I should explain why a non-engineer spent two weeks scoring enterprise AI platforms. When I deploy AI for a client, I’m making a bet on a vendor’s platform with someone else’s money. I wanted to know which bets were safe. So I built a 28-point scorecard — model choice, memory, governance, cost, whether the vendor itself is financially stable — and went through nine of them: Microsoft Azure, Google, Amazon, Oracle, Databricks, Snowflake, Salesforce, IBM, ServiceNow.
I’m sharing this because I’m exactly the audience these platforms’ marketing confuses on purpose, and I had to dig past the brochures to find anything true.
Here’s the actual scorecard. Ten criteria, scored 0–10 each: model flexibility, memory, RAG (how well it searches your own documents), tool connections, deployment, the interface, beginner-friendliness, governance, pricing, and ecosystem. The last column is the overall.
| Platform | Models | Memory | Tools | Deploy | Beginner | Govern. | Ecosystem | Overall |
|---|---|---|---|---|---|---|---|---|
| Azure AI Foundry | 10 | 8 | 10 | 9 | 8 | 9 | 10 | 8.8 |
| Google Vertex AI | 8 | 7 | 8 | 8 | 7 | 8 | 8 | 7.6 |
| Salesforce Agentforce | 5 | 6 | 8 | 6 | 9 | 8 | 10 | 7.6 |
| Oracle Cloud AI | 6 | 9 | 6 | 7 | 8 | 9 | 9 | 7.4 |
| Amazon Bedrock | 9 | 6 | 7 | 8 | 5 | 9 | 8 | 7.3 |
| IBM watsonx | 7 | 5 | 8 | 9 | 6 | 9 | 7 | 6.9 |
| ServiceNow | 4 | 6 | 8 | 5 | 7 | 9 | 9 | 6.7 |
| Databricks | 9 | 5 | 8 | 7 | 4 | 9 | 7 | 6.6 |
| Snowflake Cortex | 6 | 5 | 6 | 7 | 6 | 8 | 7 | 6.5 |
And how long it actually takes to get a first working agent on each — the number nobody puts on the landing page:
| Platform | Time to first agent | Path |
|---|---|---|
| Oracle Cloud AI | 15–30 min | No-code visual builder |
| Salesforce Agentforce | 30–60 min | Low-code builder |
| Google Vertex AI | 30–60 min | Low-code builder |
| Azure AI Foundry | 1–2 hrs | Visual designer + hosted agents |
| Snowflake Cortex | 1–2 hrs | SQL-based |
| IBM watsonx | 2–4 hrs | Quickstart + flow builder |
| Amazon Bedrock | 3–5 hrs | Lambda + knowledge base + custom frontend |
| Databricks | 4–8 hrs | Python + vector search + model serving |
The short version of what the table says: Microsoft’s Azure AI Foundry came out on top for my use case, with Google’s platform as a strong second. Oracle’s free tier is a genuinely useful sandbox to learn on — real compute, forever free — but nothing more, because the company carries over $100B in debt. Snowflake I’d skip for this purpose entirely.
But the scores aren’t the useful part. The useful part is what the marketing pages don’t tell you:
The big numbers are mostly marketing. One platform boasts about 11,000 available models. The number you can actually deploy for real agent work is 40 to 70. The rest need expensive managed hardware.
“Memory” is gated. One major platform’s long-term memory — the thing that lets an AI remember a returning user — only works with that vendor’s own models. Bring a cheaper outside model and memory silently stops working.
Geography follows your billing address, not your server. With an India billing account, certain well-known models simply can’t be deployed, no matter which region you pick.
Permissions don’t carry over. You can build and test an AI assistant in the playground, publish it, and watch it break — because the published version needs its own separate permissions that nobody mentions.
The deeper thing I took away: you’re not really choosing features, you’re choosing a vendor’s balance sheet. The giants will outspend everyone and their platforms will improve fastest. When you commit to a platform, part of what you’re buying is confidence the company will still be investing in it in three years. That mattered more to me than any single feature on the scorecard.