Arize Phoenix vs Opik: both are coding tools on Scriptiii AI Tools. Arize Phoenix pricing: Free. Free to use. (reported by the maker) Opik pricing: Free. Free to use. (reported by the maker) Both have 0 upvotes.
Arize Phoenix is Arize's open-source AI observability platform designed for experimentation, evaluation, and troubleshooting. For managed production workflows, Arize also offers Arize AX. Phoenix provides:
Tracing - Trace your LLM application's runtime using OpenTelemetry-based instrumentation.
Evaluation - Leverage LLMs to benchmark your application's performance using response and retrieval evals.
Datasets - Create versioned datasets of examples for experimentation, evaluation, and fine-tuning.
Experiments - Track and evaluate changes to prompts, LLMs, and retrieval.
Prompt Management- Manage and test prompt changes systematically using version control, tagging, and experimentation.
PXI (Phoenix Intelligence) - An AI engineering agent built into Phoenix for debugging traces, iterating on prompts, and navigating the product.
Remote MCP Server - Connect Claude Code, Cursor, and other MCP clients directly to your Phoenix instance's /mcp endpoint to query traces, datasets, experiments, and more.
Phoenix runs practically anywhere, including your local machine, a containerized deployment, or in the cloud. See Self-Hosting for a walkthrough of each option, or jump straight into the Tracing Quickstart.
[!NOTE]
Phoenix is open-source and self-hosted. For a managed platform, Arize offers Arize AX โ the same OpenTelemetry/OpenInference instrumentation works with both. See the full comparison.
The fastest way to send traces is to let your coding agent (Claude Code, Codex, Cursor, and others) instrument your app. From your project directory, run:
npx @arizeai/phoenix-cli setup
## or, with Phoenix installed: px setup
Setup detects your framework and LLM provider, installs the right OpenInference instrumentation, and wires up trace export. Prefer to wire it up in code? See the tracing documentation.
Deploy
Phoenix container images are available via Docker Hub and can be deployed using Docker or Kubernetes via the Helm chart.
[!NOTE]
The Google Cloud button builds Phoenix from source in Cloud Shell rather than deploying the prebuilt Docker Hub image. The Azure template serves plain HTTP (Azure Container Instances does not terminate TLS) โ front it with a TLS proxy such as an Application Gateway before production use.
Packages
The arize-phoenix package includes the entire Phoenix platform. However, if you have deployed the Phoenix platform, there are lightweight Python sub-packages and TypeScript packages that can be used in conjunction with the platform.
CLI for fetching traces, datasets, and experiments for use with Claude Code, Cursor, and other coding agents
Tracing Integrations
Phoenix is built on top of OpenTelemetry and is vendor, language, and framework agnostic. For details about tracing integrations and example applications, see the OpenInference project and the integrations documentation.
People and coding agents such as Claude Code, Codex, and Cursor can both work with Phoenix. To give an agent access to your traces, datasets, and experiments, connect it in one of these ways:
Claude Code or Codex. Install the Arize Phoenix plugin. It registers the Phoenix MCP server and installs the Phoenix skills.
Any other agent. Add the Remote MCP Server at your instance's /mcp endpoint. It serves your Phoenix data and the Phoenix skills, which match your server version.
Optional CLI. The Phoenix CLI, npx @arizeai/phoenix-cli, runs Phoenix commands in a terminal, and px setup instruments your app through your coding agent.
By default, Phoenix collects basic web analytics (e.g., page views, UI interactions) to help us understand how Phoenix is used and improve the product. None of your trace data, evaluation results, or any sensitive information is ever collected.
You can opt-out of telemetry by setting the environment variable: PHOENIX_TELEMETRY_ENABLED=false
Community
Join our community to connect with thousands of AI builders.
Opik: Open-Source LLM Observability, Evaluation & AI Agent Tracing
Opik is the open-source LLM observability and evaluation platform for AI agent tracing, LLM evaluation, prompt management, and production monitoring. Built by Comet. Apache-2.0 licensed, free to self-host the full platform, with 20,000+ GitHub stars.
Opik covers the full LLM application lifecycle, from the first trace in development to production monitoring, for teams building LLM apps and AI agents. Key offerings include:
AI Agent Tracing & Observability: Deep tracing of LLM calls, conversation logging, and agent activity, with full trace trees for multi-step agents and tool calls.
LLM Evaluation: Datasets, experiments, and LLM-as-a-judge metrics for hallucination detection, moderation, and RAG assessment.
Prompt & Agent Optimization: The Opik Agent Optimizer SDK to improve prompts and agents.
Production-Ready Monitoring: Scalable dashboards and online evaluation rules.
Opik Guardrails: Features to help you implement safe and responsible AI practices.
CI/CD Evaluation: A PyTest integration to test LLM pipelines on every commit.
Key capabilities include:
Development & Tracing:
Track all LLM calls and traces with detailed context during development and in production (Quickstart).
Extensive 3rd-party integrations for easy observability: Seamlessly integrate with a growing list of frameworks, supporting many of the largest and most popular ones natively (including recent additions like Google ADK, Autogen, and Flowise AI). (Integrations)
Annotate traces and spans with feedback scores via the Python SDK or the UI.
Leverage Opik Agent Optimizer and Opik Guardrails to continuously improve and secure your LLM applications in production.
Who it's for: ML engineers building LLM-powered agents, AI teams moving from prototype to production, and engineering teams that need open-source, self-hostable observability they can run in their own environment.
Why open source matters here: Opik is Apache-2.0 licensed and free to self-host: the full platform, backend included, not just a client SDK. The repository includes the server backend, web application, tracing, datasets, experiments, evaluations, prompt management, online evaluation, and agent optimization components, all under Apache-2.0. You can run LLM observability inside your own infrastructure with no data leaving your environment and no Enterprise sales conversation required.
[!TIP]
If you are looking for features that Opik doesn't have today, please raise a new Feature request ๐
โก Quick Start
Install the Python SDK and configure it:
pip install opik
opik configure
Wrap any function with the @track decorator to start logging traces:
Every call to my_function is now logged to Opik, including nested calls, so this works for full agent and pipeline traces, not just single LLM calls. See the Quickstart guide for the TypeScript SDK and other setup options.
Connect your coding agent
Let Claude Code, Cursor, VS Code Copilot, Codex or opencode read your traces, score outputs and run evaluations from chat. One command sets it up. It needs uv and no SDK:
uvx opik mcp configure
The badges and the add-mcp fallback below target Opik Cloud; the command above also handles self-hosted deployments. Other MCP clients on Opik Cloud: npx add-mcp https://www.comet.com/opik/api/v1/mcp --name opik-mcp. Details, troubleshooting and FAQ are in the MCP server guide.
๐ How Does Opik Compare?
Opik competes in the LLM observability / AI agent evaluation category alongside LangSmith, Arize (Phoenix and Arize AX), Weights & Biases (Weave), Langfuse, and Braintrust.
Capability
Opik
LangSmith
Phoenix
Arize AX
Weights & Biases (Weave)
Langfuse
Braintrust
Open source
Yes, Apache-2.0 (full platform)
No
Source-available (Elastic License 2.0, not OSI-approved)
No
Open-source SDK/toolkit; self-managed platform requires a commercial license
When teams choose Opik: Opik's full observability, evaluation, and optimization platform is Apache-2.0 licensed and free to self-host. Unlike closed platforms whose self-hosted deployment requires an Enterprise plan, Opik can be deployed without a commercial license, and it's framework-agnostic so it won't lock you into a single agent ecosystem. See the table above for where self-hosting and licensing differ across alternatives.
โ Frequently Asked Questions
Is Opik open source?
Opik is licensed under Apache 2.0. Its server, web application, and core observability and evaluation capabilities can be self-hosted without a commercial license.
Can I self-host Opik?
Yes. Opik can be deployed locally or in your own infrastructure using the documented self-hosting options.
Does Opik support AI agent tracing?
Yes. Opik captures multi-step traces containing LLM calls, tool executions, retrieval steps, and other agent activity.
Deploy Opik in your own environment. Choose between Docker for local setups or Kubernetes for scalability.
Self-Hosting with Docker Compose (for Local Development & Testing)
This is the simplest way to get a local Opik instance running. Note the new ./opik.sh installation script:
On Linux or Mac Environment:
## Clone the Opik repository
git clone https://github.com/comet-ml/opik.git
## Navigate to the repository
cd opik
## Start the Opik platform
./opik.sh
On Windows Environment:
## Clone the Opik repository
git clone https://github.com/comet-ml/opik.git
## Navigate to the repository
cd opik
## Start the Opik platform
powershell -ExecutionPolicy ByPass -c ".\\opik.ps1"
Installation Script Options
The opik.sh and opik.ps1 scripts support the following options:
## Start full Opik suite (default behavior)
./opik.sh
## Start only infrastructure services (databases, caches etc.)
./opik.sh --infra
## Start infrastructure + backend services
./opik.sh --backend
## Enable guardrails with any profile
./opik.sh --guardrails # Guardrails with full Opik suite
./opik.sh --backend --guardrails # Guardrails with infrastructure + backend
## Build the containers from source before starting
./opik.sh --build
## Check that all containers are healthy
./opik.sh --verify
## Stop all containers
./opik.sh --stop
## Stop all containers and remove all Opik data volumes
## WARNING: ALL OPIK DATA WILL BE LOST
./opik.sh --clean
## Show all available options
./opik.sh --help
Use the --help or --info options to troubleshoot issues. Dockerfiles now ensure containers run as non-root users for enhanced security. Once all is up and running, you can now visit localhost:5173 on your browser! For detailed instructions, see the Local Deployment Guide.
Self-Hosting with Kubernetes & Helm (for Scalable Deployments)
For production or larger-scale self-hosted deployments, Opik can be installed on a Kubernetes cluster using our Helm chart. Click the badge for the full Kubernetes Installation Guide using Helm.
๐ป Opik Client SDK
Opik provides a suite of client libraries and a REST API to interact with the Opik server. This includes SDKs for Python and TypeScript, plus first-party OpenTelemetry support: any language with an OpenTelemetry SDK โ including Java, Ruby, and .NET โ can send traces to Opik. For detailed API and SDK references, see the Opik Client Reference Documentation.
Python SDK Quick Start
To get started with the Python SDK:
Install the package:
## install using pip
pip install opik
## or install with uv
uv pip install opik
Configure the python SDK by running the opik configure command, which will prompt you for your Opik server address (for self-hosted instances) or your API key and workspace (for Comet.com):
opik configure
[!TIP]
You can also call opik.configure(use_local=True) from your Python code to configure the SDK to run on a local self-hosted installation, or provide API key and workspace details directly for Comet.com. Refer to the Python SDK documentation for more configuration options.
You are now ready to start logging traces using the Python SDK.
๐ Logging Traces with Integrations
The easiest way to log traces is to use one of our direct integrations. Opik supports a wide array of frameworks, including recent additions like Google ADK, Autogen, AG2, and Flowise AI:
[!TIP]
If the framework you are using is not listed above, feel free to open an issue or submit a PR with the integration.
If you are not using any of the frameworks above, you can also use the track function decorator to log traces:
import opik
opik.configure(use_local=True) # Run locally
@opik.track
def my_llm_function(user_question: str) -> str:
# Your LLM code here
return "Hello"
[!TIP]
The track decorator can be used in conjunction with any of our integrations and can also be used to track nested function calls.
๐งโโ๏ธ LLM as a Judge metrics
The Python Opik SDK includes a number of LLM as a judge metrics to help you evaluate your LLM application. Learn more about it in the metrics documentation.
To use them, simply import the relevant metric and use the score function:
from opik.evaluation.metrics import Hallucination
metric = Hallucination()
score = metric.score(
input="What is the capital of France?",
output="Paris",
context=["France is a country in Europe."]
)
print(score)
Opik also includes a number of pre-built heuristic metrics as well as the ability to create your own. Learn more about it in the metrics documentation.
๐ Evaluating your LLM Applications
Opik allows you to evaluate your LLM application during development through Datasets and Experiments. The Opik Dashboard offers enhanced charts for experiments and better handling of large traces. You can also run evaluations as part of your CI/CD pipeline using our PyTest integration.
โญ Star Us on GitHub
If you find Opik useful, please consider giving us a star! Your support helps us grow our community and continue improving the product.
Both listings report Free. Free to use. Pricing is reported by the maker and is not independently verified.
Which has more upvotes, Arize Phoenix or Opik?
Both have 0 upvotes.
What does each maker say Arize Phoenix and Opik are for?
Arize Phoenix: AI observability and evaluation tool for tracing and evaluating LLM applications. Opik: Comet open-source platform for tracing, evaluating and monitoring LLM apps and agents.
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