MLflow
Privacy-first alternative
Why teams pick it
Keep customer data in-house with privacy-focused tooling.
Open-source alternatives to Weights & Biases
Compare community-driven replacements for Weights & Biases in mlops: experiment tracking & model registry workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
Weights & Biases
W&B lets teams log and compare experiments, version datasets and artifacts, run hyperparameter sweeps, and manage a unified model registry—plus LLM app tracing/monitoring with Weave/Traces and dozens of framework/cloud integrations. Available as SaaS or self-hosted.Read more
Key stats
- 7Alternatives
- 5Active development
Recent commits in the last 6 months
- 7Permissive licenses
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Weights & Biases.
Start with these picks
These projects match the most common migration paths for teams replacing Weights & Biases.
ORMB
Fastest to get started
Why teams pick it
Works with any OCI registry (Harbor, Docker Registry, etc.)
All open-source alternatives
ORMB
Version, share, and serve ML models via OCI image registry
Permissive licenseFast to deployIntegration-friendlyGo
Why teams choose it
- OCI‑compatible storage of ML/DL models as image artifacts
- Simple CLI for save, push, pull, and export operations
- Works with any OCI registry (Harbor, Docker Registry, etc.)
Watch for
No graphical user interface; CLI‑only interaction
Migration highlight
CI/CD pipeline integration
Automatically push trained models to a Harbor registry after each training run.
Metaflow
Human‑centric framework for building, scaling, and deploying AI systems
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Pythonic API for notebook‑first prototyping with built‑in experiment tracking
- Seamless horizontal and vertical scaling on AWS, Azure, GCP, and Kubernetes with CPU/GPU support
- One‑click deployment to production‑grade orchestrators and reactive workflow management
Watch for
Primarily Python‑centric, limiting non‑Python ecosystems
Migration highlight
Rapid notebook prototyping
Iterate quickly on new algorithms with built‑in versioning and visualizations, then promote the notebook to a reusable flow.
Aim
Track, visualize, and compare AI experiments effortlessly
Active developmentPermissive licenseFast to deployPython
Why teams choose it
- Beautiful UI for visualizing and comparing runs
- Python SDK for flexible metadata queries
- Built‑in converters for TensorBoard, MLflow, and Weights & Biases
Watch for
Requires self‑hosting and maintenance
Migration highlight
Training a deep translation model
Track loss, accuracy, and resource usage, visualize progress, and compare hyperparameter variations across runs.
ModelDB
Version, track, and manage ML models end-to-end
Permissive licenseFast to deployIntegration-friendlyJava
Why teams choose it
- Docker and Kubernetes ready deployments
- Python and Scala client libraries
- Interactive dashboards for performance reporting
Watch for
Requires infrastructure setup (Docker/K8s, database)
Migration highlight
Track experiments with hyperparameters and metrics
Data scientists log each run, compare accuracy, and reproduce results easily.
TensorZero
Unified, high-performance gateway for industrial-grade LLM applications
Active developmentPermissive licenseFast to deployRust
Why teams choose it
- Unified API accesses 30+ LLM providers with a single client
- Sub‑millisecond overhead enables >10k QPS at scale
- Built‑in observability stores inferences and feedback with UI and OpenTelemetry export
Watch for
Self‑hosting required; users must manage Docker and a database like ClickHouse
Migration highlight
Real‑time chat assistant with multi‑model fallback
Seamlessly route requests between OpenAI and Anthropic, maintaining sub‑millisecond latency and automatic retries on failures.
ClearML
Automagical suite to streamline AI experiment, orchestration, and serving
Active developmentPermissive licenseIntegration-friendlyPython
Why teams choose it
- Zero‑code experiment tracking with automatic environment capture
- Unified data versioning across S3, GCS, Azure, and NAS
- Scalable model serving with Nvidia‑Triton and built‑in monitoring
Watch for
Feature‑rich UI can be overwhelming for beginners
Migration highlight
Rapid prototyping to production
Track experiments, version datasets, and deploy a model endpoint in under 5 minutes, ensuring reproducibility.
MLflow
Unified platform for tracking, evaluating, and deploying AI models
Active developmentPermissive licensePrivacy-firstPython
Why teams choose it
- Unified experiment tracking, model registry, and deployment across ML and GenAI workloads
- Built‑in tracing and observability for LLM/agent applications
- Automated evaluation suite for LLMs with integrated metrics
Watch for
Advanced scaling may require additional infrastructure configuration
Migration highlight
Experiment tracking for scikit‑learn models
Automatic logging of parameters, metrics, and artifacts enables easy comparison across runs.
Choosing a mlops: experiment tracking & model registry alternative
Teams replacing Weights & Biases in mlops: experiment tracking & model registry workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
- 5 options are actively maintained with recent commits.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Weights & Biases.