38 Tools Comparison

Chapter 38: MLOps Tools Comparison — Master Reference🔗

"The right tool for the right job. Use this chapter to make informed decisions."

38.1 Experiment Tracking Tools🔗

Feature MLflow Weights & Biases Neptune.ai ClearML Comet ML
Hosting Self/Cloud Cloud (free tier) Cloud ($) Self/Cloud Cloud ($)
Setup Minutes Seconds Minutes Minutes Minutes
Auto-logging ✅ sklearn, TF, PT ✅ Many frameworks ✅ Many ✅ Auto ✅ Auto
HPO / Sweeps Basic ✅ Advanced Bayesian ✅ Yes ✅ Yes ✅ Yes
Collaboration Limited ✅ Rich (reports) ✅ Good ✅ Good ✅ Good
Model Registry ✅ Full ✅ Enterprise ✅ Yes ✅ Yes ✅ Yes
LLMOps ✅ MLflow AI ✅ Native Limited Limited Limited
GCP Integration Yes Yes Yes Yes Yes
Cost Free + infra Free→$50/user/mo $99+/user/mo Free/enterprise Free→$179/mo
Best for Open source teams Research, GenAI Enterprise Self-hosted Any

38.2 Pipeline Orchestration Tools🔗

Feature Airflow Kubeflow Prefect Vertex AI Pipelines ZenML
Paradigm DAG-based K8s-native Python flows KFP on GCP Stack-based
Language Python Python (KFP SDK) Python Python (KFP SDK) Python
Scheduling ✅ Rich cron Limited ✅ Yes ✅ Yes Via backend
K8s native Partial ✅ Yes Partial ✅ Yes Partial
ML-specific Operators only ✅ Yes ✅ Yes ✅ Yes ✅ Yes
UI Good Good ✅ Excellent Good Good
GCP managed Cloud Composer Via GKE Cloud Run ✅ Native -
Learning curve Medium High Low Medium Low
Best for Enterprise, data eng ML on K8s Python-first GCP teams Multi-cloud

38.3 Model Serving Frameworks🔗

Framework Models Supported Protocol Scaling GCP
FastAPI / Flask Any (custom) REST Manual/K8s HPA K8s on GKE
TF Serving TensorFlow gRPC/REST K8s Vertex AI
TorchServe PyTorch REST/gRPC K8s GKE
Triton (NVIDIA) TF, PyTorch, ONNX gRPC/HTTP K8s (GPU) GKE + GPU
Seldon Core Any REST/gRPC/KfServer ✅ Native K8s GKE
KServe Any (InferenceService) REST/gRPC ✅ K8s native GKE
Ray Serve Any REST ✅ Ray cluster GKE
Vertex AI Endpoint Most formats REST ✅ Managed ✅ Native
BentoML Any REST Docker/K8s GKE

38.4 Data Validation Tools🔗

Tool Type Scale GCP Integration
Great Expectations Rule-based Medium Airflow operators
TFDV Statistical Large (Beam/Spark) ✅ Vertex AI TFX
Pandera DataFrame schema Small-medium Any Python
Deequ Spark-based Very large Dataproc
Soda SQL-based Medium Cloud Composer
Whylogs Profiling Any -

38.5 Feature Store Comparison🔗

Feature Feast Vertex AI FS Hopsworks Tecton Databricks FS
Type Open Source Managed GCP Commercial Commercial Managed
Online Serving Redis/Datastore ✅ Managed ✅ Managed ✅ Managed ✅ Managed
Streaming Kafka Pub/Sub Kafka Kafka Kafka
GCP native Good ✅ Best Good Good Limited
Cost Free + infra ~$0.05/node/hr Enterprise $ Enterprise $ Included

38.6 Monitoring Tools🔗

Tool Type What It Monitors GCP
Prometheus Metrics DB System + custom metrics K8s native
Grafana Dashboard Any Prometheus metrics K8s native
Evidently AI ML-specific Data/model drift reports Python/Airflow
Alibi-Detect ML-specific Drift, outliers, adversarial Python
Fiddler AI Commercial Full ML observability Any
Arize AI Commercial Production ML monitoring Any
Vertex AI Monitoring Managed Skew + drift on Vertex endpoints ✅ Native
WhyLabs Commercial Profiling + drift Any

38.7 CI/CD Tools for ML🔗

Tool Hosting ML-specific Ease GCP
GitHub Actions GitHub cloud Via marketplace ✅ Easy Cloud Build integration
Jenkins Self-hosted Via plugins Medium Any
GitLab CI GitLab/self Via runners ✅ Easy GKE runner
Cloud Build ✅ GCP managed Docker + K8s Medium ✅ Native
Tekton K8s native K8s pipelines Hard ✅ GKE
ArgoCD K8s GitOps CD only Medium ✅ GKE

38.8 Cloud Platform Comparison (ML Focus)🔗

Service Category GCP AWS Azure
ML Platform Vertex AI SageMaker Azure ML
AutoML Vertex AI AutoML SageMaker Autopilot Azure AutoML
Notebooks Vertex Workbench SageMaker Studio Azure ML Studio
Pipelines Vertex AI Pipelines (KFP) SageMaker Pipelines Azure ML Pipelines
Feature Store Vertex AI Feature Store SageMaker Feature Store Azure ML Feature Store
Model Registry Vertex Model Registry SageMaker Model Registry Azure ML Model Registry
Serving Vertex AI Endpoints SageMaker Endpoints Azure ML Endpoints
Monitoring Vertex AI Monitoring SageMaker Model Monitor Azure ML Monitoring
Container Registry Artifact Registry (GAR) ECR ACR
K8s GKE EKS AKS
Object Storage GCS S3 Azure Blob
Data Warehouse BigQuery Redshift Synapse
Workflow Cloud Composer (Airflow) MWAA (Managed Airflow) Azure Data Factory
LLM Gemini, Model Garden Bedrock Azure OpenAI

38.9 Decision Guide: Picking Tools🔗

WHAT IS YOUR TEAM SIZE?
  1-5 people:
    → Start simple: GitHub Actions + MLflow (local) + Docker + Cloud Run
    → Don't over-engineer

  5-20 people:
    → GitHub Actions + MLflow Server + Docker + GKE + Airflow
    → Consider W&B for experiment tracking (better collab)

  20+ people:
    → Full platform: Vertex AI Pipelines + Vertex AI Feature Store
    → Enterprise feature store (Tecton/Hopsworks)
    → Dedicated MLOps engineers

WHERE IS YOUR WORKLOAD?
  GCP-first:  → Vertex AI everything
  Multi-cloud: → Open source (MLflow + Airflow + Feast + Kubeflow)
  AWS:         → SageMaker
  On-prem:     → Kubeflow + MLflow self-hosted

WHAT IS YOUR ML TYPE?
  Traditional ML: → MLflow + sklearn/XGBoost
  Deep Learning:  → W&B + PyTorch + Triton serving
  LLMs:           → LangChain + W&B + vLLM or Vertex AI
  Computer Vision: → W&B + PyTorch + Triton

Go back to README for full table of contents.