awsIt feels like every week there’s another major AI model release. This time, it’s Anthropic’s Claude 3.5 Sonnet landing on Amazon Bedrock. The benchmarks are impressive—faster, cheaper, and smarter, especially at tasks like coding and visual interpretation. Your engineering teams are probably already asking to use it.
And they should be. But as a technology leader, your job is to look past the model's capabilities and ask a more fundamental question: what does it mean to run this in production, at scale, on someone else's infrastructure?
New Model, Same Infrastructure
Here’s the part that gets lost in the excitement. Claude 3.5 Sonnet, for all its power, is just another service running on AWS. It’s subject to the same physical constraints, network paths, and operational realities as your EC2 instances and S3 buckets. It doesn’t get a special, more resilient corner of the data center.
When you use a managed service like Bedrock, you’re abstracting away the underlying complexity, but you’re not eliminating the risk. The service is still bound by an SLA—in this case, the Amazon Bedrock Service Level Agreement. And if you read it, you’ll find it’s focused on Monthly Uptime Percentage. It doesn't account for performance degradation, increased latency, or other issues that can render your AI application useless without technically breaching the uptime SLA.
I’ve spent years inside these environments. The pattern is always the same. A new, powerful tool is adopted quickly, but the operational governance—the systems for tracking spend, performance, and accountability—lags behind. With AI, this gap is widening faster than ever. A recent report from Splunk and Oxford Economics found that 67% of leaders say the complexity of their cloud footprint is growing beyond their ability to manage it. Adding a dozen new AI services doesn't make that any simpler.
The Mismatch Between Expectation and Reality
Your teams expect these cutting-edge AI services to be flawless. The marketing tells them they're state-of-the-art. But in reality, they're just workloads. They can be slow. They can have errors. They can go down.
When your customer-facing chatbot suddenly takes 15 seconds to respond because of an underlying Bedrock latency issue, the end-user doesn't care about the model's benchmark scores. They just know your app is broken. And because you’re relying on a managed service, you have very little visibility into what’s actually going wrong. You're flying blind, waiting for the status page to turn yellow.
This creates a new category of unmanaged risk and untracked cost. You're paying for a premium service, but are you getting the performance you paid for? And if you're not, do you have a reliable way to hold your provider accountable?
What You Should Actually Be Doing Right Now
Instead of just approving the use of a new model, it's time to treat AI services as the mission-critical infrastructure they are. Here are four practical steps:
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Isolate and Tag All AI Spend. Before you start building, make sure every dollar spent on Bedrock, and specifically on models like Claude 3.5 Sonnet, is tagged. FinOps teams can't govern what they can't see. This is ground zero for managing the economics of AI.
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Define Your Performance Baselines. Don't rely on AWS's marketing. What are the actual latency and availability requirements for your application? Start measuring performance from an independent, third-party perspective on day one. Know what 'good' looks like before things go bad.
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Treat the SLA as a Contract, Not a Suggestion. Read the Amazon Bedrock SLA. Understand what it promises and, more importantly, what it doesn't. Your financial and operational plans should be based on the contractual reality, not the marketing hype.
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Automate SLA Monitoring and Credit Recovery. Manually tracking performance against an SLA and filing for credits is a losing battle. It's time-consuming, error-prone, and most organizations just don't do it. This process needs to be automated. When the service you're paying for doesn't meet its contractual obligations, you should be credited automatically. It’s not a bonus; it’s what you’re owed.
The Missing Operational Layer
This is precisely the gap Next Signal was built to fill. We provide the independent, automated operational layer for managing your critical cloud services. We don't just look at the status page; we continuously monitor the performance of services like Amazon Bedrock from the outside, giving you an objective source of truth.
When a service fails to meet its SLA, we automatically document the breach and manage the credit recovery process on your behalf. We turn SLA management from a reactive, manual chore into a proactive, automated part of your cloud operations.
For new, powerful services like Claude 3.5 Sonnet, this isn't just nice to have. It's essential for ensuring the economics and resilience of your AI strategy are built on a solid foundation.
The pace of AI innovation isn't slowing down. But the principles of sound operational and financial management haven't changed. The new models are exciting, but they need to be managed with the same discipline as any other piece of critical infrastructure.
If you're ready to close the gap between what your cloud provider promises and what you actually receive, check out the ROI calculator on our site or get started today.