Seldon pricing guide details
Pricing Overview
Seldon operates on an enterprise pricing model with custom quotes for its commercial products. The platform splits into two distinct offerings: Seldon Core, an open-source Kubernetes-native model serving framework available at no cost, and Seldon Deploy, the commercial enterprise MLOps platform that requires contacting sales for pricing. There are no self-serve pricing tiers or published rate cards. We find this approach common among enterprise MLOps vendors targeting large-scale production deployments where infrastructure requirements vary significantly between organizations.
The commercial value proposition comes through Seldon Deploy, which adds enterprise features like model monitoring, drift detection, explainability dashboards, and managed deployment pipelines on top of the Core engine. All commercial pricing requires a direct conversation with the Seldon sales team.
Plan Comparison
| Feature | Seldon Core (Open Source) | Seldon Deploy (Enterprise) |
|---|---|---|
| Price | Free | Contact Sales |
| Model Serving | Kubernetes-native inference | Managed inference with autoscaling |
| Deployment Method | Self-managed on K8s | Managed enterprise deployment |
| Model Monitoring | Not included | Drift detection and performance monitoring |
| Explainability | Basic (via Alibi library) | Full explainability dashboards |
| A/B Testing | Manual configuration | Built-in canary and A/B testing |
| Multi-Model Serving | Supported | Supported with enterprise orchestration |
| Support | Community only | Dedicated enterprise support |
| SLA | None | Custom SLA included |
| SSO / RBAC | Not included | Enterprise identity management |
| Audit Logging | Not included | Full audit trail |
The gap between the free and enterprise tiers is substantial. Seldon Core gives teams a production-grade serving layer, but organizations needing monitoring, governance, and dedicated support must move to Seldon Deploy.
Hidden Costs
While Seldon Core is free to use, the total cost of ownership extends well beyond licensing. We highlight several cost factors that teams should budget for:
- Kubernetes infrastructure: Seldon requires a running Kubernetes cluster. On major cloud providers, a production-grade cluster with enough capacity for model serving typically starts at $200-500/month for compute alone, scaling significantly with model size and traffic.
- Engineering overhead: Self-managing Seldon Core on Kubernetes demands DevOps and MLOps expertise. Teams without in-house Kubernetes experience will face steep learning curves or need to hire specialized engineers.
- Monitoring tooling: Without Seldon Deploy, teams need to build or buy separate monitoring, alerting, and drift detection solutions. Tools like Prometheus, Grafana, and custom pipelines add both cost and maintenance burden.
- GPU costs: Serving large ML models often requires GPU instances, which run $0.50-3.00+/hour on cloud providers depending on the GPU type. This can become the dominant cost factor for inference-heavy workloads.
- Storage and networking: Model artifacts, logging data, and inter-service communication within Kubernetes all incur cloud storage and data transfer charges.
For Seldon Deploy customers, the enterprise license fee is negotiated based on deployment scale, but the underlying infrastructure costs remain the customer's responsibility.
How Seldon Pricing Compares
We compared Seldon's pricing model against leading MLOps competitors to show where it sits in the market.
| Platform | Pricing Model | Starting Price | Free Tier | Key Differentiator |
|---|---|---|---|---|
| Seldon | Enterprise (Contact Sales) | Custom quote | Seldon Core (open source) | Kubernetes-native serving with optional enterprise layer |
| Weights & Biases | Freemium | $60/month (Pro) | Yes, limited usage | Experiment tracking and model registry focus |
| Gemini Enterprise Agent Platform (formerly Vertex AI) | Usage-Based | $0.49/node-hour (training) | Free tier for some services | Fully managed Google Cloud ML platform |
| Azure Machine Learning | Usage-Based | $0.10/hour (compute) | Studio free tier | Integrated Microsoft ecosystem with managed endpoints |
Seldon's pricing structure differs fundamentally from the hyperscaler MLOps offerings. Where Gemini Enterprise Agent Platform and Azure ML charge per compute hour with transparent rate cards, Seldon bundles its enterprise features into custom contracts. This makes direct cost comparison difficult without a specific quote, but we can draw several conclusions.
For teams already running Kubernetes and needing only model serving, Seldon Core at zero licensing cost competes favorably against managed alternatives. A small inference workload on Gemini Enterprise Agent Platform at $0.0612/node-hour could cost $45-90/month just for prediction serving, while Seldon Core on an existing cluster adds no incremental licensing expense.
However, once teams need the full enterprise feature set, Seldon Deploy's custom pricing likely lands in the range typical of enterprise MLOps contracts. Weights & Biases offers more pricing transparency with its $60/month Pro tier, though it focuses on experiment tracking rather than model serving. Azure ML provides a pay-as-you-go model starting at $0.10/hour for compute instances, offering more predictable budgeting for teams that prefer usage-based billing over annual contracts.
The bottom line: Seldon is strongest for organizations with existing Kubernetes infrastructure that want open-source flexibility for model serving and are willing to negotiate enterprise terms for advanced monitoring and governance capabilities.