Arize AI alternatives: Top 5 Arize competitors compared (2026)

TL;DR: Which Arize alternative fits your needs?

LangWatch wins for teams shipping production LLM applications with complex AI agents. Agent simulation testing, collaboration with PM's, OpenTelemetry-native tracing, and collaborative evaluation workflows make it the most complete platform for agentic AI.

Runner-up alternatives:

• Langfuse - Open-source flexibility, for small start-ups / dev-focussed teams

• Fiddler AI - Enterprise ML and LLM monitoring, but lacks agent simulation

• LangSmith - Great if you're locked into LangChain

• Helicone - Observability - Proxy LLM calls via their platform

Choose LangWatch if agent simulation (multi-turn conversations), automated prompt optimization, and cross-functional collaboration matter. Pick others if you only need basic logging or have specific constraints (e.g., LangChain-only, simple tracing).

Why teams look for Arize AI alternatives

Arize AI started as an ML model monitoring platform focused on traditional machine learning operations. The company built features for drift detection, model performance tracking, and tabular data monitoring designed for predictive ML models from years past.

As LLM applications moved from prototypes to production, Arize expanded into generative AI through its Arize Phoenix open-source framework and later added LLM capabilities to its web application. Arize now offers LLM evaluation, prompt versioning through their Prompt Hub, and tracing for conversational applications.

However, teams building production LLM applications with AI agents encounter specific workflow gaps:

• ML-first architecture. Arize's platform was built for traditional ML workflows. LLM evaluation, prompt management, and generative AI tracing were added later, creating a disconnected experience between classical model monitoring and agentic AI development.

• No agent simulation testing. Arize focuses on observing what happened after the fact. Teams need to test complex multi-turn agent conversations, tool usage patterns, and edge cases before deployment through synthetic simulations.

• Limited prompt optimization capabilities. Manual prompt engineering doesn't scale for complex agent systems. Teams need systematic, automated approaches to improve prompts using techniques like DSPy.

• Evaluation and tracing live in separate workflows. Dataset management exists separately from the tracing interface. Correlating evaluation results to specific production traces requires manual work.

Teams shipping LLM applications with AI agents need platforms that integrate simulation testing, automated optimization, evaluation, and observability into a single system.

Top 5 Arize alternatives (2026)

1. LangWatch: End-to-end LLM observability and agent testing platform

LangWatch takes an evaluation-first approach combined with unique agent simulation capabilities. Teams can test AI agents with thousands of synthetic conversations across scenarios, languages, and edge cases before production deployment, catching issues that traditional evaluation misses.

LangWatch consolidates agent testing, experimentation, evaluation, optimization, and observability into a single system. Teams use LangWatch instead of juggling multiple tools. Simulations, traces, evaluations, and optimization experiments stay in one place, with engineers, domain experts, and product managers collaborating together.

Pros

Agent simulation testing

• Simulate thousands of multi-turn conversations with AI agents testing scenarios before production

• Test complex agentic workflows including tool usage, reasoning chains, and multi-modal interactions

• Validate edge cases and adversarial scenarios in controlled environments

• Framework-agnostic simulation through AgentAdapter interface supporting any agent architecture

Systematic evaluation framework

• Pre-built evaluators tested for accuracy including hallucination detection and safety checks

• Auto-generated custom LLM-as-judge evaluators created by typing descriptions

• Rigorous regression detection: Failed experiments automatically become part of the regression suite that runs in CI/CD, providing measurable metrics to teams

• CLHF (Continuous Learning with Human Feedback) auto-tunes evaluators with few-shot examples

• Versioned datasets: Datasets built from production logs or curated test sets track performance over time for consistent evaluation across changes

• Run evaluations in real-time on production traces or offline on datasets

• Collaborate on the platform or run via your CI/CD

OpenTelemetry-native tracing

• Built on OpenTelemetry standard for framework-agnostic integration

• Automatic instrumentation for LangGraph, DSPy, LangChain, Pydantic AI, and more

• Support for 800+ models and providers including OpenAI, Anthropic, Bedrock, Gemini

• Zero vendor lock-in with standardized tracing

Collaborative UI & CI/CD runs

• Dual interface: platform UI for domain experts, programmatic APIs for developers

• Data review and labeling with collaborative annotation workflows

• Convert production traces into reusable test cases and golden datasets

• Custom dashboards for business stakeholders with user behavior analytics

Deployment flexibility

• Self-hosting with unlimited features

• Cloud-hosted option with generous free tier

• Hybrid deployment keeping sensitive data on-premises

• Enterprise support with air-gapped installations available

Cons

• Your team needs to be ready for setting quality standards, think about what's the goal and understand what quality looks like for your company/product.

Best for

Companies building production AI agents and complex agentic systems that need comprehensive testing, automated optimization, and cross-functional collaboration. Larger teams or organisations fit very well.

Pricing

Free tier for developers exploring evaluation workflows. Growth plan starts at €29/user/month with unlimited experiments and simulations. Enterprise plans with custom deployments including air-gapped modes.

Why teams choose LangWatch over Arize

LangWatch's agent simulation testing and automated optimization catch issues before production while enabling cross-functional teams to collaborate effectively. Start with LangWatch's free tier →

2. Langfuse: Open-source LLM observability platform

Langfuse offers an open-source observability platform with trace logging, prompt management, and basic analytics. The open-source and self-hosting option appeals to teams with strict data policies.

Pros

• Fully open-source with self-hosted deployment

• Manual annotation system for marking problematic outputs

• Session grouping for multi-turn conversation debugging

• Active community and abundant documentation

• Cost tracking across providers

Cons

• No built-in eval runners or scorers - you build everything yourself

• No CI/CD integration for automated testing

• No collaboration with PM's, Data Scientists.

• No agent simulation capabilities

• No automated prompt optimization

• Basic trace viewing without experiment comparison or statistical analysis

• Self-hosting requires dedicated DevOps resources

Pricing

Free for open-source self-hosting. Paid cloud plan starts at $29/month with custom enterprise pricing.

Best for

Teams requiring open-source self-hosted deployment with full data control who have resources to build custom evaluation and optimization pipelines from scratch.

Read more on LangFuse <> LangWatch.

3. Fiddler AI: Enterprise ML and LLM monitoring platform

Fiddler AI extends traditional ML monitoring into LLM observability. The platform offers monitoring, explainability, and safety features for both classical ML models and generative AI applications.

Pros

• Unified ML and LLM monitoring in one platform

• Drift detection for embeddings and model outputs

• Hallucination detection and safety guardrails

• Root cause analysis for model performance issues

• Alert configuration for quality thresholds

Cons

• ML-first interface makes LLM-specific workflows less intuitive

• No CI/CD deployment blocking

• No agent simulation testing

• No automated prompt optimization capabilities

• No free tier for teams to test the platform

• No built-in version control or A/B testing for prompts

Pricing

Custom enterprise pricing only.

Best for

Organizations already using Fiddler for traditional ML monitoring who want unified observability across both classical and generative AI models.

4. LangSmith: LangChain ecosystem observability

LangSmith is the observability platform built by the LangChain team. It traces LangChain applications automatically and provides evaluation tools designed specifically for LangChain workflows.

Pros

• Zero-config tracing for LangChain and LangGraph applications

• Automatic instrumentation with single environment variable

• Dataset versioning and test suite management

• Experiment comparison interface

• LLM-as-judge evaluation for correctness and relevance

Cons

• Works best within LangChain ecosystem, less flexible for other frameworks

• No agent simulation testing capabilities

• No automated prompt optimization framework

• Costs scale linearly with request volume

• Self-hosted deployment only on Enterprise plans

• Deep LangChain integration creates switching costs

Pricing

Free tier with 5K traces monthly for one user. Paid plan at $39/user/month with custom enterprise pricing including self-hosting.

Best for

Teams running their entire LLM stack on LangChain or LangGraph who prioritize zero-config framework integration over flexibility and agent testing capabilities.

Arize alternative feature comparison

Choosing the right Arize alternative

Choose LangWatch if: You need agent simulation testing, multi-turn agent testing, automated prompt optimization with DSPy, OpenTelemetry-native integration, or cross-functional collaboration between developers and domain experts.

Choose Langfuse if: Open-source self-hosting is mandatory and you have resources to build custom agent testing and optimization pipelines from scratch.

Choose Fiddler AI if: You already use Fiddler for ML monitoring and need unified observability across traditional and generative AI models.

Choose LangSmith if: Your entire stack runs on LangChain/LangGraph and deep framework integration outweighs the need for agent simulation and optimization.

Final recommendation

LangWatch covers the entire LLM development lifecycle for agentic AI, including agent simulation testing, comprehensive evaluation, and production observability. The platform uniquely enables domain experts and developers to collaborate through dual interfaces while maintaining OpenTelemetry compatibility for vendor independence.

Companies building AI agents use LangWatch to test thousands of scenarios before production, automatically optimize prompts systematically, and maintain quality through collaborative evaluation workflows. The open-source offering provides unlimited self-hosting at no cost.

Get started free or explore the documentation to see how LangWatch handles agent testing, optimization, and observability for production LLM applications.

Frequently asked questions

What is the best alternative to Arize for LLM observability?

LangWatch offers the most complete Arize alternative with agent simulation testing, collaborative evaluations, and OpenTelemetry-native tracing. Unlike Arize's ML-first architecture where LLM features were added later, LangWatch was built specifically for agentic AI workflows from day one. The platform uniquely combines simulation testing with automated optimization and comprehensive observability in one system.

Does Arize have a free tier for LLM tracing?

Arize Phoenix offers a free tier with 25K trace spans per month for one user. However, LangWatch provides a more generous free tier plus unlimited free self-hosting with no feature restrictions. LangWatch's free tier also includes agent simulation capabilities and DSPy optimization that Arize doesn't offer even in paid plans.

Do I need separate tools for LLM tracing and agent testing?

Not with modern platforms. LangWatch consolidates agent simulation testing, tracing, evaluation, prompt optimization, and dataset management in one system. Agent simulations automatically link to evaluation results and production traces, creating a complete feedback loop without tool switching.

Which LLM observability platform works with multiple AI providers?

LangWatch, Braintrust, Langfuse, LangSmith, and Fiddler AI all support multiple providers including OpenAI, Anthropic, Bedrock, and Gemini. However, LangWatch's OpenTelemetry-native architecture provides the most flexibility with support for 800+ models across any provider without vendor lock-in. Helicone only supports OpenAI.

How does LangWatch compare to Arize for agent testing?

LangWatch offers comprehensive agent simulation testing with thousands of synthetic conversations testing scenarios, tool usage, and multi-modal flows before production. Arize has no agent simulation capabilities and focuses only on observing production behavior after deployment. For teams building AI agents, LangWatch's simulation testing catches issues that traditional observability misses.

What is DSPy optimization and why does it matter?

DSPy is a systematic framework for automatically optimizing prompts using techniques like MIPROv2, ChainOfThought, and few-shot learning. Instead of manual prompt engineering, DSPy tests hundreds of variations and finds optimal combinations based on your metrics. LangWatch integrates DSPy directly into the platform, enabling teams to improve prompt performance in minutes rather than weeks. Arize has no automated optimization capabilities.

Can I self-host LangWatch for free?

Yes. LangWatch's open-source version can be self-hosted completely free with no feature restrictions using Docker Compose or Kubernetes. This includes all agent simulation, DSPy optimization, evaluation, and observability features. Arize requires an enterprise plan for self-hosting, and other platforms like Braintrust and LangSmith also restrict self-hosting to paid enterprise tiers.

Which platform is best for cross-functional collaboration?

LangWatch was designed for cross-functional teams with a dual interface approach. Domain experts from Legal, Sales, or HR can create evaluation scenarios and review results through the platform UI without coding. Developers can build complex workflows via programmatic APIs and SDKs. This enables true collaboration between technical and non-technical stakeholders on AI quality. Other platforms primarily target developers only.

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