MLOps Tools Comparison 2026: The Complete Stack Guide
The MLOps ecosystem has matured significantly. This guide compares the leading tools in every MLOps category to help you build the right stack for your team.
The Complete MLOps Stack
┌─────────────────────────────────────────────────────────────┐
│ MLOps Tool Stack 2026 │
├─────────────────────────────────────────────────────────────┤
│ │
│ Data Layer Training Layer Serving Layer │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────┐ │
│ │ Data Version │ │ Experiment │ │ Model │ │
│ │ DVC/lakeFS │──────▶│ MLflow/W&B │───▶│ KServe │ │
│ │ Delta Lake │ │ │ │ BentoML │ │
│ └──────────────┘ └──────────────┘ └────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌────────────┐ │
│ │ Feature Store │ │ Orchestration│ │ Monitoring │ │
│ │ Feast/Tecton │ │ Kubeflow │ │ Evidently │ │
│ │ │ │ Airflow │ │ NannyML │ │
│ └──────────────┘ └──────────────┘ └────────────┘ │
└─────────────────────────────────────────────────────────────┘
Category 1: Experiment Tracking
| Tool | Type | UI | Autolog | Collaboration | Price | |------|------|-----|---------|--------------|-------| | MLflow | Open source | Good | sklearn, PyTorch, TF | Self-hosted | Free | | Weights & Biases | SaaS/Self-hosted | Excellent | Deep integration | Best-in-class | Free tier, $50/user/mo | | Neptune | SaaS | Good | Wide support | Good | Free tier, custom | | Comet ML | SaaS/Self-hosted | Good | Good | Good | Free tier, custom | | Aim | Open source | Modern | Growing | Self-hosted | Free |
Recommendation
- Open source default: MLflow — universal, no vendor lock-in, model registry included
- Best UI/UX: Weights & Biases — superior visualization and team features
- Budget-conscious: MLflow or Aim — fully self-hosted, zero cost
Category 2: Pipeline Orchestration
| Tool | ML-Native | Container Isolation | GPU | Setup | Best For | |------|-----------|-------------------|-----|-------|----------| | Kubeflow | Yes | Per-step | Native | High | K8s teams | | Airflow | No | Optional | Manual | Medium | Data teams | | Prefect | No | Optional | Manual | Low | Small teams | | Dagster | Partial | Optional | Manual | Low | Software-eng teams | | Vertex AI | Yes | Per-step | Managed | Low | GCP users | | SageMaker | Yes | Per-step | Managed | Low | AWS users |
Recommendation
- Kubernetes teams: Kubeflow Pipelines — ML-native, artifact tracking, GPU scheduling
- Existing Airflow: Stay with Airflow + KubernetesExecutor for ML tasks
- Small teams: Prefect — fastest path to production with minimal infrastructure
- Cloud-native: Vertex AI (GCP) or SageMaker (AWS) — managed, minimal ops
Category 3: Feature Stores
| Tool | Type | Online Store | Offline Store | Streaming | Price | |------|------|-------------|--------------|-----------|-------| | Feast | Open source | Redis, DynamoDB | S3, BigQuery | Push-based | Free | | Tecton | Managed | DynamoDB | S3/Snowflake | Native | Enterprise | | Hopsworks | Open source | RonDB | Hudi | Flink | Free/Enterprise | | Databricks Feature Store | Managed | DynamoDB | Delta Lake | Spark | Databricks | | Vertex AI Feature Store | Managed | Bigtable | BigQuery | Dataflow | GCP |
Recommendation
- Open source: Feast — most flexible, any cloud, growing community
- Enterprise real-time: Tecton — best streaming support, managed
- Databricks users: Databricks Feature Store — tight integration
Category 4: Model Serving
| Tool | Protocol | Auto-scale | GPU | Complexity | Best For | |------|----------|-----------|-----|-----------|----------| | KServe | REST/gRPC | Built-in | Native | Medium | K8s prod | | Seldon Core | REST/gRPC | Built-in | Native | High | Complex inference graphs | | BentoML | REST/gRPC | BentoCloud | Supported | Low | Easy packaging | | Triton | REST/gRPC | Manual | Optimized | High | GPU-heavy inference | | TF Serving | REST/gRPC | Manual | Native | Low | TensorFlow only | | FastAPI | REST | Manual/K8s | Manual | Low | Flexible, custom |
Recommendation
- Kubernetes production: KServe — canary deployments, auto-scaling, multi-model
- Quick deployment: BentoML — simplest path from model to API
- GPU optimization: Triton — best for large model inference
- Maximum flexibility: FastAPI + custom — when you need full control
Category 5: Model Monitoring
| Tool | Type | Drift Detection | Explainability | Alerting | Price | |------|------|----------------|---------------|----------|-------| | Evidently | Open source | Statistical + ML | SHAP | Webhooks | Free | | NannyML | Open source | CBPE, DLE | Limited | Webhooks | Free/Cloud | | Arize | SaaS | Advanced | Built-in | Multi-channel | Free tier | | WhyLabs | SaaS/Self-hosted | Statistical | Limited | Slack/PD | Free tier | | Fiddler | SaaS | Advanced | Advanced | Multi-channel | Enterprise |
Recommendation
- Open source: Evidently — most comprehensive, great reports
- Performance estimation (no labels): NannyML — unique CBPE algorithm
- Enterprise SaaS: Arize — best debugging workflows
Category 6: Data Versioning
| Tool | Architecture | Branching | Best For | |------|-------------|-----------|----------| | DVC | Git extension | Git branches | ML experiments | | lakeFS | Git-like server | Native branches | Data lake management | | Delta Lake | Storage layer | Time travel | Spark/analytics |
Stack Recommendations by Team Size
Solo/Small Team (1-3 ML engineers)
Experiment Tracking: MLflow (local or SQLite backend)
Orchestration: Prefect (or simple cron + scripts)
Feature Store: Skip (compute features in pipeline)
Model Serving: BentoML or FastAPI
Monitoring: Evidently (reports)
Data Versioning: DVC
Total Cost: $0 (all open source)
Medium Team (4-10 ML engineers)
Experiment Tracking: MLflow (PostgreSQL + S3)
Orchestration: Kubeflow Pipelines or Airflow
Feature Store: Feast
Model Serving: KServe
Monitoring: Evidently + Grafana dashboards
Data Versioning: DVC + lakeFS
Total Cost: Infrastructure only (~$500-2K/mo)
Enterprise (10+ ML engineers)
Experiment Tracking: MLflow or Weights & Biases
Orchestration: Kubeflow Pipelines + Airflow (data eng)
Feature Store: Tecton or Feast (managed)
Model Serving: KServe + Triton (GPU-heavy)
Monitoring: Evidently + Arize (debugging)
Data Versioning: lakeFS + Delta Lake
Governance: Custom + MLflow Model Registry
Total Cost: $5-20K/mo (tooling + infra)
Key Selection Criteria
When choosing tools, prioritize:
- Interoperability — Tools should work together (MLflow + Kubeflow + Feast is a proven combination)
- Managed vs. Self-hosted — Managed saves ops time, self-hosted gives control
- Lock-in risk — Prefer open-source cores with optional managed layers
- Team skills — Choose tools your team can maintain, not the "best" tool
- Growth path — Start simple, add complexity only when needed
Related Resources
- What is MLOps? — Framework for understanding where tools fit
- MLOps best practices — How to use these tools effectively
- MLflow deep dive — Hands-on tutorial
- Kubeflow deep dive — Pipeline orchestration tutorial
- Feature store guide — Architecture patterns
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