8 Vertex AI Alternatives for Your AI Projects

Google's Vertex AI promises a single, managed platform for your entire ML lifecycle. But what happens when that convenience leads to frustration? Unpredictable costs, technical limitations, and being locked into one vendor's ecosystem can seriously slow you down. If you're ready to take back control of your stack, you're in the right place. We'll break down the best Vertex AI alternatives to help you find a platform that truly fits your project's needs, not the other way around.

For teams deeply embedded in Google Cloud, Vertex AI provides fast onboarding, seamless access to Google’s foundation models (like Gemini), and tight integration with tools such as BigQuery, Dataflow, and Looker.

Key takeaways

• Vertex AI works well for Google Cloud users but limits portability, customization, and open-source flexibility—constraints that become critical as AI workloads mature.
• Most teams moving beyond Vertex AI follow one of two paths: building a custom in-house stack for total control, or adopting an AI development platform for speed and reduced operational overhead.
• Custom builds offer maximum flexibility but demand significant engineering resources, months of integration work, and ongoing maintenance.
• AI development platforms like Databricks, Dataiku, ClearML, and especially Cake provide modular, secure, and scalable environments—without the vendor lock-in of hyperscaler-managed services.

While Vertex AI is a strong starting point for Google Cloud–native teams, over time, its tight coupling to Google’s ecosystem can feel restrictive. Many teams outgrow it as their AI strategy evolves. Common pain points include:

• Cloud lock-in: limits the ability to adopt multi-cloud or hybrid strategies
• Lack of customization: less control over pipelines, fine-tuning infrastructure, and orchestration
• Limited model support: prioritization of Google’s foundation models can hinder OSS or third-party experimentation
• Governance and compliance concerns: especially in regulated industries
• Cross-organizational friction: especially when teams collaborate across regions or business units.

These are not unique to Vertex. Other managed AI services, such as AWS SageMaker or Azure ML pose similar challenges. They’re great for users tied to those clouds, but still limit portability and flexibility. That’s why most teams looking beyond Vertex AI tend to pursue one of two paths.

IN DEPTH: Why Cake beats AWS SageMaker

The state of AI adoption and its challenges

As more companies adopt AI, they quickly move from initial excitement to the practical challenges of implementation. The central question becomes whether to build a custom AI stack from the ground up or adopt a managed platform. Building in-house offers complete control and customization, which is appealing for very specific, large-scale needs. However, this path requires a significant investment in specialized engineering talent, months (or even years) of development time, and a dedicated team for ongoing maintenance and updates. For most organizations, the resources required for a custom build are simply out of reach, making it a high-risk, high-reward strategy that can easily divert focus from core business goals.

On the other hand, managed AI platforms promise to simplify this process, offering pre-built infrastructure and tools to get models into production faster. This is where services like Vertex AI come in, providing an accessible entry point. But as we've seen, these platforms often come with their own set of trade-offs, including vendor lock-in and a lack of flexibility. This leaves many teams feeling stuck between two imperfect options: the overwhelming complexity of a custom build or the restrictive nature of a single-vendor managed service. The ideal solution often lies somewhere in the middle—a platform that combines the speed of a managed service with the flexibility of an open-source approach.

Why companies are moving to managed AI services

The decision to move to a managed AI service is almost always driven by a need for speed and efficiency. As one analysis notes, "Many teams moving beyond Vertex AI follow one of two paths: building a custom in-house stack for total control, or adopting an AI development platform for speed and reduced operational overhead." Building a custom stack is a massive undertaking. It involves integrating dozens of open-source tools, managing complex compute infrastructure, and ensuring everything works together seamlessly. This requires a dedicated team of MLOps engineers and can take months before a data scientist can even begin to train a model. The operational burden is continuous, with constant updates, security patches, and performance tuning.

This is why AI development platforms are so compelling. They handle the underlying infrastructure, allowing data science and ML teams to focus on what they do best: building and deploying models that create business value. By abstracting away the complexity of the stack, these platforms dramatically reduce the time it takes to get from an idea to a production-ready application. For companies looking to accelerate their AI initiatives without hiring an army of infrastructure engineers, a managed platform provides a clear path forward, minimizing operational headaches and maximizing impact.

Why look for a Vertex AI alternative?

While Vertex AI is a solid choice for teams already committed to the Google Cloud ecosystem, its limitations often become more apparent as AI projects mature and scale. The very integrations that make it convenient at the start can lead to significant friction down the road. Teams find themselves grappling with issues that hinder their ability to innovate, experiment, and operate efficiently. The platform's design prioritizes ease of use within Google's walled garden, but this can come at the cost of flexibility, cost control, and performance when your needs become more sophisticated or span across multiple cloud environments.

The search for an alternative is typically triggered by a few common pain points. These aren't just minor inconveniences; they are fundamental challenges that can impact budgets, timelines, and the overall success of an AI strategy. From opaque pricing models that make budgeting a nightmare to technical bugs that halt development, teams eventually reach a tipping point where the platform's constraints outweigh its benefits. Let's break down some of the specific reasons why so many organizations start looking for a more flexible and predictable solution for their machine learning workloads.

Unpredictable pricing

One of the most common frustrations with Vertex AI is its complex and often unpredictable pricing structure. For businesses trying to manage budgets and forecast expenses, this can be a major obstacle. As one report points out, "Many users consider switching from Vertex AI because of confusing prices. It's hard to guess how much Vertex AI will cost each month because its pricing is complicated." This isn't just about the total cost, but the lack of clarity. When every API call, training hour, and prediction endpoint has a different variable cost, monthly bills can fluctuate wildly, making it nearly impossible to plan for the future or justify ROI to stakeholders.

This financial uncertainty creates a chilling effect on experimentation and scaling. Teams may become hesitant to run new experiments or scale up successful models for fear of incurring an unexpectedly large bill. A predictable cost structure is essential for any serious AI initiative, as it allows teams to operate with confidence and focus on innovation rather than constantly monitoring their cloud spending. The search for an alternative is often a search for a more transparent and foreseeable financial model that supports, rather than penalizes, growth and exploration.

Slow setup time and need for specialized engineers

Although Vertex AI is marketed as a managed platform, getting started isn't always a simple plug-and-play experience. The initial setup and integration can be surprisingly complex, often requiring deep expertise in Google Cloud's infrastructure. This creates a significant bottleneck, particularly for smaller teams or organizations that don't have dedicated data engineers on staff. As one review highlights, "Smaller teams often need special data engineers to set it up, which takes time and makes things slower." This initial hurdle can delay projects for weeks or even months, eroding the very speed and agility that a managed platform is supposed to provide.

This reliance on specialized talent means the platform isn't as self-serve as many teams hope. Instead of empowering data scientists to manage their own workflows, it can create a dependency on a small group of infrastructure experts. This not only slows down the development lifecycle but also increases operational costs. An ideal AI platform should enable data scientists to be more autonomous, allowing them to quickly spin up environments, run experiments, and deploy models without waiting in a queue for an engineer to become available.

Technical limitations and bugs

When you rely entirely on a single vendor's platform, you're also at the mercy of its technical shortcomings and bugs. These issues can be frustrating and, in some cases, complete blockers for critical projects. For example, one developer on Reddit shared a significant problem where "a bug in Google's system... stops the author from running many models on a shared system with 2 GPUs." This isn't a minor glitch; it's a fundamental limitation that prevents a team from using their hardware resources efficiently and scaling their operations as planned. When you encounter a bug like this, you have no choice but to wait for the vendor to prioritize and issue a fix.

This lack of control is a major risk for any mission-critical AI application. Your project timelines and success become dependent on another company's development cycle and support responsiveness. This is a key reason why many teams prefer platforms built on open-source components. An open-source foundation provides greater transparency and control, allowing teams to troubleshoot issues themselves or tap into a broader community for support. It removes the "black box" nature of proprietary services and gives you more power to ensure your systems are reliable and performant.

Upcoming SDK deprecation

Platform stability is crucial for long-term success, but users of proprietary services often face forced migrations and deprecations that disrupt their workflows. A prime example is Google's announcement that the "'Generative AI module' in the Vertex AI SDK will no longer work after June 24, 2026." This isn't a simple update; it's a significant change that will require engineering teams to dedicate time and resources to rewriting code and adapting to a new SDK. This kind of platform churn forces developers to spend valuable time on maintenance and migration tasks instead of building new features or improving models.

These deprecations highlight the inherent risk of building on a proprietary, rapidly evolving toolset. What works today might be obsolete tomorrow, and you have no say in the matter. This forces a reactive development cycle and introduces uncertainty into your technical roadmap. In contrast, platforms built on stable, widely adopted open-source standards offer a more predictable and future-proof foundation. They allow you to evolve your stack on your own terms, rather than being forced to adapt to a vendor's shifting priorities and product strategy.

Two main paths to consider as Vertex AI alternatives

Once teams recognize the limitations of Vertex AI, the question becomes: What’s the smartest way forward? The answer usually depends on how much control you want, how fast you need to move, and how many engineering resources you can dedicate to building and maintaining your AI infrastructure.

For most organizations, the choice narrows to two clear strategies. You can either build a fully custom AI stack in-house, taking on the responsibility of integrating, securing, and scaling every component yourself, or you can adopt an AI development platform that delivers flexibility and portability without the operational overhead.

The first path maximizes control but slows time-to-market. The second prioritizes speed and reduces maintenance, while still allowing for customization, especially if you choose a cloud-neutral, open-source-first platform like Cake.

Option 1: Build your own in-house AI platform

Building your own AI foundation from the ground up means taking complete ownership of your architecture—from compute and storage to orchestration, monitoring, and security. You select each component, often from a mix of open-source frameworks and proprietary tools, and design the entire stack to meet your exact needs.

In practice, this can involve:

• Setting up infrastructure (Kubernetes clusters, GPU/TPU compute, object storage)
• Integrating MLOps tools for model training, versioning, and deployment
• Implementing observability for metrics, logs, and traces
• Building governance, RBAC, and compliance workflows from scratch
• Coordination across data science, infrastructure, and compliance teams

The upside of building in-house:

Total flexibility—choose orchestration tools, infrastructure, and observability systems

Tailored for compliance, customization, and scale

Potential challenges to consider:

Resource-intensive—can take months (or over a year) to build.

High maintenance burden—requires dedicated platform engineers.

Risk of disruption if key team members leave.

High opportunity cost as engineering time shifts away from core product work.

The upside of building in-house

The biggest draw of building your own AI platform is, without a doubt, total control. This path gives you complete freedom to handpick every component of your stack. You can select the best-in-class orchestration tools, the most efficient compute infrastructure, and the most insightful observability systems for your specific use case, without being tied to a single vendor’s ecosystem. For teams with highly specialized needs, this level of customization is incredibly appealing. It also means you can design the entire system around your unique compliance and security requirements from day one, which is a major advantage for businesses in heavily regulated industries like finance or healthcare.

Potential challenges to consider

While the idea of a perfectly tailored system is attractive, the reality of building it can be daunting. This approach is extremely resource-intensive, often requiring a dedicated team of platform engineers and a timeline that can stretch from several months to over a year. The work doesn’t stop once it’s built, either; the maintenance burden is high, involving constant updates, security patches, and troubleshooting. This creates a significant operational cost and introduces risk, especially if a key team member who holds all the institutional knowledge decides to leave, potentially disrupting your entire AI pipeline.

Beyond the technical hurdles, there’s a major strategic trade-off to consider: the opportunity cost. Every hour your top engineers spend building and maintaining internal infrastructure is an hour they aren’t spending on your core product or developing the AI models that actually create business value. Diverting your best talent to platform engineering can slow down innovation and delay your time-to-market. It forces you to ask a critical question: is your company in the business of building AI platforms, or in the business of using AI to solve customer problems?

Option 2: Use a dedicated AI development platform

AI development platforms package much of the infrastructure, orchestration, and governance you need into a single, integrated environment—while still allowing flexibility in model choice, deployment architecture, and integration with existing systems. They’re designed to shorten the gap between prototype and production by removing the heavy lifting of stitching together disparate tools.

With a good AI development platform, you typically get:

• Ready-to-use orchestration for training, fine-tuning, and RAG pipelines
• Support for both proprietary and open-source models
• Built-in MLOps workflows for model versioning, evaluation, and deployment
• Preconfigured security and compliance features (RBAC, audit logs, policy enforcement)
• Deployment flexibility—on any cloud or on-premises

This approach is best for teams that want to:

• Move fast without sacrificing compliance
• Reduce the operational burden on engineering teams
• Retain portability to avoid vendor lock-in
• Focus more on model and application logic rather than infrastructure plumbing

The key trade-off: While you gain speed and lower operational cost, you may have to work within the patterns and APIs of the chosen platform, making it essential to choose one that’s truly modular and cloud-neutral.

A look at major cloud provider alternatives

When teams decide to move away from Vertex AI, they often look to the other major cloud providers first. Amazon Web Services (AWS) and Microsoft Azure are the obvious next steps, especially for organizations that already use them for other parts of their business. While these platforms are powerful, it’s important to recognize that they often present the same core challenges as Vertex AI—namely, vendor lock-in and a level of complexity that can slow you down. They are strong contenders, but they represent a sideways move more than a fundamental shift in strategy. Let's break down what each one offers.

AWS SageMaker and Amazon Bedrock

Amazon’s ecosystem for AI is primarily split between two services. AWS SageMaker is the comprehensive machine learning service designed to cover the entire ML lifecycle, from data preparation and model building to training and deployment. For generative AI, Amazon Bedrock provides access to a wide range of foundation models from leading AI companies. The biggest draw for existing AWS customers is the seamless integration with other services you’re likely already using, creating a unified, if closed, environment for your AI projects.

Key features

SageMaker is the workhorse of the AWS ML ecosystem, providing tools for every stage of model development. It’s particularly useful for teams that need a single platform to manage both traditional ML and generative AI workflows. Bedrock complements this by offering a simple way to experiment with and deploy over 100 different foundation models from companies like Anthropic, Cohere, and AI21 Labs. This combination allows teams to build, train, and deploy models within a familiar AWS environment, leveraging existing data and infrastructure with minimal friction.

Pricing model

Both SageMaker and Bedrock operate on a pay-as-you-go model, which is attractive for teams wanting to avoid large upfront costs. With SageMaker, you pay for the compute, storage, and data processing resources you use, often billed down to the second. Bedrock’s pricing is typically based on the number of input and output “tokens” processed by the model you choose. While this offers flexibility, it also means costs can scale quickly and unpredictably as your usage grows, requiring careful monitoring to stay within budget.

Potential limitations

The primary drawback of the AWS ecosystem is its complexity. SageMaker has a notoriously steep learning curve due to its vast number of features, which can be overwhelming for teams new to the platform. Furthermore, the pay-as-you-go model can lead to hidden costs, as you’ll also be paying for related services like S3 for data storage and CloudWatch for monitoring. This can make it difficult to forecast your total spend accurately and can lead to budget overruns if not managed closely.

Azure Machine Learning

For organizations heavily invested in the Microsoft ecosystem, Azure Machine Learning is a natural choice. It’s designed to integrate smoothly with other Microsoft products like Power BI, Dynamics 365, and even Excel, making it easy to incorporate AI into existing business processes. Azure has also established itself as a leader in generative AI through its strategic partnership with OpenAI, providing straightforward access to powerful models like GPT-4 directly within the Azure platform. This makes it a compelling option for businesses focused on leveraging cutting-edge language models.

Key features

Azure Machine Learning provides a collaborative environment with tools for developers and data scientists of all skill levels, from no-code automated machine learning (AutoML) to a code-first experience with Jupyter notebooks. Its biggest differentiator is the native integration with OpenAI, which allows businesses to build applications on top of some of the most advanced language models available. The platform also offers robust MLOps capabilities to help teams automate the ML lifecycle and manage models in production, all within a secure and governed framework.

Pricing model

Similar to its competitors, Azure Machine Learning uses a pay-as-you-go pricing structure with no upfront fees. Costs are primarily driven by the consumption of virtual machine instances used for model training and deployment. You pay for the compute resources you use, which gives you control over your spending. However, like other cloud platforms, the final bill will also include costs for associated services such as Azure Blob Storage for data and Azure Monitor for logging, which need to be factored into your budget.

Potential limitations

While Azure offers a powerful and integrated experience, it works best when your data and workflows are already within the Azure cloud. Moving data from other cloud providers can be cumbersome and may introduce additional costs and complexity. The platform also abstracts away some of the underlying infrastructure, which can be a limitation for teams that require granular control over their compute environments. This can make it challenging to optimize performance or troubleshoot issues at a deeper level, reinforcing the theme of trading some control for convenience.

Exploring other dedicated and open-source platforms

Moving from Vertex AI to another major cloud provider can feel like trading one walled garden for another. You might solve a few immediate problems, but you’re still tied to a single vendor’s ecosystem, which limits your flexibility in the long run. This is why many forward-thinking teams are turning to dedicated AI development platforms. These platforms package the necessary infrastructure, orchestration, and governance into a single, integrated environment, but they do so with an open, cloud-agnostic philosophy. They are designed to shorten the gap between prototype and production by removing the heavy lifting of stitching together disparate tools.

This approach gives you the best of both worlds: the speed and convenience of a managed service without the restrictive lock-in. Instead of being limited to one cloud’s models and tools, you can choose the best components for your specific needs, whether they’re open-source or proprietary. This is the core idea behind Cake. We provide a production-ready solution that manages the entire AI stack—from compute infrastructure to pre-built project components—on your cloud of choice. By focusing on open-source tools and a modular architecture, we help you accelerate your AI initiatives while ensuring you always stay in control of your technology stack.

A closer look at Databricks

Best for: Data-intensive teams building unified lakehouse–based ML workflows.

Databricks is a unified analytics and AI platform built around the “lakehouse” concept, combining the scalability of data lakes with the performance of data warehouses. It’s deeply tied to Apache Spark and offers robust capabilities for data processing, feature engineering, and ML model development, with tooling like MLflow and Delta Lake. Databricks is powerful for enterprises with large-scale data pipelines, but it can be overkill for LLM-focused teams and often introduces vendor tie-in.

What we like about Databricks:

Powerful integration of data engineering and ML with Spark, MLflow, and Delta Lake

Strong OSS support and active community ecosystem

Limitations:

Heavy lift for LLM-focused workloads; Spark expertise required

Vendor-locked and AWS-leaning by default

BLOG: Best Databricks alternatives of 2025

A closer look at Databricks

Best for: Data-intensive teams building unified lakehouse–based ML workflows.

Databricks is a unified analytics and AI platform built around the “lakehouse” concept, combining the scalability of data lakes with the performance of data warehouses. It’s deeply tied to Apache Spark and offers robust capabilities for data processing, feature engineering, and ML model development, with tooling like MLflow and Delta Lake. Databricks is powerful for enterprises with large-scale data pipelines, but it can be overkill for LLM-focused teams and often introduces vendor tie-in.

What we like about Databricks:

Powerful integration of data engineering and ML with Spark, MLflow, and Delta Lake

Strong OSS support and active community ecosystem

Limitations:

Heavy lift for LLM-focused workloads; Spark expertise required

Vendor-locked and AWS-leaning by default

BLOG: Best Databricks alternatives of 2025

A closer look at Dataiku

Best for: Cross-functional teams that need a low-code, visual interface for both analytics and AI.

Dataiku is an end-to-end platform that emphasizes collaboration between data scientists, analysts, and business users. Its visual, flow-based interface allows teams to build data pipelines, prepare data, and create models with minimal code. While it supports custom Python and R scripting, its core strength lies in its accessibility, making it a good fit for organizations looking to democratize data science. It’s a powerful tool for analytics-heavy use cases but can feel less flexible for teams focused on deep, code-first customization of LLM pipelines.

What we like about Dataiku:

Strong visual interface that simplifies collaboration

Good for both traditional ML and business analytics

Limitations:

Can be less intuitive for complex, code-heavy AI/LLM workflows

Pricing can be complex and less transparent

A closer look at Dataiku

Best for: Cross-functional teams wanting visual and low-code AI workflows.

Dataiku is an enterprise AI and analytics platform that enables data scientists, analysts, and business users to build AI workflows through a visual interface. It offers prebuilt integrations with major cloud and on-prem data sources, visual pipelines for data prep and MLOps, and governance features for enterprise deployments. While excellent for democratizing AI across teams, it’s less flexible for engineering-first organizations that want to own and customize every layer of their stack.

What we like about Dataiku:

Collaborative interface; great for democratizing AI across roles

Built-in governance and pipeline visualizations

Where Dataiku falls short:

Less flexible for engineering-first OSS customization

Cost can scale quickly; platform-dependent

What we like about Dataiku

Dataiku's biggest strength is its collaborative, visual interface. It’s designed to bring different teams together, allowing data scientists, analysts, and even business users to contribute to AI projects without needing to be deep in the code. This approach is fantastic for democratizing AI across an organization, as it makes complex processes more accessible. The platform comes with built-in governance features and clear pipeline visualizations, which helps everyone understand how models are built and deployed. For companies focused on enabling cross-functional teams and standardizing workflows through a low-code environment, Dataiku offers a very compelling and structured path forward.

Where Dataiku falls short

The trade-off for Dataiku's user-friendly visual approach is a loss of flexibility, especially for engineering-led teams. If your organization prioritizes deep customization and wants to build with specific open-source components, Dataiku’s structured environment can feel restrictive. It’s not designed for teams that want to get their hands dirty with the underlying infrastructure. Another point to consider is the cost, which can escalate quickly as your usage grows, and its platform-dependent nature means you're still committing to a specific vendor's ecosystem. This can be a significant drawback for teams aiming for maximum portability and control over their stack.

A closer look at ClearML

Best for: Engineering-first teams committed to open-source orchestration.

ClearML is an open-source MLOps platform designed for engineers who prefer to tailor their orchestration, training, and deployment workflows. It offers queue-based job management, remote execution, and agent-based scheduling, and can run on any cloud or on-prem infrastructure. The trade-off is that scaling, securing, and maintaining the stack is more DIY compared to fully managed platforms.

What we like about ClearML:

Fully infrastructure agnostic; ideal for on-prem or multi-cloud
Strong experiment tracking and pipeline capabilities

Where ClearML falls short:

DIY scaling and security; lacks enterprise-grade features out of the box

What we like about ClearML

ClearML is a fantastic choice for teams that want deep control over their MLOps environment without being tied to a specific cloud provider. It's designed to be fully infrastructure agnostic, which is a huge plus. This means you can deploy your workflows wherever it makes the most sense for your business—whether that's on-premises, in a single cloud, or across multiple clouds. This flexibility is essential for avoiding vendor lock-in. The platform also shines with its robust capabilities for experiment tracking and pipeline management. For any team serious about optimizing their machine learning models, having a clear, organized way to track every run, parameter, and result is non-negotiable, and ClearML handles this really well.

Where ClearML falls short

However, that high degree of customization comes with a trade-off. With ClearML, the responsibility for scaling and securing your environment falls squarely on your team's shoulders. This "do-it-yourself" approach can become a significant operational burden, pulling valuable engineering time away from building models and toward managing infrastructure. You're on the hook for everything from ensuring high availability to implementing robust security protocols. Additionally, it may not come with all the comprehensive, enterprise-grade features that larger organizations need right out of the box. This is where a more managed solution, like Cake, can make a huge difference by providing a production-ready, secure platform from day one.

A closer look at Cake

Best overall alternative to Vertex AI

Cake is a cloud-neutral AI development platform optimized for enterprise-grade performance, flexibility, and speed. It runs across AWS, Azure, GCP, or on-prem, and orchestrates best-in-class OSS tools under aligned security and compliance.

With Cake, you can:

Seamlessly deploy proprietary and open-source LLMs

Orchestrate RAG pipelines, fine-tuning, and high-performance model serving

Leverage built-in enterprise-grade security (RBAC, audit logs, policy enforcement)

Save significant resources—teams report a 6–12 month acceleration to production and 30–50% infrastructure cost savings versus in-house builds

How Ping found success with Cake

Ping, a commercial property insurance platform, used Cake to rearchitect its ML infrastructure and saw transformative results:

• Speed gains: Data collection and annotation now run multiple times faster than before—enabling rapid iteration and better data quality.
  
  
• Operational efficiency: Consolidated tools into a unified GitOps-managed Kubernetes environment, replacing a patchwork of SageMaker and vendor solutions.
  
  
• High-impact savings: Delivered the equivalent output of 2–3 full-time engineers while costing only half an FTE.
  
  
• Security trust: Added enterprise-grade security controls to open-source tooling, protecting sensitive PII while enabling safe deployments.
  
  
• Continuous modernization: Integrated cutting-edge OSS tools to keep workflows current without disruptive migrations.

SUCCESS STORY: How Ping Established ML-Based Leadership

How Cake provides a managed open-source solution

So, how does Cake deliver on this promise of speed and flexibility? Instead of forcing you into a proprietary ecosystem, Cake acts as an orchestration layer that manages best-in-class open-source tools for you. It handles the entire stack—from the underlying compute infrastructure to security and compliance—so your team can focus on building models, not managing infrastructure. This approach allows you to seamlessly deploy both proprietary and open-source LLMs, build complex RAG and fine-tuning pipelines, and benefit from enterprise-grade security like RBAC and audit logs right out of the box. The result is a production-ready environment that can cut your time-to-market by 6–12 months and reduce infrastructure costs by 30–50% compared to building it all yourself.

IBM watsonx.ai for regulated industries

For large companies, especially those in highly regulated fields like finance or healthcare, governance and compliance are non-negotiable. This is where a platform like IBM watsonx.ai comes in. It’s specifically designed for enterprises that need strict rules and checks for every part of their AI lifecycle. The platform offers advanced tools for validating models, detecting bias, and providing clear explanations for how a model arrives at its decisions. If your organization requires deep transparency and audibility to meet regulatory standards, watsonx.ai provides a framework built around those needs, making it a strong contender for teams where compliance is the top priority.

Open-source frameworks like Kubeflow, Seldon Core, and MLflow

If you’re leaning toward an in-house build but want a head start, several open-source frameworks can provide the building blocks for your AI platform. Tools like Kubeflow help run ML workflows on Kubernetes, Seldon Core simplifies model deployment and scaling, and MLflow offers a suite for managing the entire ML lifecycle. The main appeal here is complete ownership. You aren't tied to a vendor’s roadmap, you can customize every component, and you can run your stack anywhere. This flexibility is powerful, but it comes with a significant trade-off: the DIY approach demands a dedicated team of platform engineers to integrate, secure, and maintain these disparate tools, creating a high operational burden.

Other specialized platforms to consider

Beyond the major cloud providers and DIY frameworks, a number of other specialized platforms cater to specific needs. For instance, Dataiku is designed for cross-functional collaboration, offering a visual, low-code interface that allows data analysts and business users to participate in building AI workflows alongside data scientists. On the other end of the spectrum, ClearML is an open-source MLOps platform built for engineering-first teams who want granular control to customize their orchestration and deployment pipelines. Each platform offers a different balance of ease-of-use, flexibility, and control, making it important to choose one that aligns with your team’s skills and project goals.

How do these Vertex AI alternatives compare?

How to choose the right Vertex AI alternative for you

Vertex AI can be a strong starting point for teams committed to Google Cloud, but as AI workloads grow, so do the demands for portability, flexibility, security, and control. That’s when teams start looking for infrastructure that works for their strategy—not the other way around.

You have two main options:

• Full control with an in-house build: Ideal if you have a large, specialized engineering team ready to invest months in building and maintaining every component of your stack.

• A portable, enterprise-ready platform: The faster, more cost-effective path for most teams, delivering the speed of a managed service with the flexibility of open source.

This is where Cake stands out. It gives you:

• The ability to run in any cloud or on-prem, avoiding vendor lock-in.

• A modular, open-source-first architecture that keeps you at the cutting edge without disruptive migrations.

• Enterprise-grade security and compliance from day one, so you can confidently deploy AI in regulated industries.

• A proven acceleration to production—teams often save 6–12 months of engineering time and cut infrastructure costs by 30–50%.

With Cake, you’re not just swapping one platform for another—you’re investing in an AI foundation that adapts with your business, enabling you to experiment faster, scale without friction, and stay in control of your data and models.

Related articles

• Why Cake Beats Amazon SageMaker
• How to Build a Scalable GenAI Infrastructure in 48 Hours
• Beyond SageMaker: Alternative AI Platforms
• GPT-5 Is Here. So, Why Are You Waiting to Build?

Frequently asked questions

Can I run Vertex AI on AWS or Azure?

No—Vertex AI is exclusive to Google Cloud. If portability or hybrid architectures are essential, platforms like Cake, ClearML, or Databricks are better options.

Is switching from Vertex AI to AWS SageMaker a good idea?

Only if you’re migrating to AWS entirely. You’ll face the same trade-offs (lock-in, limited OSS flexibility), and you still won’t gain cross-cloud agility.

How quickly can I go from prototype to production with Cake?

Many teams report being production-ready 6–12 months faster with Cake than they’d achieve building a stack in-house.

Which platform supports open-source LLM workflows best?

Cake leads with full OSS support and model flexibility, followed by ClearML. Databricks supports OSS via MLflow, but its focus is more Spark/enterprise data. Dataiku is more visual and less OSS-tuned.

What about security and compliance for regulated industries?

Cake and Dataiku both offer enterprise-grade security features out of the box (RBAC, audit logging, policy enforcement). Databricks and ClearML require more engineering to secure effectively.

Before you choose a path, take a hard look at your team's skills and bandwidth. Do you have dedicated platform engineers who can spend months building and maintaining a custom AI stack? This route offers total control but demands deep expertise in Kubernetes, MLOps tooling, and security. It’s a significant investment of time and resources that could otherwise be spent on developing your core product. The alternative is to adopt an AI development platform that handles the infrastructure heavy lifting. This approach frees up your team to focus on what they do best: building models and applications that drive business value, drastically reducing the operational burden and getting you to production faster.

Where your data lives is a critical factor. If your entire data ecosystem is already on Google Cloud, Vertex AI can feel like a natural fit. However, this convenience comes with a catch: cloud lock-in. As your AI strategy matures, you might need the flexibility to use multiple clouds for cost optimization, geographic reach, or to collaborate with partners on different platforms. Tying your AI infrastructure to a single vendor can limit your ability to adapt. A cloud-neutral platform gives you the freedom to deploy your AI workloads anywhere—on-prem or across any cloud—ensuring you stay in control of your data and your strategy, no matter how your business needs evolve.

Not all AI platforms are built for the same purpose. Some, like Databricks, excel at large-scale data processing and traditional machine learning, while others are better suited for the unique demands of generative AI. Think about your primary goal. Are you building complex RAG pipelines and fine-tuning open-source LLMs, or are you focused on analytics? The right platform should provide an integrated environment with the specific infrastructure, orchestration, and governance you need for your use case. A flexible AI development platform allows you to choose the best models and deployment architecture for the job, rather than forcing you into a one-size-fits-all approach that doesn't quite fit.

When evaluating alternatives, look beyond the initial price tag and consider the total cost of ownership. A custom in-house build requires a massive upfront investment in engineering time and carries high ongoing maintenance costs. While managed services might seem cost-effective at first, their pricing can be unpredictable, and vendor lock-in can become incredibly expensive down the line. The smartest investment is in an AI foundation that adapts with your business. With a platform like Cake, you get a predictable cost model while retaining the flexibility to experiment quickly, scale efficiently, and maintain full control over your data and models for the long term.

Security and compliance should be at the forefront of your decision, not an afterthought—especially if you operate in a regulated industry. Enterprise-grade security features like role-based access control (RBAC), audit logging, and automated policy enforcement are non-negotiable for protecting sensitive data. Some platforms require significant custom engineering to meet these standards, adding complexity and risk to your projects. In contrast, platforms like Cake are designed with security built-in from the ground up, providing these critical features out of the box. This allows you to deploy AI applications with confidence, knowing your stack meets stringent compliance requirements from day one.