Enterprise AI teams often feel stuck with a tough choice. Do you spend months building everything from scratch? Or do you rely on a restrictive cloud platform like AWS SageMaker? It feels like a no-win situation. This is precisely where Cake beats the old model. We believe you shouldn't have to compromise. An aws cake deployment gives you a powerful, fully managed platform built from the best open-source tools, right inside your own environment. It’s the flexible aws sagemaker ai alternative that puts you back in control.
No one ever loves using SageMaker; it has such an underwhelming experience. However, SageMaker appears to solve an important dilemma by providing a complete, integrated ML lifecycle platform. For many organizations, the appeal is simple: put down your corporate credit card and gain access to a suite of machine learning (ML) tools without managing individual components.
But, SageMaker's tools lag years behind open-source innovations. Its "integrated" components require significant custom integration work, and its proprietary approach locks teams into AWS-specific solutions. Meanwhile, the open source AI ecosystem leads in nearly every category, with cutting-edge innovations coming from the community.
Cake flips this whole problem on its head. Instead of forcing you to choose between building everything yourself or getting locked into one vendor, they curate, integrate, and manage hundreds of best-in-class open source components. What you get is what SageMaker keeps promising but fails to deliver: a truly integrated, cutting-edge AI platform that adapts to your needs.
Why open source is the better choice for AI
The evidence is overwhelming: when it comes to AI and ML, open source consistently delivers the most advanced, effective solutions across the entire AI stack.
Consider the trajectory of recent AI breakthroughs. Open source solutions like Milvus, PgVector, and Qdrant were created to handle vector search and RAG applications while cloud vendors were still focused on traditional relational databases. Parameter-efficient fine-tuning with LoRA and QLoRA adapters appeared from their birth as open-source demonstrations as part of groundbreaking papers. Frameworks that are built on these research community PEFT breakthroughs, including Unsloth, TRL, and LLama Factory, helped reduce training costs by up to 90% or more, months or years before appearing in cloud platforms. Even the entire category of agentic workflows—from LangChain to CrewAI to AutoGen—originated as open source, driven by practitioners solving real problems rather than product managers planning roadmaps.
"By the time cloud vendors evaluate, integrate, and release new capabilities, the open source community has often moved on to the next breakthrough."
This pattern repeats across every category of AI tooling. By the time cloud vendors evaluate, integrate, and release new capabilities, the open source community has often moved on to the next breakthrough. The result is that teams using cloud-native tools are perpetually behind the curve, implementing last year's solutions while open source leverages this year's innovations.
Why modern AI runs on open source
Open source's dominance in AI stems from two fundamental realities that cloud platforms struggle to address: the need for customization and the speed of innovation.
Real-world AI applications have something of a long tail of required functionality. They require unique combinations of capabilities that standardized platforms struggle to anticipate as they focus on one-size-fits-all. Everyone's trying to do something that's just a bit different, and the basics are never enough for sophisticated use cases. Everyone would build their AI platform like Google if they could only afford to hire the 300 engineers it would take to build something custom. That’s why open source's modularity is so essential. Teams can integrate the best document processing pipeline with the most effective vector database and the most efficient inference engine, choices that are impossible within proprietary platforms.
You also see this advantage when breakthrough research emerges. Whether it's a new attention mechanism, efficient quantization technique, or novel agent architecture, open source implementations typically appear within weeks of publication, often contributed by the original researchers themselves. In contrast, cloud vendors require months of evaluation followed by years of implementation and enterprise certification processes. This means that by the time SageMaker offers a particular AI capability, it may be 2-4 years old.
Why Cake beats AWS SageMaker for AI
SageMaker markets itself as a comprehensive platform that eliminates ML infrastructure complexity. While this promise is compelling, the reality reveals significant gaps that Cake addresses.
Complete ML lifecycle management: SageMaker claims to handle every step within a single platform, but its tools are consistently "several years behind state of the art." Cake curates hundreds of best-in-class open source components, giving teams access to cutting-edge innovations within weeks of publication rather than waiting years for AWS integration.
Seamless integration: Despite marketing claims, SageMaker's components aren't truly integrated—teams must "spend the time to get them to work together," turning engineers into integration specialists. Cake applies the "Red Hat model" to AI infrastructure, providing pre-integrated components that work together seamlessly from day one.
Enterprise-ready features: While SageMaker offers compliance and governance features, it doesn't always have access to the cutting-edge features that modern AI development requires, especially compared to alternatives. Cake delivers enterprise-grade security and governance while maintaining access to the most advanced open source tools, avoiding the functionality gaps that plague proprietary platforms.
Managed infrastructure: SageMaker promises to abstract complexity but locks teams into AWS-specific solutions. When breakthroughs emerge in open source, users face a difficult choice: wait years for integration or abandon their investment. Cake runs in your infrastructure while preserving the flexibility to adapt as requirements evolve, delivering the "Google model" of custom AI infrastructure without requiring 300 engineers.
The result is what experts call a "general cycle of disillusionment"—teams must wait three or four years for cloud vendors to integrate innovations, turning SageMaker's apparent comprehensiveness into a strategic constraint.
Cake co-founder and CTO Skyler Thomas discussed scaling real-world RAG and GraphRAB systems using open-source technologies at the 2025 Data Council
A secure and scalable platform architecture
When you're building enterprise AI, the platform's architecture is everything. It’s the foundation that determines how secure, scalable, and flexible your projects can be. A weak foundation means you’ll spend more time fighting your tools than building innovative solutions. Cake provides a modern architecture that gives you the best of both worlds: the agility of open source and the security of a private cloud environment. Instead of forcing you to compromise, this approach is designed from the ground up to support the demanding needs of enterprise AI without locking you into a single vendor's ecosystem or exposing your data to unnecessary risks.
Deployed in your private AWS environment
One of the biggest concerns for any enterprise is data security and control. With Cake, the entire platform is deployed and managed directly within your own private AWS environment. This is a critical distinction. It means your data, your models, and your code never leave your control. You aren't sending sensitive information to a third-party service; you're running best-in-class tools inside your own secure perimeter. This model simplifies compliance, enhances security, and gives you complete ownership over your AI stack, which is something you don't always get with managed cloud services that operate as black boxes.
Built with best-in-class open-source tools
The core advantage of Cake is its commitment to the open-source ecosystem. While platforms like SageMaker make you wait years for new features, Cake curates hundreds of the best open-source components, making them available to your team within weeks of their release. This means you can work with cutting-edge innovations in vector databases, fine-tuning frameworks, and agentic systems as soon as they're proven effective. You get access to the most advanced tools on the market without the immense overhead of vetting, integrating, and managing them all yourself. It’s like having a dedicated R&D team that constantly brings you the best technology, fully integrated and ready to go.
Integrated with essential AWS services
Running in your AWS account doesn't mean running in isolation. The Cake platform is designed to integrate seamlessly with the AWS services you already use. It leverages foundational services like Amazon EKS for running applications, Amazon S3 for object storage, and even AWS Bedrock for generative AI, all while keeping your work securely inside your account. This hybrid approach allows you to combine the flexibility of open source with the power and reliability of native AWS infrastructure. You can build custom AI workflows using the best tools for the job while still benefiting from the scalability and robustness of the AWS cloud.
Enterprise security and compliance you can trust
For enterprise teams, security isn't just a feature—it's a prerequisite. Building powerful AI models is pointless if they can't be deployed securely or if they fail to meet industry compliance standards. This is often where teams get stuck, as balancing cutting-edge technology with strict security protocols can be a major challenge. Cake addresses this head-on by building enterprise-grade security and compliance into the fabric of the platform. This means you can move forward with your AI initiatives confidently, knowing that the underlying infrastructure is designed to protect your most sensitive data and meet rigorous regulatory requirements from day one.
Advanced security features
The platform is specifically built to handle sensitive data, making it suitable for organizations in highly regulated industries like finance and healthcare. The architecture is designed to align with stringent security standards, ensuring that you have the controls and safeguards needed to protect your data throughout the entire AI lifecycle. This focus on security means you don't have to choose between using the latest open-source tools and adhering to your company's HIPAA compliance or other data governance policies. You can innovate freely, knowing your security posture remains strong.
Verified by industry-standard certifications
Talk is cheap, which is why verifiable proof of security is so important. Cake backs up its security claims with industry-standard certifications. The platform has achieved both SOC 2 Type 2 and HIPAA/HITECH certification, demonstrating a strong and audited commitment to security and data protection. These certifications aren't just badges; they represent a rigorous, independent verification that the platform's controls and processes meet the highest standards for managing customer data. For enterprise leaders, this provides the assurance needed to adopt an open-source-first strategy without compromising on security or compliance.
Support for a wide range of AI use cases
A powerful platform is only as good as what you can build with it. Cake provides a secure, production-ready foundation for a massive range of generative AI and machine learning projects. Whether your team is focused on developing advanced chatbots, automating complex business processes, or fine-tuning models for specific tasks, the platform has the tools you need. You can implement sophisticated systems for Retrieval-Augmented Generation (RAG), build complex AI agent systems, fine-tune open-source models with your proprietary data, and efficiently manage inference and AI workflows. This versatility ensures that as your AI strategy evolves, your platform can evolve with you, allowing you to tackle new challenges and opportunities without needing to re-tool.
What open source AI means for your business
Cake's architectural differences translate into measurable business advantages that directly impact team productivity, operational costs, and strategic flexibility.
Get to market faster
The most immediate advantage teams experience with Cake is the dramatic reduction in time-to-production. Where SageMaker deployments typically require months of configuration, integration work, and troubleshooting, Cake helps teams move from concept to production in days or weeks.
This speed advantage stems from Cake's pre-integrated approach. Rather than spending weeks figuring out how to connect SageMaker's various components, teams can immediately access tools that work together seamlessly. The equivalent of 6-12 months of engineering work becomes available on day one, allowing teams to focus on innovation rather than infrastructure plumbing.
Achieve up to 6x MLOps productivity
This speed isn't just about launching faster; it's about fundamentally changing how your MLOps team operates. Instead of spending months on integration tasks and troubleshooting—common headaches with SageMaker—your engineers can focus on what actually drives business value. Cake eliminates this bottleneck by delivering a fully integrated AI platform that's ready on day one, effectively giving you back the equivalent of months of engineering work. This massive productivity gain means your team can spend their time experimenting with new models, refining algorithms, and deploying solutions that make a real impact, rather than just keeping the lights on.
Save money as you scale
Cake's cost advantages compound over time, delivering savings that directly impact the bottom line. Most significantly, Cake runs in customers' own Kubernetes clusters rather than relying on expensive, marked-up ML instances that cloud providers charge premium prices for.
Industry data shows that teams typically save 30-50% on compute costs by avoiding the markup that AWS applies to SageMaker instances. These savings become more significant as teams scale—a difference that can represent hundreds of thousands of dollars annually for organizations running substantial AI workloads.
Beyond direct compute savings, Cake eliminates the hidden costs of vendor lock-in. Teams avoid expensive data egress fees when moving between cloud providers, don't pay markup on storage access, and maintain the flexibility to optimize their infrastructure spending based on actual needs rather than vendor pricing structures.
"Industry data shows that teams typically save 30-50% on compute costs by avoiding the markup that AWS applies to SageMaker instances. These savings become more significant as teams scale—a difference that can represent hundreds of thousands of dollars annually for organizations running substantial AI workloads."
Transparent pricing models
Cake avoids the confusing, multi-layered pricing structures common with large cloud vendors. Instead of making you decipher a complex menu of services, Cake offers a more direct approach. Pricing is tailored to your organization's specific needs through a private offer system. This means you get a customized quote that reflects your unique requirements, ensuring you only pay for what you actually need. This transparent model helps you forecast budgets more accurately and avoids the surprise costs that can derail AI projects, giving you a clear financial path from the start.
Discounts for long-term partnerships
For organizations planning their AI strategy for the long haul, Cake provides significant financial incentives. Committing to a longer partnership allows you to lock in substantial savings, making your AI investments even more cost-effective over time. For example, teams can secure a 15% discount with a 24-month contract or save up to 30% with a 36-month contract. This rewards forward-thinking teams and builds a stable foundation for continuous innovation, turning a long-term vision into a financially smart decision.
Tools for effective cost management
Beyond initial savings, Cake empowers you to maintain financial control as your AI initiatives grow. The platform includes a suite of tools designed to help you effectively monitor and manage your AI spending and resource usage. This visibility is critical for optimizing your investments and ensuring you're getting the most value from your infrastructure. Instead of flying blind, your team can make data-driven decisions about resource allocation, prevent budget overruns, and confidently scale operations without losing track of costs.
Avoid vendor lock-in
Perhaps most importantly for long-term success, Cake provides vendor independence that preserves strategic flexibility. While SageMaker locks teams into AWS-specific solutions, Cake's multi-cloud architecture allows organizations to deploy workloads wherever they make the most sense—whether that's optimizing for cost, compliance, or performance.
This flexibility matters more than you might think, especially as your AI needs change. Teams can move workloads to specialized hardware as it becomes available, take advantage of pricing differences between cloud providers, or meet changing compliance requirements without rebuilding their entire infrastructure.
The vendor independence also future-proofs AI investments. Rather than being constrained by AWS's product roadmap and pricing decisions, organizations maintain control over their AI infrastructure evolution. When breakthroughs emerge or business requirements change, teams can adapt their stack without being limited by vendor-specific implementations.
How Cake helps your team succeed
Beyond the technical advantages, a platform's real value comes from its usability and the support system behind it. Cake is designed not just to be powerful, but also accessible and fully supported, ensuring your team can move forward with confidence. We provide dedicated support options tailored to your needs and have made it simple to get started through channels you already use. This means you can focus on building great AI without getting stuck on infrastructure or tooling issues.
Dedicated support options
Standard support for all customers
Every Cake customer gets access to our standard support, which is designed to give you the help you need, when you need it. This isn't a faceless ticket system that leaves you waiting for days. You'll have direct lines of communication through email, a shared Slack channel, and regular meetings with our team of experts. We're available during business hours (9 AM–5 PM EST, Monday–Friday) to answer your questions, troubleshoot issues, and provide guidance. We believe solid, accessible support shouldn't be a premium add-on; it's a fundamental part of ensuring your AI initiatives are successful from the very start.
Enhanced support for critical workloads
For teams running AI applications where uptime is non-negotiable, we offer our Production SLA Add-On. This option is perfect for mission-critical workloads that require the fastest possible response times. When an urgent issue arises, you can’t afford to wait around for a fix. With this enhanced support, you get prioritized help, with response times as fast as three hours for the most critical problems. It’s the peace of mind you need to confidently deploy and scale your most important AI projects, knowing that an expert team is ready to resolve issues quickly and efficiently.
Find Cake on the AWS Marketplace
Getting started with a new platform shouldn't be a bureaucratic headache. That's why we've made Cake available directly on the AWS Marketplace. This makes procurement and deployment incredibly straightforward for organizations already using AWS. You can use your existing account and billing, which simplifies the entire process and lets you integrate Cake into your workflow without extensive setup. Our platform is built to help large companies successfully build and run their most ambitious AI and Machine Learning projects. By making Cake accessible through the AWS Marketplace, we're removing friction so your team can start innovating faster.
How to future-proof your AI strategy
SageMaker can work for organizations with simple, vanilla ML use cases and heavy AWS ecosystem investment. If you're building basic recommendations or classification models, prioritize procurement simplicity over technical excellence, and can tolerate integration overhead and innovation lag, SageMaker may meet your basic requirements.
However, Cake is the clear choice for organizations where AI represents a competitive advantage. Teams building cutting-edge applications (e.g., sophisticated RAG systems, agentic workflows, or custom architectures) require the flexibility and innovation speed that only best-in-class open source platforms provide.
The choice between SageMaker and Cake ultimately comes down to a fundamental question: Will your organization be constrained by what a single cloud vendor decides to provide, or will you harness the innovation speed of the broader AI ecosystem?
SageMaker offers procurement simplicity but at the cost of innovation lag, integration overhead, and vendor lock-in. Cake delivers what industry experts call "the power and the speed"—access to cutting-edge open source innovations with enterprise-grade integration and reliability.
The infrastructure decisions you make today will determine your AI capabilities for years to come.
Frequently asked questions
My team is already familiar with open-source tools. What's the real benefit of using Cake instead of managing them ourselves? That’s a great question. Many talented teams can absolutely build their own platform from open-source components. The value of Cake isn't about replacing that skill, but about redirecting it. We handle the time-consuming work of vetting, integrating, and managing hundreds of tools so they work together seamlessly. This allows your team to skip the months of infrastructure plumbing and focus their expertise on building models and applications that directly impact your business.
You say Cake runs in our own AWS environment. How does that actually work, and what does it mean for our data security? It means the entire Cake platform is deployed and managed directly within your organization's private AWS Virtual Private Cloud (VPC). Your data, your models, and your code never leave your secure perimeter to be processed by a third-party service. This approach gives you complete ownership and control over your AI stack, which simplifies meeting strict compliance standards like SOC 2 and HIPAA and ensures your most sensitive information remains protected.
Open source offers the latest tech, but it can be a headache to manage. How does Cake solve the integration and maintenance problems? You're right, keeping up with the open-source ecosystem can feel like a full-time job—and for us, it is. We take on the responsibility of continuously evaluating and integrating the best-in-class tools so you don't have to. When a new framework or database proves its worth, we do the work to make it a stable, secure, and integrated part of the platform. This frees your team from the constant cycle of patching, updating, and troubleshooting compatibility issues.
If SageMaker and Cake both run on AWS, what's the fundamental difference in your approach? While both platforms use AWS infrastructure, the core philosophy is very different. SageMaker offers a suite of proprietary tools that are often years behind the open-source community. Cake brings the most advanced open-source innovations directly into your environment, fully managed and integrated. This gives you access to cutting-edge technology within weeks of its release and ensures you never get locked into a single vendor's product roadmap or pricing model.
We're interested, but our procurement process can be complicated. How do we actually get started with Cake? We've made this as straightforward as possible by making Cake available directly on the AWS Marketplace. This allows you to use your existing AWS account and billing, which helps simplify the procurement cycle for most organizations. We work with you to create a private offer that is tailored to your specific needs, so you get transparent pricing that aligns with your budget from day one.
Key Takeaways
- Gain a competitive edge with open source: While proprietary platforms like SageMaker leave you years behind, Cake integrates the latest open-source innovations, giving your team access to cutting-edge AI tools as they emerge.
- Accelerate deployment and lower your costs: Cake's pre-integrated platform eliminates months of engineering work and can cut compute spending by 30-50%, allowing you to ship faster and reinvest savings into innovation.
- Own your AI stack without vendor lock-in: Cake deploys into your private AWS environment, giving you full control over your data and models. This secure, flexible approach means you can adapt your strategy without being constrained by a single vendor's roadmap.
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