Dynatrace announced extended capabilities for the observability of customers’ GenAI initiatives.
These advancements provide teams with access to comprehensive insights into their AI applications to drive reliability, performance, security and compliance. With this visibility, organizations now have clarity into their AI initiatives and can understand their return on investment (ROI).
Dynatrace is introducing a series of platform advancements, including:
- Enhanced LLM Model Analytics: In addition to monitoring standard KPIs such as input and output errors, response times, and token consumption, the predictive capabilities of Dynatrace Davis AI® detect changes in usage behavior to predict and forecast cost changes associated with LLM usage. This helps teams understand model performance and optimization opportunities, including how they can better manage costs and control ROI.
- LLM Input and Output Guardrails: Dynatrace safeguards the quality of AI application input and output to help build trust in AI. This enables customers to recognize model hallucinations, identify attempts at LLM misuse such as malicious prompt injection, prevent Personally Identifiable Information (PII) leakage, and detect toxic language.
- Multi-model Tracing: Dynatrace maps dependencies between multiple LLMs that work in concert with Retrieval Augmented Generation (RAG) pipelines or agentic frameworks to provide end-to-end observability of the entire system, not just the component parts. This gives teams the insight to verify that dependencies are interacting seamlessly so they can deliver optimal end-user experiences.
- Responsible AI Integrations: Dynatrace helps organizations with AI governance by tracking every input and output without sampling to provide an audit trail of monitoring and observability, including documenting what training data was used for a given model. Through Dynatrace Grail™, all data can be queried in real time and stored for future reference.
“We see a large portion of our global customer base moving their AI applications into production. AI Observability is key for ROI, governance, and explainability,” said Alois Reitbauer, Chief Technology Strategist at Dynatrace. “Dynatrace delivers AI-powered observability with real-time insights which enables data and systems to work together effortlessly. At Perform 2025, we’re excited to showcase how we’re leveraging these capabilities to power new possibilities for our customers, highlighting the transformative innovation they’re driving through the ability to effectively understand and optimize their AI deployments.”
Dynatrace supports customers now with its Observability for AI solutions.
The Latest
Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...
Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...
Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...
Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...
Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...
Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...
If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...
In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...