# Arize AI vs RunLLM

Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. RunLLM records 182 monthly visits with -90.6% growth and Free Trial, Contact for Pricing pricing.

| Metric | [Arize AI](https://openaitools.app/tool/arize-com) | [RunLLM](https://openaitools.app/tool/runllm) |
| :--- | :--- | :--- |
| Website URL | https://arize.com | https://www.runllm.com |
| Categories | Large Language Models (LLMs), AI Agent, AI Monitor, AI Developer Tools | AI Agent, AI Customer Service, AI Assistant, AI Code Assistant |
| Pricing Model | Paid, Contact for Pricing | Free Trial, Contact for Pricing |
| Monthly Visits | 249,028 | 182 |
| MoM Growth Rate | +3.2% | -90.6% |
| Primary Market | United States | Global |
| Direct Visits | 107,838 | 71 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## Arize AI

Arize is an AI engineering platform built for teams that run generative AI in production. It is split into two products: Arize AX, a managed environment for agent observability, evaluation, and experimentation, and Phoenix, an open-source tracing and evaluation library that teams can self-host when they need full data control. Both rely on OpenInference, the open-source semantic convention for generative AI tracing that the Arize team founded and that is built on OpenTelemetry.

The workflow follows three questions. Observe answers what an agent actually did by capturing every span, trace, and session; Evaluate answers whether quality is improving or regressing using span-level, trace-level, and session-level evals; Learn answers whether a proposed change will help by letting teams test prompts and harnesses before rollout. A datastore for GenAI traces (ADB) keeps data in open formats that connect to warehouses such as BigQuery, Databricks, and Snowflake, and an in-app AI engineering agent named Alyx surfaces issues and drafts review-ready fixes.

The platform integrates with dozens of models, frameworks, and tools, including OpenAI, Anthropic, Google, Amazon Bedrock, LangChain, LangGraph, LlamaIndex, CrewAI, and DSPy. Arize publishes compliance certifications including SOC 2 Type II, ISO 27001, PCI DSS, HIPAA, and GDPR, and supports cloud, hybrid, and self-hosted deployment.

**Pricing & Plans:** Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host.

- **End-to-end tracing:** Capture spans, traces, and sessions across agents and LLM calls using OpenInference conventions built on OpenTelemetry, so instrumentation stays portable.
- **Scalable evaluation framework:** Run evaluations at span, trace, and session level to grade retrieval quality, tool use, and final answers across large production datasets.
- **Alyx AI engineering agent:** An in-product agent that inspects traces, diagnoses likely root causes, and proposes fixes that engineers can review before shipping.
- **Phoenix open-source toolkit:** Self-host the leading open-source tracing and eval tool when you need to keep trace data inside your own infrastructure.
- **Warehouse-native trace storage:** ADB stores traces in open formats that connect natively to BigQuery, Databricks, or Snowflake for joint analytics with business data.

**Strengths:** Covers the full loop of observe, evaluate, and improve rather than stopping at dashboards; Backed by OpenInference and OpenTelemetry standards, limiting vendor lock-in on trace formats; Strong enterprise posture with SOC 2, ISO 27001, HIPAA, and GDPR coverage plus self-hosting options; Integrates with a very wide set of models, frameworks, and agent SDKs

**Limitations:** Self-serve pricing is not published, so cost requires a sales conversation; The breadth of features can be overwhelming for small teams shipping their first LLM feature; Full value depends on disciplined instrumentation across the whole application

[Profile (Markdown)](https://openaitools.app/tool/arize-com.md)

## RunLLM

RunLLM, now operating under the Herald brand, has evolved from an enterprise technical-support assistant into what it calls an AI SRE: an agent that catches production issues before engineers are even paged. The pitch is that traditional observability is broken — you have to instrument every data stream, tune alert thresholds, and write runbooks for each failure mode, and anything nobody documented stays invisible.

Herald takes a different approach. Before any alerts fire, it builds a context graph spanning your observability, codebase, CI/CD, documentation, and dependencies, so it learns what normal looks like. It then builds a custom anomaly detection model for each data stream — no thresholds to set — and surfaces validated signals before customers notice. When something looks wrong, the agent evaluates multiple hypotheses simultaneously against the right data sources and hands engineers a root-cause analysis in minutes, no runbook required.

The company reports 100% RCA accuracy on known issues and over 70% on novel incidents for one of the world's biggest B2B2C platforms, and notes that the agent learns from every investigation so it never makes the same mistake twice. Herald was founded by PhDs and professors from UC Berkeley's RISELab, combining expertise in AI, LLMs, data systems, and scalable infrastructure.

**Pricing & Plans:** Free trial with demos available; enterprise pricing is quoted on request via sales contact. Check the site's pricing and demo pages for current details.

- **Context-aware learning:** Herald builds a context graph across observability, codebase, CI/CD, docs, and dependencies before alerts fire, so it understands what normal looks like.
- **Predictive detection:** No thresholds to tune: Herald builds a custom anomaly detection model for each data stream and surfaces validated issues before customers notice.
- **Automated investigation:** The agent evaluates multiple hypotheses at once against the right data sources and delivers root-cause analyses in minutes without runbooks.
- **Grounded in production:** 100% RCA accuracy on known issues and 70%+ on novel incidents, learning from every investigation so it never repeats a mistake.
- **Fast onboarding:** Scans your codebase, finds the tools you use, and configures itself in minutes, adapting quickly to your stack.

**Strengths:** Detects and investigates issues before alerts fire; No thresholds or runbooks to author and maintain; Correlates signals across code, infra, and telemetry; Built by UC Berkeley RISELab researchers

[Profile (Markdown)](https://openaitools.app/tool/runllm.md)

## Frequently Asked Questions

### How do Arize AI and RunLLM compare in monthly traffic?

Arize AI has 249,028 monthly visits, while RunLLM has 182. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Arize AI has a month-over-month growth rate of +3.2%; RunLLM has -90.6%.

### How does pricing compare?

Arize AI: Paid, Contact for Pricing. Arize uses a paid model with a free entry point: teams can start free or book a demo, while enterprise-scale usage, deployment options, and support are quoted through a contact-sales process. The open-source Phoenix project is free to self-host. RunLLM: Free Trial, Contact for Pricing. Free trial with demos available; enterprise pricing is quoted on request via sales contact. Check the site's pricing and demo pages for current details.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | The AI coding agent for building ambitious software | [.md](https://openaitools.app/tool/cursor.md) |
| 2 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | Describe your idea and Replit builds a working app in minutes | [.md](https://openaitools.app/tool/replit.md) |
| 3 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) |
| 4 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | GenAI-native end-to-end QA agent that plans, writes and heals tests in plain language | [.md](https://openaitools.app/tool/kaneai.md) |
| 5 | [Augment Code](https://openaitools.app/tool/augment-code) | 326,215 | +24.1% | Freemium, Free Trial, Contact for Pricing | Agent orchestration platform for your whole software development lifecycle | [.md](https://openaitools.app/tool/augment-code.md) |
| 6 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Excels at experiment tracking and model registry workflows, but is less purpose-built for real-time LLM production observability. | [.md](https://openaitools.app/tool/prompts.md) |
| 7 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Includes integrated guardrails and simulation tools, but is less mature for large-scale enterprise production telemetry monitoring. | [.md](https://openaitools.app/tool/future-agi.md) |
| 8 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Features strong testing and prompt evaluation tools, though its monitoring interface is less oriented towards massive telemetry streams. | [.md](https://openaitools.app/tool/maxim-ai.md) |
| 9 | [PromptLayer](https://openaitools.app/tool/promptlayer) | 85,899 | -4.6% | Free Trial, Contact for Pricing | Focuses heavily on prompt management and versioning control, whereas Arize prioritizes end-to-end tracing and ML observability. | [.md](https://openaitools.app/tool/promptlayer.md) |
| 10 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Offers comprehensive LLM evaluation and unit testing via DeepEval, but lacks Arize's deep production monitoring infrastructure. | [.md](https://openaitools.app/tool/confident-ai.md) |

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Source: https://openaitools.app/compare/arize-com-vs-runllm

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