Adding AWS Bedrock Support to LLM Observability

LLMs are showing up in every part of the stack now. They power chat interfaces, internal copilots, and a growing list of developer tools. With all of that, you would expect teams to have a clear picture of what their LLMs are doing in production. The reality is that most of the time they do not.

When latency creeps up, cost spikes, or output quality starts to drift, the first question anyone asks is why. Most teams cannot answer that fast enough because they cannot see what is happening under the hood. LLMs behave like a black box and the monitoring tools developers rely on were not built for this part of the stack.

That gap is exactly what we set out to solve with LLM observability at groundcover. And with support for AWS Bedrock added on top of OpenAI and Anthropic, teams now get the same level of visibility no matter which provider they use.

What LLM observability actually gives you

As companies bring AI features into production, they need basic answers. How many requests are going out. What the latency looks like. Whether you are running into rate limits. Whether the prompt changed. Whether responses started to degrade.

Until now, none of that was easy to see. LLMs sit outside your environment. You send a request, you wait, you get something back. That is not enough for developers who need to understand and debug real systems.

LLM observability gives teams visibility into:

• Request volume and latency
• Errors, retries, rate limits
• Prompt and response bodies
• Token usage on input and output
• Cost per request, model, and service

For Bedrock specifically, we now surface this same data for any model accessed through its unified API, whether it comes from Meta, Anthropic, Cohere, or another provider. Since Bedrock keeps everything inside your AWS instance, this also fits naturally with teams that are already strict about data residency and security.

Why eBPF makes this work without any setup

groundcover uses eBPF as the backbone for the entire observability platform. Because the sensor runs at the kernel level, it sees every piece of traffic going out of your environment. That includes all calls to Bedrock, OpenAI, Anthropic, and anything else you are using.

The value for developers is simple:

• No instrumentation
• No SDKs
• No missing services because someone forgot to wrap a call
• No blind spots

Teams adopting LLMs often do not yet have the same maturity they have built up around their traditional monitoring stack. eBPF gives them full coverage even before they get there.

Cost is a real problem if you cannot see it

With LLMs, cost problems show up fast. A small prompt change can double token usage. A new flow can silently increase traffic. A rollout can accidentally hit a high end model instead of a cheaper one.

Using eBPF data, we track tokens and translate them into real cost at the request level. From there, we can roll it up by provider, model, or service that is making the call. This makes it easy to set alerts and catch issues before you burn through your budget.

For Bedrock, this is important because it offers both pay as you go pricing and provisioned throughput. Teams need to understand how traffic patterns change over time so they can pick the right setup.

Why having everything in one place matters

Developers do not want another dashboard. They want to understand what is happening across the entire flow.

The reason we built LLM observability into the same view as CPU, memory, network, tracing, frontend monitoring, and logs is because the problems are connected. Model latency might not be the root cause. A service might be slow. A database might be backed up. An API might be throwing errors before the LLM even runs.

Seeing everything together helps teams figure out where the real issue is instead of guessing.

A real example from a customer

We already saw a case where a customer shipped a change that increased LLM call frequency far more than expected. Cost went up quickly and the LLM began rate limiting. This resulted in their end users experiencing failures and having a broken experience.

Because everything was visible in one place, they caught it fast and fixed it before it turned into a full outage. That speed is the whole point. Things will break. Teams just need to see the problem early enough to act.

Security and data safety

Since groundcover sees full prompt and response bodies, we can help flag PII and other sensitive content going out to providers. This is valuable for any team using LLMs, but it is especially important now that Bedrock lets enterprises keep traffic inside their own AWS account. Combined with our bring your own cloud model, it means sensitive data stays where it should.

Where this is going

The AI space moves quickly. What feels new becomes a baseline expectation in a matter of months. Quality scoring and LLM based evaluation will improve. Tooling will mature. The main thing that will not change is the need for visibility. Teams cannot operate AI systems in production without knowing what is happening.

LLM observability should be a standard part of every deployment, not something you add only after something goes wrong.

What teams should monitor from day one

For teams just starting to adopt LLMs, the most important thing is to experiment while keeping the complexity manageable. Build step by step so each piece adds real value.

On the observability side, just turn it on early. It is simple enough with groundcover and you will not understand how valuable it is until you see the data flowing. If you are moving fast, the last thing you want is to be blind.