MLOps Tools: The Complete Guide 2026
MLOps Tools: The Complete Guide 2026
MLOps applies DevOps principles to ML systems. Here are the essential tools for every stage of the ML lifecycle.
Categories & Tools
Experiment Tracking: Weights & Biases (most popular), MLflow (open-source), Neptune.ai, Comet ML
Model Registry: MLflow Model Registry, W&B Model Registry, SageMaker Model Registry
Feature Stores: Feast (open-source), Tecton, Hopsworks
Model Serving: vLLM (open-source, high performance), Triton Inference Server (NVIDIA), Seldon Core, BentoML
Monitoring: Arize AI, WhyLabs, Fiddler AI, Arthur AI
Orchestration: Kubeflow (open-source), Airflow, Prefect, ZenML (open-source)
Recommended Stack
| Team Size | Recommended Stack |
|---|---|
| Startups | MLflow + Feast + BentoML + WhyLabs |
| Enterprise | W&B + Tecton + vLLM + Arize |
| Budget (all open-source) | MLflow + Feast + vLLM + Evidently |
Key Trends in MLOps 2026
- LLM Operations (LOps): MLOps adapted for large language models — prompt management, RAG pipelines, model routing
- Edge Deployment: Running optimized models on edge devices (phones, IoT, vehicles)
- Observability: Moving beyond monitoring to full observability — tracing, logging, metrics for ML systems
- Cost Optimization: Dynamic model routing, quantization, and caching to reduce inference costs
FAQ
Q: Do I need MLOps for a small team?
A: Start with experiment tracking (MLflow or W&B) and basic monitoring. Scale up as your model count and team grow.
Q: Open-source or commercial?
A: Both work. Open-source gives more control. Commercial gives faster setup and support. Many teams use a mix.
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