Skip to main content
Freemium
Dynatrace logo
freemium

Dynatrace

Verified 17 hours ago
Compare Alternatives Claim this listing
About Dynatrace 18s read
1 min read

Dynatrace is an enterprise AI observability and security platform known for its Davis AI engine, which automatically detects anomalies, determines root causes, and prioritises problems - eliminating manual alert triage. It provides full-stack observability across cloud, microservices, and Kubernetes environments, and has expanded into application security and AIOps with its unified Grail data platform.

Best for: Enterprise DevOps and SRE teams needing AI-driven full-stack observability with automatic root cause analysis across complex cloud environments
Global availability
Screenshot
Key Features
  • Davis AI automated root cause analysis
  • Full-stack observability (APM, infra, logs)
  • Kubernetes & cloud-native monitoring
  • Application security & runtime protection
  • Grail unified data platform
Pros & Cons
What we like
Davis AI provides causation, not just correlation
Auto-instrumentation reduces setup effort dramatically
Leading enterprise APM and AIOps platform
What to consider
Higher cost than open-source alternatives
Can be overwhelming for small teams
Questions and Answers

No questions yet. Be the first to ask!

Log in to ask a question.

User Reviews

No reviews yet. Be the first to review Dynatrace!

Log in to leave a review.

You might also like
Google AI Studio G
Google AI Studio
freemium
Appsmith A
Appsmith
freemium
Agno A
Agno
free
User Reviews

No reviews yet. Be the first to leave a review!

Log in to leave a review.

Was this page helpful?

AI Glossary

The simulation of human intelligence in machines programmed to think, learn, and problem-solve. AI encompasses machine learning, natural language processing, computer vision, and more.

A deep learning model trained on vast text datasets to understand and generate human-like language. Examples include GPT-4, Claude, and Gemini.

AI systems that create new content - text, images, audio, video, or code - based on patterns learned during training rather than retrieving existing data.

The practice of designing and refining input text (prompts) to guide an AI model toward producing more accurate, relevant, or creative outputs.

A computing architecture inspired by the human brain, consisting of interconnected layers of nodes that learn to recognise patterns in data.

A branch of AI where algorithms improve automatically through experience and exposure to data, without being explicitly programmed for each task.

AI technology that enables computers to understand, interpret, and generate human language - the foundation of chatbots, translation tools, and voice assistants.

The process of further training a pre-trained AI model on a specific, smaller dataset to specialise it for a particular task or domain.

When an AI model generates plausible-sounding but factually incorrect or entirely fabricated information, often presenting it with false confidence.

The units of text (roughly 4 characters or ¾ of a word in English) that LLMs process. Model costs and context limits are measured in tokens.

The maximum amount of text (measured in tokens) an LLM can process in a single interaction - both input and output combined.

A technique that enhances LLM outputs by fetching relevant external documents at query time, grounding responses in up-to-date or proprietary data.

Numerical vector representations of text, images, or other data that capture semantic meaning, enabling AI to measure similarity and retrieve relevant content.

The process of running a trained AI model to produce predictions or outputs from new input data - distinct from the training phase.

An AI system that autonomously plans and executes multi-step tasks by combining reasoning, tool use (web search, code execution, APIs), and memory.

AI models that can process and generate multiple types of data - such as text, images, audio, and video - within a single unified system.

A large-scale AI model trained on broad data that serves as a base for many downstream applications via fine-tuning or prompting.

A pricing model where core features are available for free, with premium features, higher usage limits, or advanced capabilities offered via paid plans.

A set of protocols that allows developers to integrate an AI tool's capabilities directly into their own applications and workflows.

Zero-shot means the model handles a task without any examples; few-shot means it is given a small number of examples in the prompt to guide its response.
Trending Now
Claude AI Learning Brain OpenAI Operator
Tool Info
Verified 17 hours ago
Pricing freemium
Added Jun 02, 2026
Source Manual Entry
Visit Website Back to Tools See Alternatives
Share:
Pricing Notes

AI tool pricing changes frequently. The pricing shown was accurate at our last editorial review. We recommend checking the tool's website directly for current plans.

Last reviewed: Oct 20, 2026
Check Current Pricing
Related Tools
Google AI Studio freemium Appsmith freemium Agno free Mistral AI freemium
Compare

See how Dynatrace stacks up against alternatives.

vs Google AI Studio vs Appsmith vs Agno
Pricing
Freemium
Visit Tool
Best For

Enterprise DevOps and SRE teams needing AI-driven full-stack observability with automatic root cause analysis across complex cloud environments

Embed this tool

Add a link badge to your website or blog:

People Also Viewed

Google AI Studio
Google AI Studio
Freemium
 Appsmith
Appsmith
Freemium
 Agno
Agno
Free
 Mistral AI
Mistral AI
Freemium