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

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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, but these are tools designed for predictive ML models deployed years ago.

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 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 conversational AI development.
• No deployment blocking in CI/CD. Arize integrates with GitHub Actions for running experiments, but doesn't block deployments when evaluation metrics degrade. Teams manually review results and decide whether to merge code.
• 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.
• Limited pre-production simulation. The platform focuses on production monitoring. Testing prompts against synthetic datasets before deployment requires external tooling or custom scripts.

Teams shipping LLM applications need platforms that integrate evaluation, experimentation, and observability into a single system, not separate tools bolted onto ML monitoring infrastructure.

Top 5 Arize alternatives (2026)

1. Braintrust: End-to-end LLM observability and evaluation platform

Braintrust takes an evaluation-first approach to LLM development. Automated evals run in CI/CD pipelines and block deployments when quality drops, catching regressions before users see them. Unlike LLM observability tools that surface issues after release, Braintrust prevents bad prompts from shipping with proactive measurements.

Braintrust consolidates experimentation, evaluation, and observability into a single system. Teams use Braintrust instead of juggling multiple tools. Traces, evals, and decisions stay in one place, with engineers and product managers reviewing outputs together and choosing what ships.

Pros

Evaluation framework

• Write custom scorers in Python or TypeScript using Autoevals for any quality metric
• Run evals locally, in the web interface, CI/CD pipelines, or on scheduled jobs
• Automatic hallucination detection, factuality checks, and custom business logic
• AI-powered Loop agent automatically generates custom scorers tailored to your use case
• Evals with LLM as a judge
• 10,000 free eval runs monthly on free tier

Automatic tracing and logging

• SDK integration captures nested LLM calls without manual instrumentation
• AI Proxy mode logs all OpenAI/Anthropic/Cohere calls with zero code changes
• Trace visualization shows full context: retrieval results, reranking scores, and final generation
• Production traces can be added directly to eval datasets

Prompt and dataset management

• Version control for prompts with full history and rollback
• Interactive playground for collaboratively testing prompts
• Dataset import from CSV, JSON, or production logs
• Collaborative annotation and labeling workflows

CI/CD native integration

• GitHub Actions and GitLab CI integration out of the box
• Eval results posted as PR comments with pass/fail status
• Automatic regression detection with deployment blocking

Deployment flexibility

• Cloud-hosted with SOC 2 compliance
• Self-hosted deployment for data residency requirements (Enterprise plan)
• API access for custom integrations
• Multi-provider support (OpenAI, Anthropic, Cohere, Azure OpenAI, custom models)

Cons

• Self-hosting requires an Enterprise plan
• Custom scorer development needs coding skills in Python or TypeScript

Best for

Companies building production LLM apps that care about accuracy, quality, and safe releases.

Pricing

Free tier includes 1M trace spans per month, unlimited users, and 10,000 evaluation runs. Sufficient for most early-stage teams and small production applications. Pro plan starts at $249/month, with custom enterprise plans available. See pricing details →

Why teams choose Braintrust over Arize

Braintrust's combined observability and evaluation framework catches regressions before they hit production. Start with Braintrust's free tier →

2. Langfuse: Open-source LLM observability platform

Langfuse offers an open-source observability platform offering trace logging, prompt management, and basic analytics in addition to cloud software. The open source and self-hosting option may appeal to teams with strict data policies, but you maintain infrastructure yourself.

Pros

• Self-hosted deployments
• Open source
• Manual annotation system for marking problematic outputs
• Session grouping for multi-turn conversation debugging
• Basic cost tracking across providers

Cons

• Logs traces but provides no built-in eval runners or scorers
• No CI/CD integration for automated testing or quality gates
• Basic trace viewing without experiment comparison, A/B testing, or statistical analysis
• Self-hosting requires dedicated DevOps resources for setup, maintenance, scaling, and upgrades
• Interface slows down noticeably with high trace volume, especially when viewing complex multi-agent traces

Pricing

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

Best for

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

Read our guide on Langfuse vs. Braintrust.

3. Fiddler AI: Enterprise ML and LLM monitoring platform

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

Pros

• 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

• The interface reflects this ML-first heritage, making LLM-specific workflows less intuitive
• No CI/CD deployment blocking
• No integrated evaluation framework
• No free tier or self-service option for teams to quickly test the platform
• No built-in version control or A/B testing framework 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 a single environment variable
• Dataset versioning and test suite management
• Experiment comparison interface showing performance across metrics
• LLM-as-judge evaluation for correctness and relevance

Cons

• LangSmith works best within the LangChain ecosystem
• Costs scale linearly with request volume rather than value delivered
• Self-hosted deployment is only available on Enterprise plans
• Deep integration with LangChain creates switching costs

Pricing

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

Best for

Teams running their entire LLM stack on LangChain or LangGraph who prioritize zero-config framework integration over flexibility and accept per-trace pricing that scales with usage volume.

5. Helicone: OpenAI proxy with cost tracking

Helicone acts as an LLM proxy between your app and LLM providers. It logs requests and responses, but stops at observability. No evals, datasets, or experimentation.

Pros

• Simple proxy setup requiring only endpoint change
• Cost tracking with per-user spending breakdowns
• Request count and latency monitoring
• Low latency overhead for OpenAI calls
• Dashboard showing usage patterns

Cons

• No eval framework, no scorers, no automated testing
• Can't build test suites, version datasets, or create evaluation workflows
• No prompt comparison, A/B testing, or quality analysis beyond basic dashboards
• No self-hosting; cloud-only deployment
• Basic logging without deep trace visualization, nested call analysis, or debugging tools

Pricing

Free tier (10,000 requests/month). Paid plan at $79/month.

Best for

Teams using only OpenAI models who need basic proxy logging and cost tracking without evaluation capabilities or multi-provider support.

Arize alternative feature comparison

Choosing the right Arize alternative

Choose Braintrust if: You need CI/CD deployment blocking, end-to-end evaluation workflows, cross-functional collaboration, or complex multi-agent tracing.

Choose Langfuse if: Open-source self-hosting is mandatory and you have resources to build custom eval pipelines.

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 flexibility concerns.

Choose Helicone if: You only use OpenAI and need basic proxy logging without evaluation capabilities.

Final recommendation

Braintrust covers the entire LLM development lifecycle, including prompt experimentation, automated CI/CD evaluation, statistical comparison, deployment blocking, and production observability. The free tier includes 1M trace spans and 10K eval runs to catch regressions before customers see them.

Companies like Notion, Zapier, Stripe, and Vercel use Braintrust in production. Notion reported going from fixing 3 issues per day to 30 after adopting the platform.

Get started free or schedule a demo to see how Braintrust handles evaluation and observability for production LLM applications.

Frequently asked questions

What is the best alternative to Arize for LLM observability?

Braintrust offers the most complete Arize alternative with CI/CD-native evaluation, automatic deployment blocking, and integrated observability. Unlike Arize's ML-first architecture, where LLM features were added later, Braintrust was built specifically for generative AI workflows from day one. The platform combines prompt experimentation, automated testing, and production tracing in one system, eliminating the need to stitch together multiple tools.

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, Braintrust provides a more generous free tier with 1M trace spans and 10K evaluation runs monthly for unlimited users. Braintrust's free tier also includes CI/CD integration and deployment blocking features that Arize doesn't offer, even in paid plans.

Do I need separate tools for LLM tracing and evaluation?

Not with modern platforms. Braintrust consolidates tracing, evaluation, prompt management, and dataset versioning in one system. Evaluation results automatically link to specific traces and datasets.

Which LLM observability platform works with multiple AI providers?

Braintrust, Langfuse, LangSmith, and Fiddler AI support multiple providers, including OpenAI, Anthropic, Cohere, and Azure OpenAI. Helicone only supports OpenAI. Braintrust's proxy mode works with all major providers without code changes, while framework-specific tools like LangSmith require additional instrumentation for non-LangChain stacks.

How does Braintrust compare to Arize for LLM tracing?

Braintrust offers 40x more free trace spans (1M vs 25K), unlimited users instead of 1, and evaluation features that Phoenix lacks. Braintrust provides CI/CD deployment blocking, prompt versioning with A/B testing, and integrated dataset management. Phoenix focuses on basic tracing without evaluation automation.