Cloud for Intelligent Agents in Banking: AWS vs Google Cloud (2025/2026)
State of the Art in 2025: Models That Changed the Equation
In 18 months, the foundation model stack available on managed cloud platforms changed radically. AWS launched Amazon Nova Pro and Nova Lite in November 2024 — proprietary multimodal models trained for low latency and high throughput in transactional applications. Anthropic released Claude 3.7 Sonnet in February 2025, introducing "extended thinking" — the model solves complex problems in multiple chained steps with full traceability. Google responded with Gemini 2.0 Flash (February 2025) and Gemini 2.5 Pro (March 2025), the latter with a 1M token context window and native tool use without fine-tuning.
Agent Frameworks: Bedrock Flows vs Google Agentspace
AWS introduced "Bedrock Agents Flows" in 2024: a multi-agent orchestration model where a supervisor decomposes tasks and delegates to specialized agents. Each subagent has access to tools (action groups) — banking core APIs, vector Knowledge Bases, or SQL against Redshift — and the flow captures the full execution graph, enabling native audit traces in CloudWatch for each agent decision.
Google launched "Agentspace" in January 2025 — a layer over Vertex AI with native access to Google Workspace, BigQuery, and Vertex AI Search, plus a pre-built UI for non-technical users. For banking, it enables deploying a portfolio analysis agent or compliance assistant over BigQuery without additional code. The key architectural difference: Agentspace is designed for business users, Bedrock Flows for platform engineering.
Security 2025: Model Armor, KMS, and Context Caching
Google launched Vertex AI Model Armor in February 2025: a proxy defense layer against prompt injection, jailbreaking, and data exfiltration that operates before and after the model. For banking with external client inputs, it is the first commercially SLA-backed prompt security solution on managed cloud. AWS offers KMS Customer Managed Keys (CMK) in Bedrock since 2024, guaranteeing that conversation logs and fine-tuned models are encrypted with bank-exclusive keys — AWS cannot access any conversation content without explicit authorization.
Prompt caching (Anthropic on Bedrock, Google on Vertex) transforms TCO: the bank's system prompt with policies (CMF/SBIF regulation, agent instructions, client context) is computed once and cached tokens cost 90% less. For an agent with a 10K-token system prompt processing 500K operations/month, the reduction can be USD 50-80K/month.
⚡ <strong>Recommended stack 2025:</strong> Claude 3.7 Sonnet (extended thinking + prompt caching) for complex credit/compliance decisions. Gemini 2.0 Flash + Model Armor for high-frequency with external inputs. Gemini 2.5 Pro (1M ctx) for full portfolio analysis without RAG.
2025 Takeaways
- Claude 3.7 Sonnet with extended thinking is the best available model for auditable step-by-step reasoning — critical for justifying credit or compliance decisions to the regulator.
- Gemini 2.5 Pro with a 1M token window enables full portfolio analysis in context, without chunking or RAG. For banks with portfolios of 10K-100K credits, this eliminates a major source of retrieval errors.
- Vertex AI Model Armor (Feb 2025) is the most comprehensive prompt security layer on managed cloud. For banking agents with external client inputs, it is non-negotiable.
- Prompt caching on both platforms reduces TCO for high-frequency agents by 60-90%. Model cached vs non-cached token costs before signing Enterprise Agreements.
- The optimal multi-platform architecture for 2025: Bedrock (Claude 3.7 + Nova Lite) for transactional with native audit; Vertex AI (Gemini 2.5 Pro + Model Armor) for complex analysis and prompt protection.