Which vendor has the fastest AI deployment times?

Cake Team

Published: 08/2025

23 minute read

Getting your AI model into production shouldn't take months. But endless infrastructure setup, compliance hurdles, and tool integration often grind projects to a halt. This delay is why so many leaders are asking, "which vendor offers the fastest deployment timelines for enterprise ai projects?" They're looking for a solution that delivers value now. With Cake, teams are achieving up to 3.9x faster deployments. Our pre-integrated, cloud-agnostic platform removes these friction points, turning a months-long process into just a few weeks. You get to see the impact of your models sooner.

Industry benchmarks confirm how significant that shift is. Moving from months to weeks translates to nearly a fourfold acceleration, or about 3.9x faster model deployment. Too often, enterprises spend months trying to move a model from prototype to production. The challenge is not the models themselves but the infrastructure that surrounds them. Standing up orchestration frameworks, configuring autoscaling, setting up monitoring, and ensuring compliance all take time. By the time a model is ready for release, opportunities may have shifted.

Cake collapses these timelines. By providing orchestration, monitoring, compliance, and open source integrations in a single platform, it transforms deployment from a slow, piecemeal process into a rapid cycle. What once took months can now be done in weeks.

Key takeaways

Traditional AI deployments often take two to three months due to infrastructure, compliance, and integration overhead.
Enterprises using Cake, including Ping and Glean.ai, moved from months to weeks, achieving up to 3.9x faster deployment.
Cake delivers speed by combining pre-wired infrastructure, built-in compliance, and open source integrations in a unified, cloud-agnostic platform.

What is AI deployment?

Think of AI deployment as the moment your AI model gets its first real job. After all the training, testing, and refining in a lab-like environment, deployment is the process of putting that model into a live production system where it can start doing useful work. It’s the transition from a promising prototype to a functional tool that real people or other systems can interact with. This is the critical step where your AI investment begins to deliver actual business value, whether it’s powering a recommendation engine on your e-commerce site, a chatbot for customer service, or a system that predicts maintenance needs for factory equipment.

Without deployment, even the most sophisticated AI model is just a piece of code sitting on a developer's machine. The deployment process involves integrating the model into your existing IT infrastructure, ensuring it can handle real-world data, and making it accessible to the end-users or applications that need it. It’s about making the AI operational, scalable, and reliable. This stage is less about the data science behind the model and more about the engineering required to make it work consistently and efficiently in a live environment, which brings its own unique set of hurdles.

The AI deployment process and its challenges

While it sounds straightforward, the path to deployment is often where AI initiatives get stuck. Many companies find it takes an incredibly long time—sometimes 18 to 24 months—to get a single project into production. By the time the model is finally live, the original business problem may have changed, or the team that built it might have moved on. The biggest delays often don't come from the AI models themselves but from the surrounding complexities. Teams grapple with messy or disconnected data, configuring the right compute infrastructure, and navigating security and compliance approvals. These operational hurdles can turn a promising project into a frustratingly slow crawl toward the finish line.

What is an enterprise AI platform?

An enterprise AI platform is a software system designed to solve the deployment puzzle. It acts as a central hub that helps companies build, connect, manage, and scale their AI tools across the entire business. Instead of having teams build everything from scratch for each new project, a platform provides the foundational "plumbing" needed to get AI models up and running quickly and safely. This includes managing the underlying compute infrastructure, integrating with different data sources, and providing tools for monitoring model performance and ensuring everything remains compliant with company policies.

These platforms are built to help businesses use AI for practical applications like improving customer service, supporting employees, and streamlining daily operations. By providing a standardized, production-ready environment, they remove many of the roadblocks that slow down deployment. For instance, a platform like Cake manages the entire stack—from infrastructure to open-source integrations—so your data scientists and developers can focus on building great models, not on configuring servers. This approach not only accelerates deployment but also makes it easier to manage and grow your AI capabilities as your business needs evolve.

Here's what slows down traditional AI deployments

Industry studies consistently show that deploying models into production is a lengthy process. Algorithmia’s State of Enterprise ML report found that half of organizations take between 8 and 90 days to push a single model into production. The New Stack reported that 40% of companies require more than a month, with only 14% managing it in under a week. Other analyses note that for many enterprises, deployments can stretch to three months or more, especially for large-scale LLM projects. Other industry reports similarly highlight that these timelines reflect the overhead of infrastructure setup, compliance approvals, and integration across multiple tools.

Against this backdrop, Cake’s impact is clear. Moving from months to weeks translates to nearly a fourfold acceleration, or about 3.9x faster model deployment.

Understanding typical AI deployment timelines

It’s easy to get excited about a new AI initiative, but that energy often fades when faced with the reality of deployment timelines. Many companies find that it can take an incredibly long time—sometimes 18 to 24 months—to get a single AI project from concept to production. By the time the model is finally ready, the original business problem may have changed, key team members might have moved on, or the market opportunity could have passed. This slow pace isn't just frustrating; it's a major barrier to innovation. When projects drag on for years, they lose momentum and stakeholder support, making it difficult to prove the value of your AI investments before everyone’s attention has shifted elsewhere.

The data cleaning trap

One of the biggest hurdles in AI is the obsession with perfect data. There's a common belief that you can't start building a model until your data is completely pristine, which leads teams down a rabbit hole of endless data cleaning. The truth is, data is never truly "clean enough." This pursuit of perfection becomes a trap, with teams spending months or even years trying to scrub and structure datasets, delaying the actual AI development indefinitely. A more effective approach is to start with the data you have and iterate. Getting a model into production quickly, even if it's not perfect, allows you to gather real-world feedback and make targeted improvements, which is far more valuable than waiting for a flawless dataset that may never materialize.

Endless debates and decision paralysis

Internal friction is another major cause of AI project delays. Before a single line of code is written, teams can spend months locked in debates over fundamental decisions. Arguments about where the AI will run—on-premise versus in the cloud—or how to handle complex data governance rules can bring progress to a screeching halt. This decision paralysis is often rooted in legitimate concerns about security, cost, and compliance, but it prevents teams from moving forward. Without a clear, standardized path for deployment, these debates can cycle endlessly, consuming valuable time and resources. A streamlined approach with pre-defined infrastructure and governance can cut through the noise and get everyone aligned and moving in the same direction.

Lack of skilled people

Even with a brilliant idea and a solid plan, many AI projects fail simply because companies can't find the right people to execute them. The demand for skilled AI and machine learning talent far outstrips the supply, making it incredibly difficult to build and retain a capable team. A study from Deloitte identified a lack of skilled experts as one of the primary reasons AI projects don't succeed. Building, deploying, and maintaining AI systems requires a specialized skill set that covers everything from data engineering to MLOps. When you can't fill those roles, projects stall, and the complex infrastructure required for production-grade AI becomes an insurmountable obstacle, leaving valuable models stuck in the development phase.

Common missteps that don't actually help

When an AI project starts to fall behind schedule, a common reaction is to simply add more engineers to the team. Unfortunately, this rarely solves the underlying problem. If the core issues are rooted in poor planning, siloed data access, or infrastructure bottlenecks, throwing more people at the project just adds complexity and communication overhead without addressing the actual roadblocks. As experts point out, adding more engineers doesn't fix fundamental problems with strategy or access. The solution isn't always more manpower; it's a better process and the right platform that empowers the team you already have to work efficiently and overcome systemic challenges.

How Cake helps your team achieve faster deployments

Glean.ai, an AI-powered accounts payable platform, is one of several enterprises that has proven a faster path with Cake. The Glean engineering team needed to build a custom LLM stack, a project that would traditionally require months of infrastructure work. With Cake, they had it up and running in weeks. “Cake helps you deploy the entire ML stack, everything you need in one package,” said Artiom Tarasiuk, CTO at Glean.ai. “It is an all-in-one SaaS platform for MLOps that requires far less effort.” By removing infrastructure hurdles, Cake gave Glean the speed and confidence to focus on building their product instead of maintaining infrastructure.

"[Cake] has enabled us to release a new model much faster than we would have otherwise."
—Bill Granfield, Machine Learning Engineering Lead, Ping

Ping, a data platform supporting the insurance industry, saw a similar acceleration. By consolidating its infrastructure on Cake, the team was able to move new models into production much faster than before. “With Cake, we are collecting this data several times faster than we were before, in a way that makes it easy to analyze what we have, understand the provenance of annotations, and measure the performance of different annotators,” said Bill Granfield, Machine Learning Engineering Lead at Ping. “It has enabled us to release a new model much faster than we would have otherwise.”

Together, these examples show how enterprises that once waited months to see results are now putting models into production in a fraction of the time.

How Cake accelerates deployment cycles

The acceleration that enterprises like Glean.ai and Ping achieved is not luck. It comes from how Cake is built to streamline every step of the deployment process. Traditional approaches require teams to spend weeks preparing infrastructure, securing compliance approvals, and stitching together disparate open source tools before a single model can run in production. Cake removes those friction points by delivering a platform where orchestration, scaling, monitoring, and governance are already in place. Instead of reinventing the wheel for each project, engineers can focus on the model itself and get it into production far faster.

Pre-wired infrastructure

Scaling, orchestration, and monitoring are ready from day one. Teams no longer spend weeks configuring Kubernetes or wiring together Argo workflows. Instead, they can move directly into experimentation and deployment.

Compliance and security built in

SOC 2 and HIPAA readiness are already included in the platform. This removes the cycle of audits and documentation that typically push back release dates. With compliance baked in, enterprises shorten approvals and lower risk.

Open source integrations

Cake supports the tools teams already know and use, including MLflow, Kubeflow, and Ray. There is no need to migrate workloads or re-platform. This flexibility keeps projects moving instead of waiting on new vendor integrations.

Unified observability

Monitoring, tracing, and performance tracking are part of the stack. Enterprises avoid the trial-and-error of bolting on observability after the fact and can instead validate deployments with confidence.

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The core benefits of enterprise AI

Beyond just speeding up deployment, a streamlined AI platform helps your business unlock tangible benefits that can reshape how you operate. When your teams can focus on building models instead of managing infrastructure, they can deliver value across the entire organization. From making smarter, data-backed decisions to creating standout customer experiences, enterprise AI is about driving real-world results. The faster you can get your models into production, the sooner you can start seeing these advantages take shape. Let's look at some of the core benefits that a well-executed AI strategy can bring to the table.

Better decision-making

In any business, the quality of your decisions depends on the quality of your information. AI excels at processing massive datasets to find patterns and insights that would be impossible for a human to spot. This capability gives business leaders fast, accurate information, helping them make smarter choices quickly. Instead of relying on gut feelings or outdated reports, you can use predictive models to forecast sales, optimize inventory, or identify market trends before they happen. This data-driven approach reduces guesswork and allows you to act with confidence, knowing your strategy is backed by solid evidence. The key is turning those insights into action without delay, which is why rapid deployment is so critical.

Improved customer experience

Your customers expect fast, personalized, and helpful interactions, and AI is a powerful tool for meeting those expectations. Think about how AI chatbots and automated emails can answer customer questions 24/7, providing instant support without making someone wait for a human agent. Beyond support, AI can analyze customer behavior to offer personalized product recommendations, tailor marketing messages, and create a more engaging journey. This level of personalization makes customers feel understood and valued, which builds loyalty and sets you apart from the competition. It transforms the customer experience from a one-size-fits-all approach to a one-on-one conversation, all at scale.

Better risk management

Every business faces risks, from financial fraud to supply chain disruptions. AI provides a proactive way to identify and mitigate these threats before they cause significant damage. By analyzing historical data, AI models can predict future risks with a high degree of accuracy, allowing you to make safer, more informed choices. For example, AI can flag suspicious transactions in real-time, predict equipment failures in a manufacturing plant, or assess credit risk with less human bias. This foresight helps protect your assets, ensure compliance, and maintain operational stability. It’s about shifting from a reactive stance to a preventative one, using data to stay one step ahead of potential problems.

More innovation

Innovation is the engine of growth, but it often requires sifting through endless possibilities to find the next big idea. AI can act as a catalyst for creativity by automating research and uncovering new opportunities. For instance, AI can analyze market data and customer feedback to show research teams what consumers truly want, guiding the development of successful new products. It can also brainstorm new concepts or optimize existing designs, freeing up your team to focus on the creative aspects of their work. By handling the heavy lifting of data analysis, AI empowers your organization to experiment more freely and bring groundbreaking ideas to market faster.

What faster AI deployments mean for your business

Reducing deployment time by nearly fourfold is more than a technical improvement. It has ripple effects across the entire business. Faster deployment changes how quickly organizations realize value from AI investments, how often they can iterate and improve, and how effectively they compete in markets where speed makes the difference. By turning long deployment cycles into rapid release cycles, Cake helps enterprises shift AI from an experimental initiative into a dependable driver of business impact.

1. Faster time to value

Models deliver measurable results sooner, whether that means improved customer experiences, better insights, or cost savings. Instead of waiting a quarter or more, enterprises see impact in weeks.

2. More frequent releases

Shorter deployment cycles enable a culture of iteration. Teams can deploy, measure, and improve models continuously, leading to higher-quality outcomes and a faster feedback loop.

3. Agility in competitive markets

In industries where speed matters, deploying AI-powered features first can be a decisive advantage. Faster deployment means enterprises can respond to opportunities quickly and stay ahead of competitors.

Ready to shorten your AI deployment timeline?

Case studies from Ping and Glean.ai demonstrate that enterprises using Cake can deploy models to production 3.9x faster than traditional approaches. Industry benchmarks confirm that traditional deployments often take months, while Cake customers are seeing results in weeks. By eliminating infrastructure hurdles and streamlining the deployment process, Cake transforms AI delivery from a slow march into a rapid cycle of experimentation and impact.

Ready to cut months from your deployment timelines? Talk to us.

Different approaches to AI deployment

Once a model is trained, the next step is getting it into the hands of users. This process, known as deployment, isn’t a one-size-fits-all operation. The right approach depends entirely on your specific use case, from the resources you have to the speed your application requires. The two most common paths are deploying to the cloud or embedding AI directly onto a device. Understanding the difference is key to building a strategy that works, as the infrastructure and management needs for each are fundamentally different. Choosing the wrong path can lead to significant delays and unnecessary costs, derailing a project before it even gets off the ground.

Most organizations lean on cloud infrastructure for its power and scalability, but this is often where projects stall. Research shows that many companies take an astonishing 18 to 24 months to get a single AI project fully operational in the cloud. By that time, the business problem you were trying to solve might have completely changed. This is why having a streamlined deployment strategy is so critical. The goal is to get your model working in a real-world environment quickly, so you can start learning from it and delivering value, rather than getting stuck in endless setup cycles.

Cloud-based AI

Cloud-based AI is the most familiar deployment model. It involves hosting and running your AI models on servers in a data center managed by a provider like AWS, Google Cloud, or Azure. This approach offers immense scalability, allowing you to handle massive datasets and complex computations without investing in your own physical hardware. However, this flexibility comes with a major challenge: complexity. Setting up the required infrastructure, ensuring it's secure, and integrating all the necessary tools is the primary reason deployment timelines stretch out for months. This is precisely the friction that a managed platform is designed to eliminate, handling the underlying infrastructure so your team can focus on the model itself.

Embedded AI

Embedded AI takes a completely different approach by running models directly on a physical device, like a smartphone, a car, or a factory sensor. The core idea is to bring the intelligence to the data, rather than sending the data to the cloud. This method is perfect for applications that need to make split-second decisions, operate without a constant internet connection, or handle sensitive information that shouldn't leave the device. As one expert puts it, embedded AI is about enabling devices to make "fast, private, and reliable decisions." Think of the facial recognition on your phone or the driver-assist features in a modern vehicle—these are prime examples of embedded AI in action.

How to choose the right AI platform or partner

Deciding between cloud and embedded AI is just the first step. The next, and arguably more critical, decision is selecting the right technology platform or partner to bring your vision to life. This choice will directly impact your project's speed, cost, and ultimate success. A great partner or platform acts as an accelerator, clearing technical hurdles and providing the expertise needed to move from concept to production smoothly. A poor choice, on the other hand, can introduce new layers of complexity, leading to delays and frustration. It's essential to look beyond flashy marketing and evaluate potential partners and platforms on their ability to deliver tangible results for your business.

What to look for in a partner

When evaluating a potential partner, you need someone who can build and implement AI in your business, not just talk about it. Look for a team with deep technical skills across the entire stack, from data infrastructure to model optimization. They should be fluent in modern AI techniques and have experience deploying solutions for large enterprises. Ask for proof of real-world results and check if they have experience in your industry. A strong partner should feel like an extension of your own team, capable of connecting AI to your core business goals and demonstrating a clear path to value. They should provide the tools and expertise to make your AI initiatives successful and sustainable.

Key questions to ask about a platform

When you're assessing an AI platform, it’s important to dig into the details. Start by asking how well it can handle different use cases and how easily it connects with your existing systems. Security and transparency are non-negotiable, so inquire about its compliance certifications and whether it allows for human oversight. A flexible platform should support a variety of AI models and be able to manage entire workflows, not just isolated tasks. Most importantly, ask if it can grow with you. The right platform should be scalable enough to meet your future needs, ensuring you won't have to start from scratch in a year or two.

The future of AI deployment

The world of AI deployment is changing fast. As models become more powerful and business needs grow more complex, the way we put AI to work is evolving. The next wave of innovation is moving beyond single-purpose models and toward more dynamic, interconnected systems. We're heading toward a future where AI doesn't just execute tasks but actively collaborates to solve complex problems. This shift will place even greater demands on the underlying infrastructure, making robust, scalable, and well-managed platforms more critical than ever. Staying ahead of this curve means thinking about not just what you need to deploy today, but what you'll need to support tomorrow.

Expert predictions on autonomous AI

Industry analysts are already pointing to the rise of autonomous AI. One prediction suggests that by 2027, a third of all AI projects in large companies will use autonomous AI agents that work together on complex challenges. Imagine a team of specialized AI agents—one for data analysis, one for customer communication, one for logistics—collaborating in real time to resolve a supply chain disruption. This level of coordination requires a sophisticated control plane to manage their interactions, monitor their performance, and ensure they operate securely. A platform like Cake, which provides a unified stack for orchestration and governance, is built to handle this complexity, providing the foundation needed for the next generation of collaborative AI.

Frequently asked questions

What's the difference between an enterprise AI platform and just using cloud services from AWS or Google? Think of it this way: cloud providers like AWS or Google Cloud give you the raw ingredients—the servers, storage, and databases. You still have to do all the work of connecting them, configuring them for AI, and building the systems to manage and monitor your models. An enterprise AI platform like Cake is more like a professional kitchen. It provides all the necessary infrastructure, pre-integrated and ready to go, so your team can focus on creating great models instead of spending months on setup.

Is the main bottleneck for AI deployment really the infrastructure, not the model itself? It’s a common misconception that the data science is the slowest part. While building a great model certainly takes skill, the most significant delays often happen when you try to make that model work in a live business environment. Setting up servers, ensuring the system can handle real-world traffic, navigating security approvals, and integrating various tools are complex engineering tasks that can easily take months. A solid platform automates most of that heavy lifting so you can get to the finish line faster.

My team already uses popular open-source tools. Do we have to abandon them to use Cake? Not at all. The goal of a good platform is to work with the tools your team already knows and trusts, not force them to start over. Cake is designed to integrate smoothly with popular open-source software like MLflow, Kubeflow, and Ray. This means your team can keep their existing workflows while gaining the benefits of a managed, production-ready environment, which avoids a painful and time-consuming migration process.

You mention a '3.9x faster' deployment. What does that actually mean for my team's timeline? That figure comes from comparing typical deployment timelines with what our customers achieve. Many companies spend two to three months or even longer getting a single model into production due to all the manual setup involved. With a streamlined platform handling the infrastructure, that same process can often be completed in just a few weeks. So, instead of waiting an entire quarter to see results from your AI investment, you could be launching and iterating on your model within the same month.

Our biggest delay is getting security and compliance approval. How does a platform help with that? This is a huge source of delays for many teams, and it's an area where a platform can make a significant difference. A platform like Cake helps by building compliance directly into the infrastructure from the start. For example, being SOC 2 and HIPAA ready means the platform already meets strict security and data privacy standards. This eliminates the long back-and-forth with security teams because the foundational controls they require are already in place and verified.