Generative AI, and especially Large Language Models (LLMs), are no longer merely academic exercises: they are now a mission-critical infrastructure. These models are redefining user interaction and automating internal business processes, offering an unprecedented competitive advantage. However, the integration of LLMs, often in complex architectures such as Retrieval-Augmented Generation (RAG), exposes development teams to a new and profound operational challenge.
The question arises: how can we ensure that these AI applications maintain the reliability, performance and economic sustainability required for a customer-facing service or internal workflow? The non-deterministic nature of LLMs, combined with their dependence on an ecosystem of external services, makes traditional monitoring tools insufficient. Attempting to debug, optimize costs or diagnose a hallucination without complete visibility is like navigating in the dark.
The strategic and technological response to this challenge is represented by what is known as LLM Observability. This concept, which goes beyond simple monitoring, is the ability to ask any question of the LLM system in production to understand its status, behaviour and, last but not least, its impact on the business.
At Bitrock, we believe that LLM Observability is not just an option, but an essential step in successfully scaling AI-based digital evolution projects.
In this article, we will analyse the three pillars of Observability and demonstrate how a solution such as Radicalbit‘s AI Gateway can unlock the full operational potential of GenAI applications.
The Limitations of Traditional Monitoring
The adoption of LLMs introduces elements for which classic monitoring systems are not optimised. These include unpredictable costs due to the volatility of token consumption, the management of model hallucinations and deviations that require granular context tracking, and performance degradation resulting from bottlenecks in external services such as APIs or vector databases.
Attempting to track these elements with standard Application Performance Monitoring tools is ineffective, as they are not inherently equipped to interpret unstructured payloads (prompts/completions) or to aggregate specific metrics, such as token consumption rates from different provider APIs.
The Pillars of LLM Observability
To govern this complexity, it is therefore necessary to implement the three pillars of Observability, specifically adapted for AI:
Metrics: Metrics provide the quantitative data needed to define robust service level objectives (SLOs) and for capacity analysis. Key metrics for LLMs go beyond system parameters and include P95/P99 latency of API calls, token consumption rates (input and output), model-specific error rates (e.g., 429 Rate Limit), and prompt success rate (often evaluated using a secondary model). These measurements are essential for quickly identifying anomalies (such as cost spikes) before they impact the user experience.
Tracing: Distributed tracing captures the flow of a single user request through the entire distributed system, especially in RAG architectures. A complete trace must document every span, from the start of the user request to the query to the Vector Database (retrieval time), to the model invocation (inference time), and to the rendering of the final response. Traces answer the ‘why’ of latency or failure by clearly visualising the time spent in each component. This is essential for optimising complex prompt chains and isolating the exact component that degrades performance.
Logging: Logs are granular, immutable records that, for LLMs, must include the complete transaction payloads: the exact user prompt, the completion generated by the model, the hyperparameters used, and any context retrieved from the vector database. Logs are the primary source for post-mortem analysis, content moderation auditing, and data leakage prevention. When a model generates an unsatisfactory response, inspecting the log is the only way to replicate the failure and optimise prompt engineering.
The AI Gateway as a Unified Control Centre for Observability
Implementing the three pillars becomes logistically complex when using multiple models (OpenAI, Anthropic, OSS models) with different API schemes and varying governance requirements.
In this scenario, an AI Gateway such as the one offered by Radicalbit, part of the Fortitude Group Product Portfolio, solves the problem by acting as a centralised control point for all model interactions and all AI traffic. This architecture is crucial for scalability and governance, decoupling the application from the rapidly evolving LLM ecosystem.
Among the features of the AI Gateway, some are particularly strategic from an observability perspective:
Metrics Normalisation and Cost Accounting
The AI Gateway standardises usage metrics by collecting token consumption data from different APIs and transforming it into a single normalised metric stream. This is critical for accurate cost allocation (chargeback), allowing LLM costs to be assigned to specific teams or tenants, and for implementing unified rate limiting across all models and services.
Cohesive Tracing
The Gateway ensures that every LLM call is automatically enriched with essential metadata (model name, version, temperature) and correctly injected into the application’s existing distributed tracing context. By automatically enriching trace data, it eliminates the need for developers to manually instrument each interaction, ensuring complete and consistent performance visibility across all services.
Data Governance and Security in Logging
Centralising logs through the Gateway is crucial for compliance. Before making logs persistent, the Gateway can apply PII Scrubbing (i.e., automatic masking of Personally Identifiable Information) and analyse data in real time to detect prompt injection. This centralisation not only ensures regulatory compliance but also creates a first line of defence against potential model-specific security threats.
Conclusion
LLM Observability, built on the pillars of Metrics, Tracing, and Logging, is key to maintaining governance, controlling costs, and accelerating the model iteration cycle. In this context, a dedicated Gateway is the catalyst that provides the necessary operational maturity, transforming a series of LLM interactions into a manageable, transparent, and scalable system.
Bitrock, as a leading IT consulting firm in enterprise innovation and digital evolution, positions itself not only for initial integration, but for the operational maturity of LLM projects. Our specific expertise is focused on ensuring the governance, security and economic sustainability of AI infrastructure. This includes the design of scalable AI architectures, the integration of standards such as OpenTelemetry and, above all, the implementation of the AI Gateway.
We transform the operational uncertainty associated with Artificial Intelligence into strategic confidence, ensuring that investments made in AI are robust, optimised and ready for enterprise scalability in mission-critical workflows.
Transform your AI project into a manageable business asset, contact us.