Google Vertex AI vs Truefoundry
Building an AI application or Agent requires a lot of components and has a lot of moving parts than people estimate. The team needs to take care about models, compute, databases, testing and evaluation, guardrails, MCP servers, monitoring, deployment and what not before you even get started. The team should be focusing on their problem and the business value rather than fundamental pieces of infrastructure common to all projects.
For typical software engineering projects this has been figured out previously but AI products are unique and needs different sets of tools. That is where AI native infrastructure comes into picture through companies like Truefoundry or products Google Cloud Vertex AI. In this article we will be comparing the two in terms features, ease of use, cost and enterprise integration. Although there are other platforms also available we will limit our scope to these two for now. If you are an enterprise looking to build an AI product internally or externally facing, this article will guide you to make the right decision.
I'm myself an AI/ML Engineer who has worked on building AI applications and used both of these and so can share some perspective apart from comparing raw features.
Before we go forward lets explore what each platform is
What is Vertex AI
Vertex AI is Google Cloud’s fully managed machine learning platform for training, fine-tuning, deploying, and monitoring ML and generative AI models at scale.
It provides native integration with Google models (Gemini), open-source frameworks, data services, and MLOps tools within the GCP ecosystem.
What is Truefoundry ?
TrueFoundry is an enterprise AI platform that helps teams build, deploy, and operate ML/LLM applications reliably using a production-first, outcome-driven approach.
It focuses on end-to-end ML/AI lifecycle management—covering training, inference, monitoring, cost control, and governance on customer cloud infrastructure.
Features and Components
models, compute, databases, testing and evaluation, guardrails, MCP servers, monitoring, deployment
We can divide the use cases into two major groups in which both of these platforms can help
Training or fine tuning models
This would be important if you have some proprietary or custom data or a specific task which generic models may not be able to do
Training a machine learning or AI model essentially required data, compute and training algorithms. So any platform serving for this use case should be able to integrate all three seamlessly. It could be training a classical machine learning model on tabular data, or fine tuning an image classification model or pre-training an LLM, these essential requirements remain there.
On top of that you might need a cloud notebook to run your code, a way of log your experiments to select the one with best metrics, deploy your models, and post production monitoring.
Building AI applications using pre-trained models
With the advent of general purpose large language and vision models, you don't really train or even finetune the models. You don't even labeled which is the major bottleneck in training models. You use already trained models (because it's expensive to train your own what is already out there), customise the input and output for your application and design a system around it.
Since you want to use someone else's model you need a central platform (AI Gateway) to connect to those models. These models could be proprietary models like OpenAI GPT5, Claude or Gemini or opensource models like Llama3, Qwen or Deepseek. For proprietary models, you need to connect through api credentials but for open-source models you need to deploy and serve it using your own infrastructure.
Verdict
Both the platforms have the features needed, Google Cloud Vertex AI might be better if you are in Google ecosystem and want to stay within it maybe because of native big query integration or gemini models.
If you want flexibility and freedom, Truefoundry is the way to go because it can be connected to Azure, Google cloud (including vertex ai models) or even on prem which is important for a lot of enterprises.
Ease of Use
This one can be subjective and I might be biased but I like interface, ease of use and user of experience much better for Truefoundry than Google Cloud Vertex AI. Vertex AI as any google cloud can feel clunky and difficult to navigate. Especially in enterprise settings where security permissions and access is a major thing, the intuitive interface of truefoundry makes it easier to move faster through these hurdles.
Verdict
Go for truefoundry if you want faster developer adoption and less time wasted in back and forth with security teams. Go for Vertex AI if already familiar with it, even then give truefoundry a try
Cost Comparison
Vertex AI
Managed convenience comes with a premium
Effective GPU cost is ~1.15× raw GCP GPU pricing due to Vertex management fees (Vertex AI pricing )
Cost compounds for long-running or iterative training jobs
TrueFoundry
Uses raw GCP GPU pricing (no per-hour markup)
One time fee already covered → no incremental platform cost
Enables future shift to on-prem GPUs, where hardware buy cost ≈ 3 months of cloud spendOutcome:
TrueFoundry offers ~15% lower training cost immediately and significantly lower long-term TCO.
Verdict
If you are already using Google Cloud and Vertex AI and your usage is less, you wont mind the 15% surcharge on GPU cost but for large workloads Truefoundry would be better
Enterprise Integration
Truefoundry offers centralized governance, key management, and cost controls that Vertex AI lacks, especially when integrating multiple cloud providers like Azure OpenAI. Truefoundry's gateway provides unified budget enforcement and compliance controls, essential for enterprise-scale deployments. For example, two layers of key management would be need while using Azure OpenAI in Google Vertex AI.
From a developer standpoint, they get flexibility to try out things that may or may not be available in Vertex and the moment some scale is reached there would be cost benefit from on-prem GPU.
So,
- Centralized Governance
- On prem GPU support for cost benefits
- Developer Flexibility