Choose an experiment tracking replacement by the work your team does most: logging training runs for models you build, or tracing and evaluating LLM apps and agents. Tools lean one way or the other. Then decide between a hosted platform and open source you run yourself, and log one real run or trace in two tools before switching.
Experiment tracking used to mean one job: recording the parameters, metrics and artefacts from each training run so you could compare them later. Now plenty of teams leaving Weights & Biases are building on top of large language models instead, and what they need is tracing and evaluation. Here are ten options, in no particular order, grouped by the job they lean towards.
Ten trackers and platforms at a glance
| Tool | Kind of tool | Good for |
|---|---|---|
| Arize AI | Agent observability | Tracing, evaluating and experimenting with AI agents |
| ClearML | AI infrastructure platform | Managing GPU clusters and the AI workflow together |
| Comet | Experiment tracking and LLM observability | Experiment tracking plus LLM observability through Opik |
| Langfuse | Open-source LLM observability | Open-source tracing and evals for LLM apps |
| Metaflow | Open-source ML framework | Building and managing real-life ML and data science projects |
| MLflow | Open-source MLOps | Open-source tracking for agents, LLMs and models |
| Polyaxon | Kubernetes AI control plane | Running and observing ML workloads on Kubernetes |
| Trackio | Lightweight local tracking | Free, local-first experiment logging |
| Valohai | MLOps for specialised models | Comparing and managing LLMs alongside specialised models |
| ZenML | Pipeline and agent engineering | Watching agents and proving fixes, with an open-source route |
Training runs or LLM traces: settle this first
The category has split in two. Model builders still need classic experiment tracking: which hyperparameters produced which loss curve, which dataset version went into which checkpoint, and which run should be promoted. That work happens in notebooks, training scripts and GPU clusters.
Teams building LLM apps and agents need something else. They want to see every call an agent made, what it was given, what it returned and whether the answer was any good. That's tracing and evaluation, and it lives next to the production app rather than the training cluster.
Several tools below now cover a bit of both, but each still leans one way. So ask your team which they do more of each week. If the honest answer is training models, compare the classic trackers and MLOps platforms. If it's shipping prompts, agents and retrieval, go straight to the observability group.
Classic tracking and open-source frameworks
MLflow is the long-standing open-source choice, now pitched as an AI platform for agents, LLMs and models and backed by the Linux Foundation. Metaflow is an open-source framework for building and managing real-life ML and data science projects, and it's more about structuring the whole project than only logging runs. Trackio is the lightest option here: a free, local-first tracking library from Hugging Face that suits researchers who want something simple.
MLOps and pipeline platforms
If tracking is only one part of what you need, these platforms reach further into infrastructure. ClearML manages GPU clusters alongside the AI workflow. Polyaxon is an open-source control plane for scheduling, running, debugging and observing workloads on your own Kubernetes clusters. Valohai is an MLOps platform for products that combine LLMs with specialised models. ZenML describes itself as an always-on AI engineering team that watches your agents and proves fixes work, with an open-source route.
These are bigger commitments than a tracker. They shape how your team runs jobs, so involve whoever looks after your clusters before you shortlist one.
Observability for LLM apps and agents
Langfuse is an open platform for tracing, evaluating and improving AI agents and LLM calls, with open-source agent evals. Arize AI covers agent observability, evaluation, tracing and experimentation, and the vendor says it's built on open standards including OpenInference. Comet bridges both worlds: it's an AI developer platform and the creator of Opik, an open-source LLM observability project.
Open standards matter here more than in classic tracking. If your traces follow a shared format, moving between observability tools later is far less painful.
What each tool is for, A to Z
Arize AI
Best for: Teams shipping AI agents who need observability, evaluation, tracing and experimentation, built on open source and open standards such as OpenInference, per the vendor.
ClearML
Best for: Organisations that want GPU cluster management and the AI workflow in one infrastructure platform, which the vendor describes as a three-layer approach.
Comet
Best for: ML and LLM teams wanting an AI developer platform from the creator of Opik, which turns LLM trace data into fixes, as the vendor puts it.
Langfuse
Best for: Developers who want an open platform for tracing, evaluating and improving AI agents and LLM calls, with open-source agent evals.
Metaflow
Best for: Data scientists after an open-source framework for real-life ML, AI and data science projects, which the vendor says Netflix open sourced.
MLflow
Best for: Teams wanting an open-source AI platform for agents, LLMs and models under the Apache license, backed by the Linux Foundation.
Polyaxon
Best for: Infrastructure teams on Kubernetes who want an open-source control plane to schedule, run, debug and observe AI and ML workloads on their own clusters.
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Trackio
Best for: Researchers and hobbyists who want a lightweight, local-first and free experiment tracking library built by Hugging Face.
Valohai
Best for: AI product teams combining LLMs with specialised models who want to compare, evaluate and manage them in one MLOps platform.
ZenML
Best for: Engineers wanting something the vendor calls an always-on AI engineering team that watches agents and proves fixes work, with a free or open-source start.
My advice on making the switch
Don't choose on the dashboard screenshots. Choose on the job your team does most often, then on whether you want to run the tool yourself. Open source is attractive, and several of the strongest options here are open source, but it moves the work into hosting, upgrades and upkeep. Somebody on your team will own that, so name them before you decide.
Check that a project's still moving, too. A few open-source projects in this space look quiet, so open the repository and look at recent releases and issue replies before you build your workflow around it. A tool that hasn't shipped in a while may still be fine, but you'll want to know.
Then do the one test that matters: log one real training run, or one real agent trace, in two shortlisted tools, using your own code rather than a quickstart. You'll see within an afternoon which one your engineers find natural. If your work is mostly on the LLM side, my guide to LLM evaluation tools goes deeper on testing outputs, and for getting models into production see my list of ML model deployment platforms. If features and training data are the pain, my feature store round-up is the next read.
Whatever you pick, export your existing runs and reports before you switch off the old tool, so your team's history comes with you.
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Frequently asked questions
What's the difference between experiment tracking and LLM observability?
Experiment tracking records training runs: settings, metrics and model versions. LLM observability records what an app or agent did in use: each call, its inputs, its outputs and how good the answer was.
Is open-source experiment tracking free to run?
The software is free, but you'll need servers, storage and someone to upgrade and watch it. For a small team, a hosted option can be less work, while larger teams often prefer control.
Can I move my old runs to a new tracker?
Usually you can export runs, metrics and artefacts, but the format and depth vary by tool. Export everything before you cancel, and check what the new tool can import.
Do I need an MLOps platform or just a tracker?
If you only need to compare runs, a tracker is enough. If you also need to schedule jobs, manage GPUs or run pipelines, an MLOps platform covers more, but it's a bigger change to how your team works.
What's OpenInference?
It's an open set of conventions for recording traces from AI apps, so different observability tools can read the same data. Using an open format makes switching tools later easier.
How do I check whether an open-source project is still active?
Look at the repository's recent releases, commits and how quickly issues get replies. A project with regular releases and responsive maintainers is a safer base for your workflow.
