, and repeatable. Cake was purpose-built around these exact principles, helping teams manage the entire AI stack with confidence, from prototype to production.

AI platform pricing: What to expect

Figuring out AI platform costs can feel like solving a puzzle. Companies must consider multiple factors, and each vendor approaches them slightly differently. The good news? Several standard pricing models are available across providers. Understanding these will help you find a plan that fits your budget and use case. 

Let's look at the typical ways AI platforms structure their pricing so you can make an informed choice for your business.

Pay-as-you-go options

If flexibility is high on your list, pay-as-you-go models are worth exploring. With this approach, you only pay for the resources you actually use. This can be fantastic if your AI needs fluctuate or if you're just starting to explore AI capabilities and want to avoid big upfront commitments. Some platforms include free usage credits or tiered plans, making it easier to experiment without major upfront costs. It’s a straightforward way to manage costs, especially when you’re scaling your AI initiatives.

Subscription-based plans

For businesses that prefer predictable monthly or annual expenses, subscription-based plans are a popular choice. These plans often bundle together a set amount of resources, features, and support for a flat fee. This makes budgeting easier and can offer cost savings if you have consistent AI usage. When comparing AI tools, you'll find various subscription tiers, allowing you to select one that aligns with your current needs and anticipated future growth. It’s all about finding that sweet spot between features and affordability.

Enterprise solutions

Larger businesses or those with highly specific and complex AI requirements often find that enterprise solutions are the best fit. These plans are typically custom-quoted and tailored to your organization's unique demands, offering extensive features, dedicated support, and robust data processing capabilities. While they represent a more significant investment, enterprise solutions provide the scalability and comprehensive tools needed for large-scale AI deployments and integrations. If your AI strategy is a core part of your operations, this route offers the most power and customization.

Free trials and special offers

Don't overlook the power of a test drive! Many AI platform providers offer free trials or special introductory credits, which are fantastic for getting hands-on experience before you commit financially. For instance, new customers on some platforms might receive free credits to experiment with various services. These offers allow your team to assess the platform's interface, test its features with your own data, and see if it truly meets your needs. It’s a smart, low-risk way to ensure the platform is the right match for your AI goals.

How AI platforms benefit your industry

It's one thing to talk about AI in theory, but it's another to see it making a real difference in the world around us. AI platforms are the engines driving this transformation, providing the essential tools and robust infrastructure businesses need to implement AI solutions effectively. Think of them as the launchpad for your AI initiatives, helping you get from idea to impact much faster and more efficiently. Companies like Cake specialize in offering comprehensive, production-ready solutions that manage the AI stack—from compute infrastructure to open-source elements and pre-built project components. This approach streamlines deployment, allowing businesses to focus on driving success with AI rather than getting bogged down in complex setups.

The exciting part is that AI isn't limited to just one or two niche areas. Its versatility means it's creating positive change and delivering tangible benefits across a whole spectrum of industries.

The exciting part is that AI isn't limited to just one or two niche areas. Its versatility means it's creating positive change and delivering tangible benefits across a whole spectrum of industries. You might be surprised by the diverse ways AI is being applied! From how doctors approach patient care and diagnose illnesses to the way your favorite online stores offer personalized shopping recommendations, AI is quietly reshaping our daily experiences and revolutionizing business operations. It’s about making processes smarter, insights deeper, and outcomes better. Let's look at how these platforms are specifically benefiting various sectors, bringing real-world value every day.

Healthcare and life sciences

The healthcare and life sciences fields are seeing some truly amazing advancements thanks to AI. While the industry often moves with caution, the potential for AI to enhance patient care and streamline operations is undeniable, leading to a surge in its adoption. AI platforms are enabling professionals to analyze complex medical data more efficiently than ever before, resulting in faster diagnoses and more personalized treatment plans. Imagine AI assisting researchers in discovering new drugs or helping hospitals manage resources more efficiently. It's about empowering healthcare providers with smarter tools so they can focus on what matters most: patient well-being. This means better outcomes and a more efficient healthcare system for everyone.

Finance and banking

In the fast-paced world of finance and banking, AI platforms are becoming indispensable. These systems are powerhouses for tasks such as detecting fraud in real-time, managing algorithmic trading, and automating customer service inquiries. Consider the vast volume of transactions and data that banks process daily; AI helps make sense of it all, leading to more secure and efficient operations. Predictive analytics, a key feature of many AI platforms, can assess risk with greater accuracy and offer customers more personalized financial advice. This not only helps protect institutions and their clients but also paves the way for innovative financial products and services.

Retail and e-commerce

If you've ever been impressed by spot-on product recommendations online, you've likely experienced AI in retail. AI platforms are transforming the shopping experience by enabling businesses to understand their customers on a deeper level. They power everything from personalized suggestions and dynamic pricing strategies to sophisticated inventory management. For retailers, this means optimizing supply chains, reducing waste, and ensuring popular products are always in stock. For shoppers, it translates to a smoother, more intuitive, and ultimately more satisfying journey, whether they're browsing online or in a physical store. It’s all about making shopping smarter and more enjoyable.

Manufacturing and supply chain

The manufacturing sector is undergoing a significant upgrade with the help of AI. Platforms in this space are helping companies refine production quality, streamline complex supply chains, and even predict when machinery might need maintenance before a breakdown occurs. This proactive approach saves time, reduces costs, and keeps operations running smoothly. AI also plays a role in designing innovative new products and finding ways to conserve energy, making manufacturing more efficient and sustainable. By optimizing processes from start to finish, AI platforms empower manufacturers to stay competitive and deliver better products to market faster.

Marketing and advertising

Marketers are finding a powerful ally in AI platforms. These tools are revolutionizing how brands connect with their audiences by enabling incredibly personalized content delivery and offering deep insights into customer behavior through predictive analytics. Imagine being able to tailor your message so precisely that it resonates perfectly with each individual customer—that's the power AI brings. Beyond personalization, AI also automates many aspects of campaign management, freeing up marketing teams to focus on strategy and creativity. From optimizing ad spend to understanding market trends, AI helps businesses create more impactful and efficient marketing efforts that truly connect.

Insurance

AI is helping insurers modernize everything from claims to customer service. With automation and computer vision, claims can be reviewed and approved faster, reducing delays for policyholders. In underwriting, AI analyzes large datasets—like health records or driving behavior—to better assess risk and offer more personalized coverage. It also strengthens fraud detection by identifying patterns that might signal suspicious activity. On the customer side, AI chatbots and virtual agents provide instant support around the clock, helping users manage policies, file claims, or get answers without waiting on hold.

You want an AI platform that not only delivers powerful results but also keeps your data safe, respects privacy, and scales with your business. It’s about building a sustainable and trustworthy AI practice.

Performance, security, and compliance

Alright, so you're exploring AI platforms and getting excited about all the cool things they can do—from automating tasks to uncovering amazing insights. That’s fantastic! However, as we delve into the details, there are a few crucial, non-negotiable aspects we need to address: performance, security, and compliance. Think of these as the bedrock of your AI initiatives. Without them, even the most advanced AI features can fall short or, worse, create problems.

You want an AI platform that not only delivers powerful results but also keeps your data safe, respects privacy, and scales with your business. It’s about building a sustainable and trustworthy AI practice. This is where a comprehensive solution, such as what we aim to provide at Cake by managing the entire stack from compute infrastructure to pre-built project components, becomes invaluable. It’s not just about having the tools, but also about ensuring they work together seamlessly to keep things running smoothly, securely, and in accordance with all necessary guidelines. Let’s break down what this means for you.

Scalability and processing power

When you're diving into AI, especially with ML models, you're often dealing with massive amounts of data and complex calculations. Your AI platform needs the muscle to handle this. Scalability means the platform can grow with you. Whether you’re starting with a pilot project or deploying AI across your entire organization, you need assurance that the system can handle increasing data volumes and user demands without a hitch. This involves having access to robust infrastructure and tools capable of developing, training, and deploying these models efficiently. Without adequate processing power, your AI projects can slow to a crawl, delaying those valuable insights you’re after and hindering your ability to truly accelerate your AI initiatives.

Accuracy and precision

The insights and actions driven by your AI are only as good as the AI's accuracy. If your AI model is making incorrect predictions or classifications, it can lead to poor business decisions, wasted resources, or even damage your brand's reputation. High accuracy and precision come from well-trained models using quality data. It's crucial that the AI platform supports robust model training and validation processes. As AI adoption grows, its ability to help businesses gain strategic insights and improve customer experiences hinges on this reliability. This is especially true in fields like manufacturing, where AI can optimize production and predict equipment failures, but only if its outputs are consistently trustworthy.

Data management and privacy

AI models are hungry for data; it's what they learn from. So, how you manage that data is incredibly important. This includes everything from how data is ingested, stored, processed, and governed. You need effective data management to maximize the value of your AI investments. Crucially, this also ties into the issue of privacy. With increasing awareness and regulations around data privacy, your AI platform must offer strong capabilities to handle data responsibly, ensuring that sensitive information is protected. Many platforms allow you to build or use open-source algorithms to process your internal data, giving you control while emphasizing the need for careful, privacy-conscious data handling from the outset.

Regulatory compliance

Speaking of data privacy, regulatory compliance is a big one. Depending on your industry and the location of your operations, you’ll be subject to various regulations, including GDPR, CCPA, HIPAA, and others. Your chosen AI platform should help you meet these obligations, not make it harder. This means looking for platforms that have built-in features for data governance, audit trails, and tools that support compliant data handling. The importance of data governance and compliance is only growing, so choosing a platform that prioritizes these aspects is essential for long-term success and avoiding hefty fines or legal issues. It’s about building trust with your customers and stakeholders.

Security protocols and risk mitigation

Finally, let's talk security. When you're working with valuable business data and powerful AI models, robust security protocols are non-negotiable. This includes protecting against unauthorized access, data breaches, and other cyber threats. Your AI platform should offer comprehensive security features, like encryption, access controls, and threat detection. As AI becomes more integrated into critical sectors like finance and transportation, the need for stringent security and risk mitigation strategies is paramount to protect sensitive information and maintain operational integrity. Think about how you’ll manage these risks from day one to ensure your AI journey is a secure one.

• READ: Cake's Security Commitment
  
  

Common AI platform challenges

Bringing AI into your business operations is an exciting step, but like any significant tech adoption, it comes with its own set of hurdles. Being aware of these common challenges upfront means you can plan effectively and find the right support to overcome them. Think of it as preparing for a marathon—knowing the tough spots on the course helps you train smarter! With a strategic approach, these challenges become manageable steps on your path to AI success. Platforms like Cake aim to simplify this journey by managing the complex underlying infrastructure, including compute resources and open source elements, allowing you to focus on the AI applications themselves rather than getting bogged down in the foundational setup. This means you can launch your AI initiatives more efficiently.

Cost management

Let's be real: implementing AI isn't always cheap. There are initial setup costs, ongoing maintenance, and the potential need for specialized talent. While AI platforms can automate tasks and improve decision-making, it's smart to understand the full financial picture from the get-go. Think about creating a detailed budget that includes software, infrastructure (e.g., compute power, which a comprehensive solution can help manage), data preparation, and training for your team. Also, consider the long-term return on investment. Sometimes, a platform that handles the end-to-end infrastructure may seem like a bigger initial investment. Still, it can save you money in the long run by streamlining processes and reducing the need for multiple, disparate tools or extensive in-house infrastructure management.

Security risks

Introducing new technologies, especially those as powerful as AI, naturally brings security to the forefront of our minds. AI systems often work with vast amounts of data, some of which can be sensitive. It's absolutely crucial to implement robust security measures to protect this data and maintain the trust of your customers and stakeholders. This involves data encryption, access controls, and conducting regular security audits to ensure optimal security. When selecting an AI platform, inquire about its integrated security features and how it facilitates compliance with data protection regulations. A platform that prioritizes security and offers a managed environment can be a huge asset in safeguarding your operations and sensitive information.

Data quality

You've probably heard the saying, "garbage in, garbage out." This is especially true for AI. The performance and accuracy of your AI models are directly tied to the quality of the data you feed them. If your data is messy, incomplete, or biased, your AI outcomes will likely be unreliable. That's why prioritizing data quality is non-negotiable. This involves cleaning your data, ensuring its accuracy, and making sure it's relevant to the problem you're trying to solve. Many AI platforms offer tools for data processing and management, which can significantly help in preparing your data for AI applications, ensuring your models are built on a solid foundation.

AI bias

AI models learn from the data they're trained on. If that data reflects existing societal biases, the AI can unintentionally learn and even amplify those biases. This is a serious concern, as it can lead to unfair or discriminatory outcomes. It's crucial to work actively to prevent discriminatory AI in your systems. This involves carefully curating diverse and representative training datasets, regularly auditing your models for biased behavior, and establishing clear ethical guidelines for AI development and deployment. Some platforms are beginning to incorporate tools to help detect and address bias, which is a positive step forward in building more equitable AI.

Change management

Successfully integrating AI into your business isn't just about the technology; it's also about your people. AI can change workflows and job roles, and it's natural for employees to feel uncertain or resistant. Effective change management strategies are key to a smooth transition. This means clear communication about why AI is being implemented and how it will benefit everyone. Investing in training and support to help your team develop new skills and adapt to new processes will make a world of difference. When employees feel supported and understand the value AI brings, they're more likely to embrace it and contribute to its success.

Choose the AI platform for your business

Picking the right AI platform is a big step, but it doesn’t have to be overwhelming. Think of it like choosing a key partner for your business—you want one that fits your current needs and can grow with you. Let's walk through some practical steps to help you find the best match.

Assess your needs

First things first, get clear on what you actually need an AI platform to do. "Choosing the right AI platform for your needs requires a careful evaluation of each platform’s features, strengths, and weaknesses," as experts at Bilderberg Management wisely note. What specific problems are you trying to solve? Are you looking to automate customer service, analyze large datasets, or develop new AI-powered products? Make a list of your must-have features versus nice-to-haves. Consider the types of data you'll be working with and the technical skills your team already possesses. This initial homework will make the selection process much smoother and ensure you select a platform that truly serves your operational requirements.

An AI platform isn't just a tech toy; it's a strategic tool. Think about your long-term vision. How will this AI platform help you achieve your broader business goals, like increasing efficiency, improving customer satisfaction, or innovating in your market?

Align with business goals

An AI platform isn't just a tech toy; it's a strategic tool. Think about your long-term vision. How will this AI platform help you achieve your broader business goals, like increasing efficiency, improving customer satisfaction, or innovating in your market? Ensure the platform’s capabilities directly support these objectives. A platform like Cake, which focuses on accelerating AI initiatives with production-ready solutions, can help ensure this alignment by streamlining the path from concept to real-world impact.

Evaluate support and community resources

Even the most intuitive AI platform can come with a learning curve or unexpected challenges. Look for platforms that offer comprehensive documentation, responsive customer support, and an active user community. These resources are invaluable for troubleshooting issues, sharing best practices, and maximizing the value of your investment. Good support can make all the difference when you’re working with new AI functionalities or integrating the platform into your existing workflows, helping your team feel confident.

Consider future scalability

Your business isn't static, and your AI platform shouldn't be either. It's vital to pick a solution that can grow with you. Will the platform be able to handle increasing volumes of data as your business expands? Can it support more users and more complex AI models down the line? Opting for a platform designed for scalability, perhaps one that manages the entire compute infrastructure like Cake does, ensures you won’t hit a wall as your AI ambitions grow and your operational demands increase.

Top AI platforms 

Once you’ve defined your goals and understand what features matter most, the next step is to evaluate how today’s leading platforms actually deliver. This section compares the major players in the AI space, highlighting their strengths, weaknesses, and how Cake stands out as a modern alternative. Whether you’re navigating cloud lock-in, dealing with complex infrastructure, or seeking simplicity at scale, understanding these differences will help you make a more informed and confident decision.

Google Vertex AI

Vertex AI provides deep integration across Google Cloud and a comprehensive suite of tools for model training and deployment. AutoML is a helpful feature for building custom models without extensive manual coding. However, Vertex often locks users into the Google ecosystem, limiting portability and flexibility across clouds. Teams that need hybrid deployments or more control over their infrastructure may find it restrictive. In contrast, Cake’s cloud-neutral approach supports broader enterprise AI strategies without vendor lock-in.

Amazon SageMaker AI

SageMaker is Amazon’s flagship ML service, built for scale and backed by AWS’s powerful cloud. But with great power comes great complexity. Many users report steep learning curves, scattered documentation, and a need for significant DevOps involvement. For teams seeking simplicity or faster time to value, SageMaker may feel cumbersome. Cake abstracts much of this complexity, helping teams get from prototype to production faster.

• READ: Why Cake Beats AWS SageMaker for Enterprise AI 

Azure Machine Learning

Azure ML is often appealing for Microsoft-heavy enterprises, offering integrated access to tools like ChatGPT and enterprise security layers. However, the platform can be rigid, with limited openness and a focus on Microsoft-centric tooling. Teams seeking cross-cloud flexibility or integration with open-source ML tools may feel constrained. Cake supports both open-source and proprietary model workflows without enforcing a narrow stack.

Dataiku

Dataiku stands out for its user-friendly approach and emphasis on cross-team collaboration. But while its pre-built apps and governance tools are great for initial AI democratization, the platform can lack the depth or flexibility needed for custom, production-grade deployments. Cake provides both a developer-friendly interface and enterprise-ready infrastructure to take projects further.

Databricks

Databricks is a powerful platform for data engineering, analytics, and Spark-based machine learning. It shines in unified data lakehouse architectures and is widely used for large-scale data prep and ML experimentation. However, when it comes to modern AI use cases—like GenAI apps, LLM fine-tuning, or multi-cloud model deployment—Databricks can feel overly complex and tightly coupled to its own ecosystem. While it’s great for Spark pipelines, many teams find themselves needing more modularity, lower-level infrastructure control, or better support for open-source GenAI tools.

IBM Watson

IBM Watson pioneered enterprise AI but has struggled to keep pace with more nimble, developer-friendly platforms. Its strengths in NLP and unstructured data remain, but the platform is often seen as heavyweight and expensive. For businesses needing fast iteration or broader AI capabilities, Watson may not deliver the agility required today.

Cake

Cake is a modern, cloud-agnostic AI platform purpose-built for enterprises that want full control without complexity. Unlike traditional platforms tied to a single cloud or ecosystem, Cake provides a modular, open-source-friendly stack that spans the entire AI lifecycle—from data ingestion to deployment and compliance.

It’s designed to reduce infrastructure overhead (saving some teams $500K–$1M per year), support cutting-edge open tools, and give teams the security, performance, and agility they need to scale AI across the business. Cake saves precious time by automating the AI platform setup process and keeping it updated.

Who is Cake best for?

• Teams building production-grade AI with open-source tools
• Enterprises trying to avoid cloud lock-in
• Orgs with security, compliance, or audit requirements
• Businesses seeking a lower total cost of AI infrastructure ownership

How Glean.ai cut costs and boosted LLM performance with Cake

Glean.ai—a fast-growing workplace search and knowledge assistant company—wanted more control over their LLM infrastructure. They were running into high costs, limited visibility, and vendor constraints with managed platforms.

By partnering with Cake, Glean brought their LLMs fully in-house. Cake provided the modular, cloud-agnostic foundation to manage training and inference across GPU environments—without sacrificing speed or developer velocity.

The results:

• 30–50% cost savings on inference workloads

• Improved performance with optimized GPU scheduling

• Full control over infrastructure and security

Today, Glean runs a production-grade RAG pipeline and multiple fine-tuned LLMs on Cake, built entirely on open source. Read the full success story.

The future of AI platforms: Key trends 

The world of AI is anything but static; it's constantly evolving, bringing new possibilities and tools to the forefront. If you're looking to make the most of AI for your business, keeping an eye on upcoming trends is super important. These aren't just fleeting buzzwords; they represent significant shifts in how AI is developed, deployed, and used. Understanding these key trends will enable you to make informed decisions and ensure your AI strategy remains future-proof. So, let’s explore what’s shaping the next wave of AI platforms and how you can get ready.

Agentic AI workflows

AI agents are quickly becoming one of the most exciting developments in the space. These are autonomous systems that can plan, take action, and use tools to achieve goals, often working through multi-step processes without constant human intervention. In other words, agents don’t just generate responses—they take action on your behalf, whether that’s querying a database, scheduling a task, or triggering an automation.

As this paradigm gains traction, AI platforms need to evolve to support agentic workflows. That means orchestrating not just models, but the tools, memory, context windows, and infrastructure agents that rely on them. From secure sandboxing to multi-modal input handling and state management, supporting agents at scale introduces new architectural demands.

Cake is ahead of the curve here. Its modular infrastructure and integration with popular agentic AI tools are ideal for powering production-ready AI agents, enabling teams to deploy complex multi-tool agents across clouds with observability, access control, and compliance built in. As agentic AI moves from labs to real enterprise deployments, platforms that support this complexity natively will lead the way.

• Read: Build powerful AI agents using cutting-edge open source tools with Cake 

Generative AI integration

You’ve likely heard a lot about Generative AI, and for good reason! It's rapidly moving from a cool concept to a core business tool. We're seeing Generative AI integrated across various business functions in nearly half of the companies, fundamentally changing how we approach operations and sparking creativity. Think beyond just chatbots; this technology can help draft marketing copy, generate code, design product prototypes, and even assist in scientific discovery. As AI platforms continue to mature, expect to see more sophisticated and seamless generative capabilities built right in, making it easier for your teams to harness this transformative power for everyday tasks and big-picture innovation.

Edge computing

Imagine AI that works instantly, right where you need it, without sending data to a distant cloud. That's the power of edge computing in AI. This trend involves processing data closer to its source, which is a game-changer for industries such as manufacturing, healthcare, and automotive. For instance, AI at the edge can help manufacturers improve production quality, predict when machinery might fail, or optimize energy use in real-time. This approach not only speeds up decision-making but also enhances data privacy and reduces bandwidth costs. As more devices become "smart," expect AI platforms to offer robust support for developing and deploying AI models directly onto edge devices, bringing intelligence to the point of action.

No-code/low-code solutions

Used to think AI development was only for hardcore coders? Think again! The rise of no-code and low-code AI platforms is truly democratizing access to AI. These platforms offer intuitive, visual interfaces that allow people with minimal technical programming skills to build, train, and deploy AI applications. This means your marketing team could create a customer segmentation model, or your HR department could develop an AI tool to help screen resumes, all without needing to write complex code. This trend empowers more of your team to innovate with AI, speeding up development cycles and fostering a culture of AI adoption across your entire organization, making powerful AI tools accessible to everyone.

Collaborative AI ecosystems

In the evolving AI landscape, no platform is an island. The future holds more collaborative AI ecosystems, where various tools, datasets, and platforms can work together seamlessly. The AI platform market is expanding rapidly, and businesses are realizing that the best approach often involves leveraging multiple specialized solutions. This means AI platforms need to be built with interoperability in mind, supporting open standards and easy integrations. Such ecosystems allow businesses to combine the strengths of various AI technologies, share insights more effectively, and ultimately maximize the return on their AI investments. Look for platforms that play well with others and foster a spirit of open innovation.

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Frequently asked questions

My business is just starting to explore AI. How can I tell if investing in an AI platform is the right next step for us? 

That's a great question to ask! Think about the challenges or opportunities you're facing. Are there repetitive tasks that tie up your team's valuable time? Are you looking for deeper insights from your customer data to personalize experiences? Or perhaps you want to make more accurate forecasts? If you're nodding along, an AI platform could be a smart move. These platforms provide the tools to build and deploy AI solutions more easily, helping you tackle those very issues without needing a massive, specialized AI team from day one.

With so many AI platforms available, what's a practical first step to figure out which one might fit my company's specific needs? 

It can definitely feel like a lot of options! A really helpful first step is to get super clear on what you want AI to achieve for your business. Instead of getting lost in feature lists, define one or two key problems you want to solve or goals you want to reach. Once you have that clarity, you can start looking at platforms whose strengths and features directly support those specific objectives. Many platforms offer demos or free trials, which are perfect for getting a feel for their interface and seeing if they align with your team's workflow and your project's requirements.

You mentioned platforms like Cake manage the "entire stack." What tangible benefits does that offer my team compared to piecing things together ourselves? 

That's a key point! When a platform manages the entire stack—from the underlying computing power to the software components and even pre-built project pieces—it essentially takes a lot of the complex, foundational work off your team's plate. This means your people can spend less time wrestling with infrastructure, integrations, and compatibility issues, and more time focusing on developing and deploying the actual AI applications that will bring value to your business. It streamlines the whole process, helping you get your AI initiatives up and running more efficiently and with fewer headaches. Cake also ensures you get the best of the latest technologies by ensuring your stack stays up-to-date.

We're concerned about the costs. Are there ways for smaller businesses or those with tighter budgets to still leverage AI platforms effectively? 

Absolutely! It's a common concern, but many AI platforms are designed with flexibility in mind. You'll often find pay-as-you-go pricing models, which mean you only pay for the resources you actually use – great for when you're starting out or if your needs fluctuate. Some platforms also offer free tiers or introductory credits, allowing you to experiment and test capabilities without a big upfront investment. Starting with a smaller, well-defined project is often a smart way for businesses of any size to begin exploring AI's potential without overstretching the budget.

What's one common pitfall businesses encounter when implementing an AI platform, and how can we proactively avoid it? 

One challenge many businesses run into is not having a crystal-clear objective for their AI project from the outset, or underestimating the importance of data quality. To avoid this, really spend time defining the specific business problem you want AI to solve and what success will look like. Alongside that, take a good look at your data. Is it clean, relevant, and sufficient for training an AI model? Ensuring you have clear goals and a solid data foundation before you dive deep into platform selection and implementation will save you a lot of time and effort down the road and set you up for much better results.