The rapid proliferation of large language models (LLMs) in the enterprise has catalyzed a new wave of ambition among engineering leaders. As businesses race to adopt generative AI solutions, many are investing in advanced architectures and capabilities without first addressing the fundamental requirement for success: observability.
Observability, long considered a DevOps concern, now sits at the core of AI-driven product development. Without a clear and continuous view into model behavior, output variability, and system performance, teams are effectively engineering in the dark. The implications for organizations at the early and mid-stages of AI maturity are substantial.
Enterprises deploying LLMs today often begin with simple prompt engineering or retrieval augmented generation (RAG) use cases. These starting points are deceptively straightforward but can quickly evolve into complex, multi-agent workflows. At each stage, the ability to trace prompts, responses, context, and grounding sources becomes non-negotiable. Lacking this, experiments devolve into guesswork, and debugging becomes prohibitively expensive.
More critically, as non-ML-native teams increasingly engage with LLMs, observability functions must shift left. Product teams, application developers, and infrastructure engineers now require real-time visibility into the inference pipeline. This includes tracing how an LLM reached its conclusion, understanding which context windows were activated, and validating whether outputs were grounded in legitimate sources.
Both Google Cloud and Datadog have articulated this new paradigm clearly. In their recent fireside discussion, Dr. Ali Arsanjani (Director of AI at Google) and Sajid Mehmood (VP of Engineering at Datadog) emphasized that evaluation, traceability, and lifecycle observability are foundational to operationalizing GenAI. Their insights are captured in the white paper, The Future of AI, LLMs, and Observability on Google Cloud: 7 Key Insights for Leaders.
Observability tooling must evolve alongside the model lifecycle. Simple dashboards and latency metrics are no longer sufficient. Organizations must invest in full-stack platforms that monitor compute utilization (including GPU and TPU nodes), track inference chains, and expose model-level quality indicators. This is particularly important as enterprises move beyond zero-shot use cases and begin incorporating custom tuning, vector databases, and reinforcement learning from human feedback (RLHF).
To illustrate, consider the following technical implementations that support a mature observability strategy:
LLM Chain Tracing and Telemetry Correlation: Using platforms such as Datadog's LLM Observability suite, organizations can instrument each step in a prompt-to-response chain, including the context source, prompt structure, inference time, and output verification. This allows teams to identify latency bottlenecks and diagnose output hallucinations with clarity.
GPU and TPU Utilization Monitoring: Implementing fine-grained monitoring for hardware acceleration layers ensures optimal usage of AI compute resources. Datadog provides dashboards that correlate GPU workload distribution with model performance metrics, enabling cost-performance tuning during training and inference stages.
Grounding Source Validation and UX Transparency: Tools that trace the origin of retrieved documents in RAG pipelines and display this information alongside outputs improve user trust. For instance, enabling click-through access to source documents within application interfaces mirrors human explanation standards and reinforces output reliability.
The takeaway for technical leaders is clear: do not treat observability as an afterthought. It is not merely a compliance requirement or a debugging tool. It is a strategic enabler that determines whether your GenAI investments scale successfully or stall prematurely.
As a Datadog partner, we at Forte Group are seeing firsthand that the organizations best positioned to succeed with LLMs are those who embed observability from the outset. They establish a culture of continuous evaluation, equip teams with the right tools, and design systems where transparency is the default, not the exception.
For mid-market and private equity-backed companies where execution speed is critical, this may be the most important insight of all. The difference between experimentation and production is not just deployment. It is observability.