Arize AI

What Arize AI does

Arize AI provides an AI engineering platform for self-improving AI agents and LLM applications. It offers end-to-end workflows for agent debugging, including observability, evaluation, and improvement. The platform helps teams trace agent activities, evaluate performance, and test changes before deployment. Built on open-source standards like OpenInference and OpenTelemetry, Arize AI supports various models, frameworks, and AI tools, and offers flexible deployment options including SaaS and self-hosted environments. The company emphasizes security, privacy, and compliance, holding certifications such as SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR.

Products

• Arize AXThe AI engineering platform for self-improving agents, offering end-to-end workflows for agent debugging, evaluation, and improvement. It includes features for tracing, evaluating agent performance, and testing prompts and harnesses.
• AlyxAn AI engineering agent built into Arize AX that automates AI engineering workflows. It runs evals, debugs issues, improves agents, performs instant root cause analysis, generates synthetic data from failures, and optimizes prompts.
• adb (Arize Database)An AI native datastore designed for generative AI workflows. It unifies observability and evaluation data in open formats, enabling zero-copy access across the AI and data stack, with elastic and real-time capabilities for massive scale.
• PhoenixAn open-source, local-first platform for tracing, evaluation, experimentation, and prompt iteration. It helps users understand and improve AI applications by providing workflows for debugging and iteration, built on OpenTelemetry and OpenInference.
• OpenInferenceAn open-source leader in GenAI semantic conventions, built on OpenTelemetry, for instrumenting AI applications without proprietary trace formats.

Key capabilities

• Agent observability
• Agent evaluation
• Agent improvement loop
• End-to-end workflows for agent debugging
• Trace everything with OpenInference standards
• Comprehensive evaluation framework (span, trace, session evals)
• Prompt and harness testing
• Agent-first debugging for coding agents
• Agent-native development workflows
• AI engineering agent (Alyx)
• GenAI native datastore (adb)
• Open-source tools (Phoenix, OpenInference)
• Integration with 40+ models, frameworks, and AI tools
• Flexible deployment options (SaaS, self-hosted)
• Security and compliance certifications (SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, GDPR)
• Signal: automatically find failure modes and create PRs
• Managed agents for issue fixing
• Swarm observability for monitoring multiple agents
• Agent-as-a-Judge for continuous evaluation
• Agent experimentation for continuous improvement
• Instant Root Cause Analysis (Alyx)
• Synthetic Data from Real Failures (Alyx)
• Auto-Generated Evals (Alyx)
• Prompt Experimentation (Alyx)
• Open, elastic, and real-time datastore (adb)
• Zero-copy access across AI and data stack (adb)
• Cost control with elastic tiers (adb)
• Tracing (Phoenix)
• LLM-based evaluations (Phoenix)
• Prompt management and iteration (Phoenix)
• Datasets and experiments (Phoenix)
• Auto-instrumentation for popular frameworks and providers

Use cases

• Debugging AI agents
• Evaluating AI agent performance
• Improving AI agents and LLM applications
• Operationalizing AI workflows
• Building trustworthy, high-performing AI systems
• Catching regressions early in AI systems
• Meeting strict SLOs at scale for AI applications
• Understanding AI agent behavior
• Measuring quality with evaluations
• Monitoring production performance of AI systems
• Continuously improving prompts, models, and workflows
• Building modern AI applications (chatbots, RAG systems, copilots, agents)
• Automating AI engineering workflows
• Instant root cause analysis for AI failures
• Generating production-realistic test datasets
• Optimizing prompts through experimentation
• Tracing LLM calls and agent activities
• Managing and analyzing datasets for AI models
• Running experiments to compare different versions of AI applications
• Ensuring accuracy and trust in GenAI deployments
• Evaluating and observing new AI products and capabilities
• Rapid prototyping of LLM projects
• Monitoring quality and managing cost of AI models in production
• Identifying areas for improvement in AI agents
• Testing AI agents in shadow mode
• Accelerating health research with LLM observability
• Developing GenAI features for large user bases

AI approach

Arize AI provides an AI engineering platform for self-improving agents, focusing on observability, evaluation, and improvement of AI applications. They offer tools for tracing, evaluating, and debugging agents, including an AI engineering agent called Alyx. Their platform is built on open-source standards like OpenInference and OpenTelemetry, and they develop their own datastore (adb) optimized for AI workloads. They enable users to build, evaluate, and improve their agents, supporting various AI applications like chatbots, RAG systems, copilots, and agents.

Tech named: OpenInference, OpenTelemetry, Alyx, adb, Signal, Managed Agents, Agent-as-a-Judge, LLM-based evaluators, code-based checks, Iceberg, Ragas, Deepeval, Cleanlab

Industries served

• Software Development
• Technology
• Travel
• Real Estate
• Cybersecurity
• Enterprise Content Management
• Construction
• Fleet Management
• Healthcare
• Retail

What it says sets it apart

• Built for AI engineers by AI engineers
• Focus on self-improving agents and continuous learning loops
• Open-source foundation with OpenInference and OpenTelemetry standards
• Comprehensive evaluation framework for span, trace, and session evals
• AI engineering agent (Alyx) for autonomous workflow orchestration
• AI native datastore (adb) optimized for generative AI workloads
• Flexible deployment options (SaaS or self-hosted)
• Strong emphasis on security and compliance with multiple certifications
• Ability to store and query years of data at significantly lower cost (adb)
• Real-time and elastic performance for AI workloads (adb)
• Agent-first debugging for coding agents
• Managed agents for automated issue fixing
• Agent-as-a-Judge for scalable evaluations
• Phoenix for local-first tracing, evaluation, and experimentation
• Seamless integration with 40+ models, frameworks, and AI tools
• Customer success stories from leading enterprises like Atlassian, PepsiCo, TheFork, Booking.com, PagerDuty, Keller Williams, Siemens, TripAdvisor, Handshake, Typeform, Geotab, Bazaarvoice, Flipkart, Atropos Health.

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.