Between 2024 and 2026 the leading foundation models ended up with similar benchmark scores, so a benchmark chart won't tell you much about which platform to buy. What separates the options is how your people will use it, whether your data can stay in your own cloud, whether you can switch models, how you're billed, and how well it fits the systems you already run. Settle those and most vendors drop off the list.
Five things to decide before looking at vendors
How people will use it tells you which kind of product to look at. If employees will work inside office software, you want something like Copilot. If engineers will build on APIs, you want a foundation-model platform. If AI needs to live inside existing business systems, you want something like Agentforce. These are three different layers of product, and comparing across them doesn't get you far.
Deployment and compliance comes down to whether it can run inside your own cloud tenant. Azure OpenAI supports private deployment within your Azure tenant with Azure AD permissions. [1] Regulated industries like finance and healthcare usually require that, which rules out pure-SaaS products.
Model choice means deciding between one vendor's models and a multi-model gateway. AWS Bedrock offers Claude, Llama, Mistral and Titan[2] behind one API, which suits teams that don't want to be tied to a single model. If you've already committed to one model family, Copilot Studio or the vendor's own platform is simpler.
Billing is either per seat or per token. Copilot and ChatGPT Enterprise charge per user; Bedrock and Vertex meter usage. High-volume automation usually costs less on metered pricing, while a broad rollout with light use is easier to budget per seat. The choice also decides which department pays. It's the same reason Claude's Team and Enterprise plans are priced so differently, which we covered earlier.
Fit with your current systems: heavy Microsoft 365 users can connect Copilot Studio with almost no development work, teams whose data lives in BigQuery usually go with Vertex AI[3], and ServiceNow customers go straight to its workflow AI. The deeper the integration, the cheaper the rollout, and that matters more than a longer feature list.
Which products fit which teams
Foundation layer. Azure OpenAI fits Microsoft shops in regulated industries, mainly because of private deployment. AWS Bedrock is the most flexible multi-model platform, good for teams that want to be able to change models. Vertex AI is strong on multimodal work and BigQuery ML; teams already on Google Cloud should start there, and others should look more carefully.
Application layer. Copilot Studio lets business teams build agents without writing code and connects directly to Microsoft 365. ServiceNow puts AI into IT service management and ticketing for companies already running on it. Teams that skip this layer and connect foundation models directly often spend 40 to 60 percent of their engineering effort rebuilding access control, audit logging and authentication, which these platforms already handle.
Business-system layer. Products like Salesforce Agentforce put AI inside CRM workflows[4]. What matters here is how well it fits your sales or service process, not which model runs underneath.
Questions to answer before signing
Does our data leave our cloud? Are audit logs and permission inheritance built in? Are we paying per seat or per use? Which of our existing systems does it connect to? What's the fallback if it fails? If you can't answer three of the five, don't sign yet, however good the pitch looks. As things stand, regulated Microsoft shops tend to pair Azure OpenAI with Copilot, teams that want model choice go with Bedrock, and companies on Google Cloud go with Vertex.
Sources: AI Advisory's layered review of enterprise AI platforms (Azure OpenAI, AWS Bedrock, Vertex AI, Copilot Studio, ServiceNow).