When customers started telling us about their AI initiatives last year, we noticed something interesting. They'd built impressive LLM-powered applications, but their observability setup looked fragmented. Traditional APM for their APIs and infrastructure. Separate AI monitoring tools for LLM traces and token usage. Manual correlation when things broke.

It reminded us of observability a decade ago: separate tools for metrics, logs, and traces. Different dashboards. Different query languages. Engineers jumping between systems trying to understand what happened. We solved that problem once with unified observability. We weren't about to let it happen again with AI workloads.

Why AI Workloads Aren't Special

Here's what we realized: an LLM-powered API is fundamentally still an API. It receives HTTP requests. It executes business logic that happens to include LLM calls. It queries databases that happen to be vector databases. It returns responses.

The LLM call is one span in a distributed trace, not a separate universe of telemetry that needs its own monitoring stack.

When we looked at how separate AI monitoring tools worked, we saw the same operational silos we'd eliminated years ago. Different access controls. Different rate limits. Different pricing models. Different query interfaces. Worse, when an LLM-powered API was slow, you couldn't easily tell if the problem was the LLM, the vector database, the infrastructure, or something else entirely.

Why should AI workloads be second-class citizens in observability?

Extending APM, Not Creating a New Product

Our approach was straightforward: extend our existing APM capabilities to natively support LLM traces. Not a separate product line. Not a separate data store. Just APM that understands AI workloads.

We built on OpenTelemetry's semantic conventions for AI instrumentation. LLM calls generate spans with prompt and completion text captured as events, token usage as attributes, and model metadata included. Framework integrations for LangChain and LlamaIndex automatically generate structured traces for chains, agents, and RAG pipelines.

All of this flows into the same unified observability data lake as traditional APM traces. Same OLAP engine handling high-cardinality attributes like prompt variations and model versions. Same query languages. Same visualizations. Same alerting.

The result? When you look at a slow API request, you see the complete picture. The HTTP request took 2 seconds. The LLM span took 1.8 seconds of that. The prompt was 1500 tokens to GPT-4. And the logs show "OpenAI API returned 429 Too Many Requests."

Root cause identified in one view: rate limit exhaustion, not model performance.

Same Operational Controls

Native AI monitoring meant giving AI workloads identical operational controls to traditional services.

Rate limiting applies to LLM traces the same way it applies to HTTP traces. Set stream-specific ingestion limits. Prioritize production over development. Apply filter-based rules. When a development environment starts flooding LLM traces, throttle just that stream without affecting production monitoring.

Access control uses the same stream-specific RBAC. Control who views LLM traces and prompts. Restrict access to sensitive prompt data. Apply the same team-based and environment-based permissions. Platform teams don't learn new security models.

Cost tracking includes LLM telemetry automatically. Track data volumes by team, service, and environment. Enable chargeback models that attribute observability costs to the teams generating them. Same consumption dashboards, same accountability.

Query languages work across both traditional and AI telemetry. Use TraceQL to find slow LLM traces. Use PromQL to aggregate token usage metrics. Use FuseQL to search LLM errors. Engineers don't context-switch between different query interfaces.

Token Usage as Standard Metrics

Token usage appears as Prometheus-compatible metrics with standard naming conventions: llm_tokens_prompt_total, llm_tokens_completion_total, llm_request_duration_seconds. Dimensions include service, model, provider, and environment.

This enables standard observability workflows. Build dashboards showing token trends over time. Alert on usage spikes that might indicate prompt bloat. Correlate token costs with application features. Forecast capacity needs based on historical patterns.

More importantly, these metrics live alongside your infrastructure metrics. When token usage spikes, you can immediately see if it correlates with increased traffic, a deployment change, or a specific customer segment.

Multi-Provider Support

Kloudfuse 3.5 supports LLM observability across major providers: OpenAI, Anthropic, Google, AWS, and Azure. Track token usage across prompt and completion, monitor error rates from both API failures and model-level issues, and correlate performance across different LLM providers.

This multi-provider approach matters because many organizations use different models for different use cases. You might use GPT-4 for complex reasoning, Claude for long-context tasks, and local models for sensitive data. Native monitoring means you can compare performance and costs across providers using the same metrics and dashboards.

What We Learned

Building native AI monitoring reinforced something important: AI workloads aren't fundamentally different from traditional workloads. They run in the same Kubernetes clusters. They use databases with some new characteristics. They serve HTTP APIs. They have the same operational requirements for rate limiting, access control, and cost management.

Treating AI monitoring as a separate silo creates unnecessary operational complexity. Teams shouldn't need new tools, new query languages, or new workflows just because they added an LLM call to their API.

Native AI monitoring in Kloudfuse 3.5 eliminates these silos. AI workloads are first-class citizens, receiving the same operational excellence as traditional services. That's the difference between bolting on AI features and building AI-native observability.

Learn more about LLM observability in Kloudfuse 3.5 in our launch announcement.