Dagster vs Redpanda Pricing (2026)

How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.

Dagster Redpanda
Starts at $10/mo Custom
Number of plans 3 3
Free plan
Free trial
Pricing model hybrid hybrid

Solo Plan

$10/mo
  • Pay-as-you-go $0.040/credit
  • Serverless compute $0.010/minute
  • 1 User
  • 1 Code location
  • 1 Deployment
  • 30-day free trial
  • Asset-based orchestration
  • Workflow-based orchestration
  • Local development workflow (dg CLI)
  • Components
  • Declarative Automation
  • Partitions and backfills
  • Branch deployments
  • Virtual assets (Preview)
  • Asset checks
  • Freshness policies (new API, now GA)
  • Integration with dbt tests
  • Asset Health status
  • Asset metadata in UI
  • Asset-level lineage
  • Platform and pipeline metrics (30-day lookback)
  • Materialization success rate, failure count, step execution time
  • Metrics-based alerts
  • Dagster skills for Claude Code, Cursor, Codex (open source)
  • Dagster+ MCP server (Preview)
  • Alerts: email, Slack, Microsoft Teams
  • Alert policy types: asset, run, code location, automation, agent downtime
  • Serverless deployments
  • Hybrid deployments — ECS, Kubernetes, Docker, Azure, local
  • Terraform and Pulumi providers
  • SOC 2 Type II
  • GDPR
  • Data portability
  • Dagster community
  • Support Center and Ask AI
  • Billing support

Starter Plan

$100/mo
  • Pay-as-you-go $0.035/credit
  • Serverless compute $0.010/minute
  • Up to 3 Users
  • 5 Code locations
  • 1 Deployment
  • Catalog Search
  • 30-day free trial
  • Asset-based orchestration
  • Workflow-based orchestration
  • Local development workflow (dg CLI)
  • Components
  • Declarative Automation
  • Partitions and backfills
  • Branch deployments
  • Virtual assets (Preview)
  • Asset checks
  • Freshness policies (new API, now GA)
  • Integration with dbt tests
  • Asset Health status
  • Asset metadata in UI
  • Asset-level lineage
  • Data Column Lineage
  • Catalog search
  • Platform and pipeline metrics (30-day lookback)
  • Materialization success rate, failure count, step execution time
  • Metrics-based alerts
  • Dagster skills for Claude Code, Cursor, Codex (open source)
  • Dagster+ MCP server (Preview)
  • Alerts: email, Slack, Microsoft Teams
  • Alert policy types: asset, run, code location, automation, agent downtime
  • Role-based access control
  • RBAC scope: Per deployment
  • Assignable roles: View, Catalog Viewer, Launcher, Editor, Admin
  • Serverless deployments
  • Hybrid deployments — ECS, Kubernetes, Docker, Azure, local
  • Terraform and Pulumi providers
  • SOC 2 Type II
  • GDPR
  • Data portability
  • Dagster community
  • Support Center and Ask AI
  • Billing support

Pro Plan

Custom
  • Custom Serverless compute
  • Unlimited code locations
  • Unlimited deployments
  • Cost Tracking and Insights
  • Personalized Onboarding Support
  • Private Slack channel
  • Uptime SLAs
  • Custom Security Questionnaires
  • Asset-based orchestration
  • Workflow-based orchestration
  • Local development workflow (dg CLI)
  • Components
  • Declarative Automation
  • Partitions and backfills
  • Branch deployments
  • Virtual assets (Preview)
  • Asset checks
  • Freshness policies (new API, now GA)
  • Integration with dbt tests
  • Asset Health status
  • Asset metadata in UI
  • Asset-level lineage
  • Data Column Lineage
  • Catalog search
  • BI Tool Cataloguing
  • Connections (Preview)
  • Platform and pipeline metrics (30-day lookback)
  • Materialization success rate, failure count, step execution time
  • Metrics-based alerts
  • Custom metrics from asset metadata
  • Cost tracking for BigQuery and Snowflake
  • Dagster skills for Claude Code, Cursor, Codex (open source)
  • Dagster+ AI chat
  • Issues
  • AI proactive monitoring and auto implement
  • Dagster+ MCP server (Preview)
  • Alerts: email, Slack, Microsoft Teams
  • Alert policy types: asset, run, code location, automation, agent downtime
  • Webhook alerts
  • PagerDuty and Datadog
  • Alerting asset owners
  • Role-based access control
  • RBAC scope: Granular roles, per project
  • Assignable roles: View, Catalog Viewer, Launcher, Editor, Admin
  • SSO
  • SAML
  • SCIM provisioning — Okta, Entra ID
  • Audit logs
  • Serverless deployments
  • Hybrid deployments — ECS, Kubernetes, Docker, Azure, local
  • Terraform and Pulumi providers
  • Agent queues — multi-agent routing, multi-region failover
  • EU data residency (Dagster+ EU)
  • SIEM log export
  • SOC 2 Type II
  • GDPR
  • Data portability
  • HIPAA
  • Custom security questionnaires
  • Dagster community
  • Support Center and Ask AI
  • Billing support
  • Dedicated Slack or Microsoft Teams channel
  • Premium support
  • Uptime SLAs

Serverless

$0/mo
  • Multi-tenant AWS, GCP
  • 100 MB/s max write throughput
  • 300 MB/s max read throughput

Bring Your Own Cloud

Custom
  • In your cloud AWS, Azure, GCP
  • 2 GB/s max write throughput

Enterprise Edition

Custom
  • Built-in HTTP proxy & schema registry
  • Prometheus support

Dagster vs Redpanda FAQ

Which one is cheaper?
One or both use custom pricing, so it depends on your specific needs.
Can I use either one for free?
Redpanda has a free plan. Dagster doesn't — though they do offer a free trial.
How do they charge?
Both use a hybrid model, so the comparison is straightforward — it comes down to features and limits at each price point.
Which one is a better deal?
Depends on what you need. Dagster: At $10 and $100 entry points, Dagster undercuts most orchestration and data platform competitors that charge per-seat or start pricing conversations well north of $500/mo. They're clearly fishing for indie developers and small data teams who'll self-serve now and hopefully graduate into Pro as pipelines multiply. Redpanda: Redpanda plays as the performance-focused alternative to Kafka and Confluent, leaning on 'no ZooKeeper, no JVM' as a technical wedge rather than a price wedge. The free serverless tier undercuts Confluent's more enterprise-heavy sales motion for small teams, but once you're in BYOC or Enterprise territory, pricing is opaque and clearly aimed at matching (not undercutting) what large data platform teams already expect to pay.

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