# Arize AI vs Langtail

Arize AI records 249,028 monthly visits with +3.2% month-over-month growth and Paid, Contact for Pricing pricing. Langtail records 4,213 monthly visits with +10% growth and Freemium, Paid pricing.

| Metric | [Arize AI](https://openaitools.app/tool/arize-com) | [Langtail](https://openaitools.app/tool/langtail) |
| :--- | :--- | :--- |
| Website URL | https://arize.com | https://langtail.com |
| Categories | Large Language Models (LLMs), AI Agent, AI Monitor, AI Developer Tools | Large Language Models (LLMs), AI Testing, No-Code&Low-Code, Prompt Engineering |
| Pricing Model | Paid, Contact for Pricing | Freemium, Paid |
| Monthly Visits | 249,028 | 4,213 |
| MoM Growth Rate | +3.2% | +10% |
| Primary Market | United States | United States |
| Direct Visits | 107,838 | 1,680 |
| 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)

## Langtail

Langtail is built around a single premise: LLM output is unpredictable, and the way to regain control is to treat prompts as managed production assets. Instead of scattering prompt strings through application code, teams write them in a spreadsheet-like editor, run them through evaluation suites, version the changes, and invoke them by name from a typed SDK or the OpenAPI interface. A TypeScript SDK with code completion makes the same prompts callable from application code, so what a product manager edits in the UI is exactly what ships.

The observability layer is what makes testing meaningful. Every invocation is logged, so you can inspect inputs, outputs, latency, and cost, then trace a bad answer back to a prompt change or a model switch. Evaluation supports natural-language assertions, regex-style pattern matching, or fully custom code, and teams can experiment with models and parameters side by side to find the combination that performs best. Langtail positions this as essential infrastructure rather than a nice-to-have, using publicised incidents where unguarded chatbots misfired to make the point.

Security rounds out the platform. The AI Firewall is a one-click integration that inspects traffic for prompt injection, denial-of-service patterns, and information leakage, with content filtering you can tune and alerts for unusual activity. The platform works with all major model providers and exposes a public API and status page; enterprise customers can self-host for maximum data control, while a free tools section offers extras such as a prompt improver and an LLM price comparison outside the paid plans.

**Pricing & Plans:** Free tier of $0/month with unlimited users, 2 prompts or assistants, 1,000 logs per month, and 30 days of data retention. Pro is $99/month for a single user with 20 prompts, unlimited logs, 90-day retention. Team is $499/month for 10 users with unlimited prompts, unlimited logs, one-year retention, and Radars & Alerts. Enterprise is custom and adds the AI Firewall, self-hosting, and dedicated support. Prices exclude VAT.

- **Spreadsheet-style prompt editor:** Build, test, and version prompts in a table-based editor that anyone comfortable with spreadsheets can use.
- **Automated evaluation and test runs:** Add natural-language assertions, pattern matching, or custom code checks and run them over datasets to catch regressions.
- **Request logging and observability:** Trace every request and response, inspect latency and cost, and diagnose why an LLM answered the way it did.
- **AI Firewall and safety checks:** One-click AI Firewall blocks prompt injection, denial-of-service attempts, and information leaks with customisable content filtering and alerts.
- **Multi-provider support and versioning:** Compare OpenAI, Anthropic, Gemini, Mistral and more, and roll out prompt changes through versioned environments.

**Strengths:** Non-developers can manage prompts without touching the codebase; Evaluation and log inspection live alongside prompt editing; Works across all major LLM providers, avoiding lock-in; Self-hosting on Enterprise answers strict data-control requirements

**Limitations:** Pro plan supports only a single user, so small teams may need the pricier Team tier; Log limits and retention windows differ sharply between tiers; Feature depth is aimed at production teams rather than one-off prompt experiments; Self-hosting and the AI Firewall sit behind the Enterprise plan

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

## Frequently Asked Questions

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

Arize AI has 249,028 monthly visits, while Langtail has 4,213. 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%; Langtail has +10%.

### 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. Langtail: Freemium, Paid. Free tier of $0/month with unlimited users, 2 prompts or assistants, 1,000 logs per month, and 30 days of data retention. Pro is $99/month for a single user with 20 prompts, unlimited logs, 90-day retention. Team is $499/month for 10 users with unlimited prompts, unlimited logs, one-year retention, and Radars & Alerts. Enterprise is custom and adds the AI Firewall, self-hosting, and dedicated support. Prices exclude VAT.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [DeepSeek](https://openaitools.app/tool/deepseek) | 372,761,591 | +4.3% | Freemium | High-efficiency open-weight and hosted reasoning AI powered by DeepSeek-V4 and R1 at ultra-low token costs. | [.md](https://openaitools.app/tool/deepseek.md) |
| 2 | [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) |
| 3 | [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) |
| 4 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |
| 5 | [Maxim AI](https://openaitools.app/tool/maxim-ai) | 121,351 | +10% | Contact for Pricing, Free Open Source | Evaluate, monitor, and govern your AI applications in production | [.md](https://openaitools.app/tool/maxim-ai.md) |
| 6 | [testRigor](https://openaitools.app/tool/testrigor-com) | 105,865 | -2.7% | Free Trial, Contact for Pricing, Free | Write end-to-end test automation in plain English, no code required | [.md](https://openaitools.app/tool/testrigor-com.md) |
| 7 | [PromptLayer](https://openaitools.app/tool/promptlayer) | 85,899 | -4.6% | Free Trial, Contact for Pricing | The collaboration layer for AI engineering teams | [.md](https://openaitools.app/tool/promptlayer.md) |
| 8 | [Confident AI](https://openaitools.app/tool/confident-ai) | 83,400 | -3.3% | Freemium, Paid, Contact for Pricing | Standardize LLM evaluation, observability, and red teaming across your whole org | [.md](https://openaitools.app/tool/confident-ai.md) |
| 9 | [LangWatch](https://openaitools.app/tool/langwatch) | 52,098 | +73% | Contact for Pricing | Agent observability, evals and governance for AI platform teams | [.md](https://openaitools.app/tool/langwatch.md) |
| 10 | [Openlayer](https://openaitools.app/tool/openlayer) | 48,415 | -6.4% | Freemium, Contact for Pricing | Stronger focus on compliance enforcement and AI governance, but less specialized for deep developer tracing workflows. | [.md](https://openaitools.app/tool/openlayer.md) |

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

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