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Dynatrace Davis AI

4.6

Free

Users highly value the deterministic root-cause analysis which significantly reduces MTTR, though some note the pricing model can be complex to predict for high-volume log environments.

The industry's only hypermodal AI combining causal, predictive, and generative intelligence for unified observability.

DevelopmentFree pricingAPI availableUpdated 2026-03-17

Best fits

Automated Root Cause AnalysisPredictive Capacity Planning

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Dynatrace Davis AI interface

About Dynatrace Davis AI

Dynatrace Davis AI represents a paradigm shift in AIOps through its 'hypermodal' architecture, integrating causal, predictive, and generative AI models into a unified platform. Unlike traditional probabilistic AI that relies on correlation, Davis AI utilizes Causal AI through its 'Smartscape' topology—a real-time map of all entity dependencies across the full stack. This allows it to provide deterministic root-cause analysis with zero false positives. By 2026, Davis AI has expanded its footprint into the 'Grail' data lakehouse, enabling Davis Co-Pilot to perform complex natural language queries across petabytes of logs and metrics. Its predictive engine utilizes sophisticated time-series forecasting to anticipate infrastructure bottlenecks before they impact end-users, while the AutomationEngine orchestrates self-healing workflows based on Davis's findings. This technical synergy positions Dynatrace as a leader for enterprises managing hyper-complex microservices and multi-cloud environments, where manual troubleshooting is no longer viable. The platform is designed for zero-configuration, automatically discovering and instrumenting environments via OneAgent, ensuring that the AI has a high-fidelity data stream for both security and performance analytics.

Core Capabilities

Dynatrace Davis AI represents a paradigm shift in AIOps through its 'hypermodal' architecture, integrating causal, predictive, and generative AI models into a unified platform.

Main Tasks

Use Cases

Rapid Incident Response

Alert storms during a microservices outage making it impossible to find the root cause.

VIEW EXECUTION STEPS

1.

Davis AI detects a drop in success rate for a critical checkout service.

2.

Davis follows the Smartscape topology to a downstream database node.

3.

The engine identifies a specific SQL query causing high CPU on that node.

4.

Davis consolidates 500+ alerts into a single problem ticket with the database as the root.

Automated Capacity Management

Over-provisioning cloud resources leading to high costs.

VIEW EXECUTION STEPS

1.

Davis Predictive AI analyzes seasonal traffic patterns over the last 90 days.

2.

It identifies clusters that are 40% underutilized during off-peak hours.

3.

Davis suggests an automated scaling policy change via AutomationEngine.

4.

Infrastructure is automatically rightsized, saving 25% in cloud spend.

GenAI-Driven Troubleshooting

Junior SREs struggling to write complex queries to analyze log patterns.

VIEW EXECUTION STEPS

1.

SRE asks Davis Co-Pilot: 'Why did the payment gateway latency spike at 3 PM?'

2.

Co-Pilot translates this to a DQL query against Grail.

3.

Co-Pilot analyzes the logs and returns a summary: 'A deployment at 2:58 PM introduced a thread-lock issue.'

4.

The SRE initiates a rollback based on the natural language advice.

Security Vulnerability Triage

Security teams overwhelmed by 10,000+ CVEs with no context.

VIEW EXECUTION STEPS

1.

Dynatrace Application Security detects a Log4j vulnerability.

2.

Davis AI checks the runtime context to see if the vulnerable function is actually being called.

3.

Davis checks if the server is exposed to the public internet.

4.

The vulnerability is prioritized as 'Critical' only if it is active and exposed.

Cloud Migration Validation

Ensuring performance parity when moving from on-prem to AWS.

VIEW EXECUTION STEPS

1.

Baseline performance is captured on-prem using OneAgent.

2.

Traffic is mirrored to the AWS environment.

3.

Davis AI compares response times and error rates across both environments in real-time.

4.

Migration is validated once Davis confirms 0% performance regression.

Business Impact Analysis

IT issues occurring without knowing if they affect revenue.

VIEW EXECUTION STEPS

1.

Dynatrace tracks session data and cart conversion rates.

2.

An infrastructure failure occurs in the 'Shipping' service.

3.

Davis AI calculates exactly how many 'Checkouts' failed and the USD value lost.

4.

The issue is prioritized for fix based on the $50k/hour loss.

Automated Remediation (Self-Healing)

Repetitive manual tasks for clearing disk space or restarting crashed services.

VIEW EXECUTION STEPS

1.

Davis AI identifies a 'Disk Full' event on a production server.

2.

AutomationEngine triggers a workflow to clear temporary logs.

3.

If disk space remains high, it triggers a script to expand the EBS volume.

4.

The incident is closed without human intervention.