The hidden cost of Amazon Nova 2

Amazon’s Nova 2 announcement at AWS re:Invent 2025 is exactly the type of AI offering we expected from AWS and, frankly, exactly what should make thoughtful architects nervous. Nova 2 is positioned as a frontier-grade model, tightly integrated with Amazon Bedrock. It’s part of a growing ecosystem of “frontier agents” and the AgentCore framework unveiled at re:Invent 2025. The story is compelling: better models, better tools, and a single platform to build, deploy, and scale agentic AI.

And yet, there’s a problem. It isn’t that Nova 2 is technically weak. The problem is that it is strategically strong in all the wrong ways for customers who care about independence, portability, and long-term value. AWS is not just selling you a model; the company is selling you an entire worldview where your agentic fabric, data flows, and operational patterns are deeply rooted in one cloud.

Vendor lock-in versus actual value

Lock-in is a spectrum, and the ecosystem of Nova 2, Bedrock, and AgentCore pushes you far toward the “tightly coupled” end of that spectrum. On paper, you get convenience: native integrations, managed infrastructure, observability, and security primitives that understand the agentic constructs AWS has defined. In practice, you are anchoring the core of your emerging AI capability into APIs, runtimes, and orchestration semantics that exist only within AWS.

The question I want enterprises to ask is simple: Are you optimizing for the next six quarters or the next six years? It’s likely that during the next six quarters, Nova 2 and its ecosystem will make you more productive. But during the next six years, the cost of migrating away from this ecosystem—or even meaningfully using a second cloud for AI—will rise dramatically. Your agents will be written to AWS’s tool APIs, observability model, security posture, and the way AWS wires agents to data and events. That is not theoretical lock-in; it is baked into every line of code and every workflow you build.

If you view AI as a transient experiment, this may not bother you. If you believe, as I do, that agentic systems will become the operational nervous system of most enterprises, then concentrating that critical capability inside a single vendor’s ecosystem is a strategic risk, not a feature.

Agentic fabric: native versus portable

The notion of an “agentic fabric” is useful: a mesh of agents that can reason, act, and collaborate across data sources, applications, and infrastructure. AWS’s vision is a cloud-native fabric where agents are first-class citizens inside services like Bedrock, wired to Lambda, Step Functions, EventBridge, and a growing set of AI-ready data services. The fabric is smooth—as long as you stay inside their walls.

The alternative is a cloud-portable fabric. Instead of building directly against closed, vendor-specific agent frameworks, you define agents in terms of more open abstractions: model-agnostic interfaces, cross-cloud orchestration, and data access layers that do not assume a particular vendor’s storage or event model. You might still run agents on AWS, but you can also run them on other clouds, on-premises, or at the edge without rewriting them from scratch.

Nova 2 and the surrounding tools tilt you hard toward cloud-native and away from cloud-portable. When your agents depend on AWS-specific features—say, Bedrock’s proprietary agent orchestration primitives or AWS-only plug-in patterns—your portability story collapses. The cost to move is not just “change the model endpoint”; it becomes “re-implement how the agent thinks, acts, and integrates.” That type of cost kills multicloud strategies in practice, even when they look good on PowerPoint.

Operational burden or simplification

AWS is selling Nova 2 and AgentCore as simplifying complexity and, in some respects, that is true. You get unified observability, integrated security, and pre-packaged patterns for building safe, production-grade agents. But let’s be very clear about what is happening. AWS is not removing complexity, it is encapsulating it inside black boxes you do not control.

When those black boxes malfunction, drift, or change, you are at the mercy of AWS’s release cadence and operational practices. You will still need teams who understand the behavior of your agents in detail, but you will be diagnosing problems in systems whose core behavior is defined by a vendor’s code and policies, not your own. That is a different kind of fragility. Instead of owning complexity you can see and manage, you’re renting complexity and hoping it behaves.

On top of that, operations teams now have to understand not only distributed cloud-native systems, but emergent, probabilistic agent behavior embedded within them. If your observability, governance, and control mechanisms are all bound to AWS-specific services, you lose the ability to build a unified operations view across clouds and on-prem systems. AWS wants to be your single pane of glass, but the reality is that most large enterprises need several panes, and those panes must interoperate.

Taking the long view

When you adopt Nova 2 and its ecosystem as your primary agentic platform, you are choosing a vertically integrated stack. The immediate upsides are undeniable: optimized performance, deep integrations, turnkey security patterns, and less glue code. For many teams, particularly those that are small, under-resourced, or deeply aligned with AWS already, this is a rational short-term decision.

But the downsides show up over time, and they show up at the architectural level, not in developer convenience. You lose leverage on pricing as your dependence on AWS-specific agent capabilities grows. You will find it harder to adopt innovations that emerge on other clouds or in open source communities, because your systems are built around a specific model of agents and tools. You will discover that “multicloud” has devolved into “one primary cloud for anything that matters and some residual workloads elsewhere,” which is exactly the outcome the big clouds are optimizing for.

If you want more open and portable approaches, you pay more up front. You build or adopt neutral orchestration layers, use frameworks that abstract model providers, and design observability that spans heterogeneous environments. You resist the gravitational pull of single-vendor AI fabrics, even when they look impressively polished. The payoff is flexibility: the ability to change direction when economics, regulation, or innovation demand it, without rewriting the nervous system of your enterprise.