Featured work / Energy and cloud infrastructure case study
Elatre engineered LLM observability and AI SRE for an energy and cloud infrastructure program using OpenTelemetry, Datadog LLM Observability, prompt caching, and approval-controlled operations bots. The program reached an 88% cache hit rate, reduced token inference spend by 67%, and cut P1/P2 incident mean time to resolution by 74%, from 52 minutes to 13.5 minutes.
88% cache hits
After prompt-prefix restructuring
67% less spend
Token inference expenditure
52 to 13.5 minutes
P1/P2 incident MTTR
74% lower MTTR
Approval-controlled AI SRE
A model request can appear successful while the broader business workflow is slow, expensive, or unreliable. An agent may repeat a tool call, retrieve excessive context, or spend tokens reconstructing information that has not changed. Conventional application monitoring does not always explain those decisions.
For this energy and cloud infrastructure program, Elatre combined application observability with model-level telemetry and operational automation. The architecture used OpenTelemetry gen_ai.* instrumentation and Datadog LLM Observability to make model activity part of the operating picture. Prompt-prefix restructuring addressed repeated inference work, while approval-controlled AI SRE bots supported incident response.
These were related engineering efforts. Telemetry made execution visible, caching changed the economics of repeated context, and controlled automation helped the operations team respond to incidents. A useful operating model needs all three to remain understandable after deployment.
| Layer | Implementation | Engineering purpose |
|---|---|---|
| Instrumentation | OpenTelemetry with gen_ai.* attributes | Capture structured signals about model activity within application workflows. |
| LLM analysis | Datadog LLM Observability | Investigate model execution alongside operational performance. |
| Inference economics | Restructured prompt prefixes | Increase reuse of stable context through prompt caching. |
| Incident response | Approval-controlled AI SRE bots | Assist investigation and operational actions while retaining approval boundaries. |
| Outcome measurement | Cache, spend, and incident metrics | Connect infrastructure changes with measurable operating results. |
Elatre instrumented the system with OpenTelemetry and generative AI attributes. Structured telemetry gives engineering a common way to describe operations across application components. It helps connect a user-visible workflow to the model activity and dependent services involved in completing it.
For an agent system, a single top-level request can contain several decisions and tool interactions. Treating that sequence as one undifferentiated API call makes investigation difficult. A useful trace preserves the relationship between the parent request, model operations, and downstream work without relying on an analyst to reconstruct the entire sequence from scattered logs.
Instrumentation also needs a deliberate data-handling policy. Request metadata can support diagnosis without requiring every prompt, retrieved document, or response to be stored in full. The right collection boundary depends on the operational question and the sensitivity of the underlying information.
Our LLM observability, agent monitoring, and AI SRE services connect this instrumentation layer to the engineering team's investigation and operating workflows.
The program used Datadog LLM Observability alongside OpenTelemetry instrumentation. The objective was to make model execution inspectable in the context of the application, rather than separating AI performance from the systems that users depend on.
Useful investigation starts with a concrete question. Was the workflow slow because inference took longer, because a dependency was delayed, or because the agent repeated work? Did spend increase because usage grew, because prompts became larger, or because caching stopped being effective? These questions require connected evidence rather than isolated dashboard totals.
The distinction between model quality and system availability also matters. A request can complete within its latency target and still return an unsuitable result. Operational monitoring therefore needs to work with evaluation and release controls, not replace them.
Elatre's AI agent platform migration and LLM evaluation services provide the complementary work needed to assess behavior when prompts, models, tools, or orchestration change.
Elatre restructured prompt prefixes and achieved an 88% cache hit rate. The design principle is straightforward: stable context should remain stable, while request-specific content should sit in the appropriate variable portion of the prompt. Reordering shared instructions or injecting changing values into an otherwise reusable prefix can interfere with cache reuse.
This is an application design issue as well as a model-provider feature. Teams need to understand how their prompts are assembled, which sections change between requests, and when a shared context revision should intentionally invalidate reuse. Treating prompt construction as an unmanaged string makes those decisions harder to inspect.
Different models and providers can apply different caching rules. The engineering task is to implement prefix reuse against the selected platform's actual behavior, then measure the result. A cache hit rate and a financial saving describe different things, so both belong in the operating review.
The program reduced token inference spend by 67%. That outcome is separate from the 88% cache hit rate. A cache hit does not imply that the entire request is free, and a hit-rate percentage should not be presented as an equivalent percentage reduction in every cloud cost.
For this case study, the measured economic outcome concerns token inference spend. That makes the result precise enough to guide an enterprise discussion about AI operating costs without extending it to unrelated infrastructure, licensing, or staffing expenditure.
Our AI cost optimization services examine the relationship between workload behavior, prompt construction, model choices, and cost measurement. The practical objective is to reduce waste while preserving the behavior the application needs.
Elatre deployed approval-controlled AI SRE bots as part of the incident-response architecture. In this design, automation can assist with investigation and prepare a proposed response, while consequential actions remain subject to the defined approval process.
The boundary is especially important in infrastructure operations. Reading telemetry, explaining a failure, and changing production state are different privileges. An operations assistant should not inherit broad authority simply because it can interpret an alert.
A useful action proposal identifies the affected system, the reason for the action, the intended change, and the evidence the reviewer needs. The approval mechanism should apply to the actual operation being executed, rather than an informal statement elsewhere in a conversation.
The program reduced P1/P2 incident mean time to resolution from 52 minutes to 13.5 minutes, a 74% reduction. The bots remained approval-controlled; the outcome does not require describing production operations as unrestricted autonomous execution.
| Metric | Program result | Measurement scope |
|---|---|---|
| Prompt cache hit rate | 88% | Reuse achieved after prompt-prefix restructuring. |
| Token inference spend | 67% reduction | Inference-token expenditure. |
| P1/P2 incident MTTR | 52 minutes to 13.5 minutes | Mean time to resolution for the reported incident classes. |
| MTTR improvement | 74% reduction | Change in the reported mean resolution time. |
- Trace the full workflow. Connect model operations with application requests and dependent services.
- Define telemetry boundaries. Collect useful evidence while applying the appropriate content-handling policy.
- Separate stable and changing context. Make prompt-prefix reuse an explicit design decision.
- Measure savings directly. Track inference spend independently from cache hit rate.
- Restrict operational actions. Separate observation, recommendations, approvals, and execution.
- Evaluate changes. Review quality and behavior alongside latency, cost, and reliability.
The value of an AI platform depends on how well the organization can understand and operate it. Elatre brings observability, inference economics, evaluation, and controlled incident automation into one engineering program. For an enterprise reviewing a comparable system, that work begins with the current workflow, the available telemetry, and the decisions the operations team needs to make.
Tell us which systems, workflows, and operating results your team needs to improve. We can review the architecture and define a practical implementation scope.