Amazon Bedrock vs Google Vertex AI
The question that decides it: Which cloud already holds your data, and do you need FedRAMP High or explicit per-feature ZDR documentation?
Our verdict
Whichever cloud your data is already in — the egress and integration cost dominates any feature difference. Where that is genuinely open: Bedrock for FedRAMP High and the widest third-party model vendor list; Vertex AI for the clearest documented data-governance guarantees.
Why
Neither of these is a gateway in the sense the rest of this catalog means it. Both only route to models their own cloud hosts, so neither can fail over to the other, or to OpenAI directly. If you want cross-cloud failover you need a real gateway in front of one or both, and that changes the shape of the whole decision.
Given that, the honest answer is usually "the one you are already on". Vertex AI's value drops sharply if your data is not already in Google Cloud, once you count egress and integration. The same logic applies in reverse to Bedrock. Model quality differences between the two are far smaller than the cost of moving your data across a cloud boundary to reach them.
Where the decision is genuinely open, the differentiators are concrete. Bedrock brings 19 model providers plus custom model import under one AWS API and one bill, and a compliance posture including FedRAMP High — which is decisive for US public sector work and has no equivalent on the Vertex side of this comparison. It also has built-in Guardrails, evaluations, Knowledge Bases and AgentCore rather than bolt-on tooling, and cost levers in Batch and Flex at 50% off standard.
Vertex AI's edge is documentation quality on data governance, which sounds unglamorous and matters enormously in regulated procurement. It publishes per-feature zero-retention conditions and a dedicated EU multi-region endpoint, and Generative AI on Vertex AI is explicitly named in scope for SOC 2 Type II with quarterly audits. When a security reviewer asks "prove this specific feature does not retain our prompts", Vertex is the easier document set to answer from. It also has the broadest first-party modality coverage — Gemini text, Imagen, Veo video, Chirp audio, Lyria music, embeddings — in one platform.
Both have the same two structural problems. Pricing is fragmented in ways that make forecasting genuinely hard: Bedrock across tiers, Provisioned Throughput units and per-feature surcharges; Vertex across tokens, modality units, GSUs, grounding calls and cache storage. And both are deep lock-in — IAM, regions and fine-tuned artifacts do not port from one to the other.
Which one, concretely
Choose Amazon Bedrock if
- Your data and workloads are already in AWS
- You need FedRAMP High, CSA STAR Level 2, or an AWS BAA
- You want 19 model providers plus custom model import under one API
- You want Batch and Flex at 50% off standard pricing as a cost lever
Choose Google Vertex AI if
- Your data and workloads are already in Google Cloud
- You need explicit per-feature zero-retention documentation and an EU multi-region endpoint
- You want Gemini alongside 200+ Model Garden models
- You need broad modality coverage — text, image, video, audio, music — from one vendor
What catches people out
- Neither can route to models the other hosts. Cross-cloud failover requires a separate gateway in front.
- Model availability varies by region on Bedrock, so a model you tested may be unavailable where your data must legally live. Check per-region before designing around one.
- Vertex AI's residency and ZDR guarantees do not extend to global endpoints for partner and open-weight models — the guarantee is narrower than the headline.
- Both have fragmented pricing that makes forecasting hard. Model your actual workload rather than reading a rate card.
- Azure AI Foundry's Model Router and 11,000+ model catalog is worth adding to this comparison if Microsoft is also in play.
Side by side
1 of 15 fields differ, marked with a dot. Every figure links to the vendor page it came from. Blank values read Not published rather than No — silence from a vendor is not a negative answer.
| Field | Amazon Bedrock | Google Vertex AI |
|---|---|---|
| Ease of leaving Derived score, higher is easier | 60/80 Some work to leave | 60/80 Some work to leave |
| What kind of product Category | Cloud platform | Cloud platform |
| Who runs it Deployment model | Managed only | Managed only |
| Licence Licence | Proprietary | Proprietary |
| Models available Models available | ~100 | ~200 |
| Model providers reachable Upstream providers | Not published | Not published |
| SOC 2 audited SOC 2 audited | Yes | Yes |
| Will sign a HIPAA agreement HIPAA BAA | Yes | Yes |
| Does not retain your data Zero data retention | Yes | Yes |
| Can keep data in the EU EU data residency | Not published | Yes |
| Content guardrails Content guardrails | Yes | Not published |
| Quality testing Evals | Yes | Yes |
| Batch processing Batch processing | Yes | Yes |
| Rule-based routing Conditional routing | Yes | Not published |
| Prompt versioning Prompt management | Not published | Yes |
| Settings can live in version control Declarative config-as-code | Yes | Yes |
Verified 3 days ago for Amazon Bedrock and Verified 3 days ago for Google Vertex AI. Want more fields, or a third option in the mix? Open these two in the full comparison tool.
Common questions
Can Bedrock or Vertex AI act as a multi-cloud gateway?
No. Each only routes to models hosted by its own cloud. Bedrock cannot fail over to OpenAI, Azure or Google endpoints, and Vertex AI cannot fail over to AWS or Azure. Cross-cloud routing needs a dedicated gateway such as LiteLLM, Portkey or OpenRouter in front.
Which has better data residency guarantees?
Vertex AI has the more explicit documentation: per-feature zero-retention conditions and a dedicated EU multi-region endpoint, with Generative AI on Vertex AI named in scope for SOC 2 Type II. Note that those guarantees do not extend to global endpoints for partner and open-weight models.
Which is cheaper?
Neither has a straightforwardly cheaper rate card, and both fragment pricing across multiple units — Bedrock across tiers, Provisioned Throughput and per-feature surcharges; Vertex across tokens, modality units, GSUs, grounding calls and cache storage. In practice the dominant cost is usually data egress if your workload is not already in that cloud.