Dynatrace

What Dynatrace does

Dynatrace keeps today's AI-driven world working by advancing observability for digital businesses. It provides a holistic view of entire digital ecosystems, removing the burden of operational monitoring and alert management. The platform turns data into decisions and autonomous actions, preventing problems, automating workflows, and delivering better, more secure software faster. Dynatrace offers best-in-class observability for Generative AI applications, LLMs, and agents, along with application, infrastructure, business, and digital experience observability, log analytics, software delivery, and threat observability. It unifies and contextually analyzes data in its data lakehouse, Grail, and drives productivity and resilience with agentic operations.

Products

• AI ObservabilityMonitors, optimizes, and secures Generative AI applications, LLMs, and agentic workflows, improving performance, explainability, and compliance.
• Application ObservabilityProvides APM, distributed tracing, and profiling for cloud-native and enterprise stacks, ensuring optimal service performance and SLOs.
• Application SecurityAutomatically detects, analyzes, and remediates runtime application vulnerabilities and attacks in real time.
• Business ObservabilitySimplifies critical, real-time business decisions with precision, speed, and context, tracking KPIs and optimizing processes.
• Digital ExperienceDelivers flawless digital experiences with real-user and synthetic monitoring and session replays.
• Infrastructure ObservabilityProvides end-to-end infrastructure observability for modern multi-cloud environments.
• Log AnalyticsDrives intelligent and intuitive analytics from log data, from troubleshooting to business processes.
• Software DeliveryDrives intelligent cloud ecosystem automations with observability and security insights.
• Threat ObservabilityOffers advanced threat protection, automated response, and forensics for unmatched protection.
• Dynatrace IntelligenceAn agentic operations system that leverages deterministic insights to take action in real-time, enabling predictions, anomaly detection, and causal root cause analysis.
• GrailThe industry's most powerful data lakehouse that unifies and contextually analyzes all data with massively parallel processing (MPP), fast, indexless, schema-on-read storage.
• SmartscapeAutomatically identifies and maps interactions and relationships between applications and underlying infrastructure in real time.
• OpenPipelineAutomatically captures and pre-processes data from any source, including open source datasets, and uses high-performance stream processing for enrichment and contextualization.
• OneAgentDeploys once on a host to instantly and continuously collect all relevant metrics along the full application-delivery chain.
• PurePathCaptures and analyzes timing and code-level context for all distributed traces, end-to-end, across the full stack.
• AppEngineEnables users to create and share custom apps that leverage insights from all observability, security, and business data.

Key capabilities

• AI-powered observability
• Agentic AI initiatives
• Unified data and real-time context
• Deterministic insights
• Automated workflows
• Problem prevention
• Real-time vulnerability detection and remediation
• End-to-end observability for AI, LLMs, and agentic workflows
• Integration with top AI platforms (Amazon Bedrock, Azure Foundry, LangChain, NVIDIA NIM, OpenAI, Google Vertex)
• Monitoring of token usage, cost, stability, latency, invocation errors for models
• Guardrail monitoring for LLM input and output
• Detection of model hallucinations and malicious prompt injection
• PII leakage prevention and toxic language detection
• End-to-end tracing, logging, and dependency mapping for AI services
• Intelligent detection for root cause analysis in LLM chains
• AI Evaluations / LLM-as-a-judge for answer quality
• Model drift detection
• Continuous production profiling
• Code-level visibility
• Real-time debugging
• Automated root cause analysis
• Complete visibility across the stack
• Real-time performance intelligence
• Automated observability for cloud-native workloads
• Real-time topology discovery
• Intelligent baselines
• Health and availability tracking
• Version and deployment analytics
• Interactive exploratory analytics
• Database performance optimization
• SQL statement execution analysis
• Continuous runtime exposure monitoring
• Automated risk reprioritization
• Runtime application protection
• Zero-day attack protection
• DevSecOps integration
• Real-time business decision support
• Business KPI tracking
• Process anomaly detection
• Cloud cost and carbon footprint optimization
• Compliance management integration

Use cases

• Monitoring, optimizing, and securing Generative AI applications, LLMs, and agentic workflows
• Improving performance, explainability, and compliance of AI systems
• Tracking business impact of AI (productivity gains, support ticket deflection, ROI)
• Tracing end-user experience, availability, and reliability of AI-powered applications
• Monitoring orchestration layer performance, guardrails, and prompt caching
• Observing agent protocols, command execution, tool usage, and multi-agent communications
• Assessing model integrity (token usage, cost, stability, latency, errors)
• Monitoring RAG pipelines, data volume, distribution, and retrieval patterns
• Tracking utilization, saturation, and errors across GPUs, TPUs, and compute resources
• Reducing cost and improving performance of AI/LLM stack
• Managing costs by predicting increases and proactively making changes
• Reducing AI agent and LLM response times and improving reliability
• Comparing different AI model performance with A/B testing
• Monitoring token costs, tool behavior, and reliability across AI coding agents
• Safeguarding quality of AI applications by monitoring guardrail metrics
• Mitigating potential biases, errors, and misuse of AI systems
• Preventing PII leakage and detecting toxic language
• Analyzing effectiveness of LLM guardrails
• Gaining end-to-end visibility into execution of user requests
• Logging, tracing, and mapping dependencies between services
• Accelerating resolution of errors and failures in LLM chains
• Continuously measuring accuracy, relevance, and grounding of AI agent/LLM outputs
• Detecting model drift, safety, and quality issues with evaluations
• Ensuring optimal service performance and SLOs for applications
• Innovating faster and resolving issues instantly in application development
• Preventing downtime and reducing MTTR for applications
• Pinpointing and resolving issues at the source with AI-powered root cause analysis
• Monitoring cloud-native workloads and microservices
• Maintaining reliability and meeting performance goals in fast-changing environments
• Improving team collaboration and eliminating war rooms
• Optimizing database performance and application dependencies
• Improving developer productivity
• Optimizing application efficiency with code-level insights
• Troubleshooting and debugging code live in production
• Detecting, analyzing, and remediating runtime application vulnerabilities and attacks
• Enabling developers to ship faster with security embedded by design
• Protecting uptime and performance with real-time security intelligence
• Unifying security teams and accelerating response
• Lowering regulatory compliance risk
• Minimizing vulnerabilities in runtime
• Understanding impact of zero-day vulnerabilities
• Detecting third-party and code-level vulnerabilities
• Accelerating focused remediation by pinpointing root causes
• Precisely determining malicious activity and blocking it
• Protecting against zero-day attacks
• Enabling DevSecOps at scale to ship secure code fast
• Validating that no new critical vulnerabilities were deployed
• Automating creation, assignment, and resolution of vulnerability tickets
• Investigating underlying service issues
• Making critical, real-time business decisions
• Understanding and reporting on business health
• Tracking business metrics, KPIs, and SLOs
• Prioritizing improvements and automating remediation based on business impact
• Tracking, analyzing, and optimizing business processes
• Accelerating sustainability and reducing cloud costs
• Integrating compliance into IT operations
• Detecting major compliance incidents in real time
• Improving customer service and satisfaction (Next)
• Mitigating risk of revenue loss during innovation (Next)
• Accelerating UK and international growth (Next)
• Strengthening resilience of security systems (ADT)
• Unifying logs and telemetry (ADT)
• Optimizing cloud migration (Air France-KLM)
• Accelerated root-cause identification (Air France-KLM)

AI approach

Dynatrace leverages deterministic AI and AI agents to provide AI-powered observability, security, and business insights. It focuses on preventing problems, automating workflows, and delivering better software faster. The platform uses a unique combination of AIs (predictive, causal, generative) to turn data into decisions and autonomous actions, offering capabilities like anomaly detection, causal root cause analysis, and AI model performance comparison. It also provides AI Observability specifically for Generative AI applications, LLMs, and agentic workflows, monitoring performance, explainability, and compliance.

Tech named: AI-powered observability, Deterministic AI, AI agents, Predictive AI, Causal AI, Generative AI, Anomaly detection, Causal root cause, AI Observability, Generative AI applications, LLMs, Agentic workflows, Intelligent detection, A/B testing insights, LLM guardrails, LLM-as-a-judge, Model drift, Toxicity detection, AIOps

Industries served

• Software Development
• Travel
• Security & Protection Services
• Retail & Ecommerce

What it says sets it apart

• AI-powered observability leader
• Agentic AI capabilities for coordinated actions
• Deterministic AI and AI agents
• Unified platform for all data, all teams
• Grail data lakehouse: industry's most powerful, causal, massively parallel processing (MPP), indexless, schema-on-read storage
• Smartscape: automatic real-time environment visualization and mapping
• OpenPipeline: automatic data capture, pre-processing, enrichment, and contextualization
• OneAgent: single deployment for full application-delivery chain metrics
• PurePath: code-level context for distributed traces
• AppEngine for custom app creation
• Clear, simple, and flexible pricing with single commit and scalable volume discount
• No penalties for exceeding commit
• Real-time, contextual data to fuel trustworthy AI
• Best-in-class observability for Generative AI applications, LLMs, and agents
• Seamless integration across the full AI application stack with native support for top AI platforms
• Intelligent detection to identify changes in user behavior and predict cost increases
• A/B testing insights for AI model performance comparison
• Unified observability layer for AI coding agents
• Continuous production profiling
• Live debugging in production
• Automated root cause analysis for E2E transactions
• Code-level profiling
• Kubernetes Platform Monitoring
• OpenTelemetry metrics and traces support
• Runtime security embedded directly into application observability without separate agents or tools
• Precise, contextualized risk insights that reduce false positives and security tickets
• Unified view of security, performance, and SLAs/SLOs
• Actionable guidance for developers and operators for faster remediation
• Lower compliance risk through continuous monitoring and audit-ready evidence
• Ability to track every user session for digital experience monitoring (Next)
• AI and prediction capabilities (Air France-KLM)
• Ability to unify logs, traces, metrics, and events into one observability platform (ADT)