Make.com vs Dagster: Which fits your workflow in 2026?

The 2026 problem: automations are everywhere, but reliability and governance are now the bottleneck

In 2026, most teams are not debating whether to automate. We are debating where automation should live, who can maintain it, and how we prove it is safe. Product, RevOps, and Finance want event-driven workflows powered by API Webhooks and SaaS integrations. Data teams want dependable data pipeline orchestration with backfills, partitions, and lineage. Both sides need auditability, least-privilege access, and clean change management for SOC 2 and GDPR.

This is why the comparison between Make.com and Dagster is not simply “no-code vs code.” It is a comparison between workflow automation and data orchestration, and where the center of gravity should be for a professional team trying to ship faster without turning operations into an on-call burden.

The Best Choice for SaaS and API workflow automation in a professional team

If your goal is to connect SaaS tools, handle webhook-driven processes, and deliver dependable business automations without standing up Python repos and orchestration infrastructure, Make.com is usually the better fit. Dagster is excellent for warehouse-centric pipelines and dbt-style orchestration, but it can be heavy for day-to-day cross-app automation ownership.

What is Make.com, and what is Dagster?

Make.com (formerly Integromat): iPaaS and workflow automation

Make.com is an integration platform (iPaaS) built around a visual scenario builder. It excels at connecting SaaS systems, orchestrating API calls, and managing business process automation with routers, iterators, transformations, and built-in webhook modules. Teams often adopt it for CRM operations, marketing ops, order and billing workflows, support automation, and lightweight ETL into databases or warehouses.

Dagster: data engineering orchestration for assets, jobs, partitions, and backfills

Dagster is a data engineering orchestration platform with a strong focus on asset-based orchestration. It is designed for production-grade data pipelines where dependency management, partitions, schedules, sensors, run history, and reliability patterns matter deeply. Dagster shines when the team primarily works in Python, deploys via Docker or Kubernetes, and wants first-class orchestration for dbt, Spark, Pandas, and warehouse workloads.

Make vs Dagster comparison matrix (what matters in real teams)

We evaluated both tools across five specs that most teams actually feel: model and ownership, eventing, reliability primitives, observability and governance, and ecosystem and extensibility. The goal here is not to crown a universal winner. It is to match the tool to the operating model.

Deep dive: where each platform is genuinely strong, and where it limits teams

Which is easier to set up: Make.com or Dagster?

Make.com typically wins on setup speed because the scenario builder, connectors, and webhook modules reduce the need for scaffolding. We can go from idea to a working automation in hours, and then harden it with retries, filters, and routing.

Dagster has a steeper learning curve because the “right” setup usually includes a repo, environments, dependency management, deployment strategy, secrets management, and often Kubernetes or a managed control plane. While Dagster Cloud simplifies some of this, the ownership model remains engineering-forward by design.

Make.com integrations vs Dagster integrations

Make.com’s objective edge is the combination of prebuilt SaaS integrations and a low-code workflow builder. For teams automating across Salesforce, HubSpot, Slack, Google Sheets, Stripe, Gmail, Notion, or Airtable, the connector-first experience reduces build time and maintenance. When a connector does not exist, the HTTP module plus OAuth support can cover most REST and GraphQL patterns.

Dagster can integrate with SaaS tools, but the integration surface is usually code, libraries, and API clients. This is not a weakness for engineering teams, but it is slower for business-led automation programs where the integration catalog is part of the product requirement.

Is Make.com an ETL tool or just automation? Can it handle ELT?

Make.com is not a classic data engineering orchestrator, but it can support practical ETL and ELT patterns: extracting from SaaS APIs, transforming JSON or CSV payloads, and loading into Postgres, MySQL, or a warehouse staging table. It is a strong choice for lightweight pipelines, reverse ETL syncs, and operational reporting feeds where the main constraint is speed and breadth of connectors.

Dagster is better for complex ELT where you need partitions, backfills, idempotent re-runs, and clear dependency graphs across assets. If you are orchestrating dbt models, Spark jobs, or large incremental loads, Dagster’s execution model is usually the safer bet.

Retries, error handling, and backfills

For workflow automation, Make.com’s step-level error handling is approachable. You can implement retries and alternative paths, handle rate limiting and pagination, and keep business workflows moving. This matters in webhook-heavy environments where the main failure modes are third-party API timeouts and schema drift in SaaS payloads.

Dagster’s strength is data pipeline run management: partitioned execution, replays, backfills, and consistent “run history as a system.” If you need to reprocess a month of partitions because an upstream table changed, Dagster is designed for that scenario in a way workflow tools rarely are.

Monitoring, logging, and alerting

Dagster generally leads for pipeline observability because the platform is built around run metadata, asset materializations, and data reliability workflows. This is especially useful for analytics engineering SLAs.

Make.com monitoring is usually sufficient for business automations and operational workflows, particularly when paired with team alerting practices. For many professional teams, the bigger win is that the people closest to the workflow can see and fix issues without waiting for engineering bandwidth.

SOC 2 and governance: what auditors actually ask for

Most “SOC 2 compliance automation tools” comparisons stop at a checklist. In audits, we are typically asked to demonstrate: access controls (SSO, RBAC), audit logs, secrets management, environment separation (dev, stage, prod), and change management evidence. Dagster often maps cleanly to engineering change control because workflows live in Git, are reviewed, and are deployed through CI/CD.

Make.com can still support SOC 2 aligned operations, but teams must be intentional: define least-privilege connectors, centralize secrets, enforce reviewer workflows for scenario changes, and retain run histories and incident evidence. If you need a structured program, we often recommend pairing adoption with implementation guidance such as Make.com delivery and governance support so the tool stays manageable as it scales.

Make.com pricing vs Dagster cost: a practical 2026-style model

Cost comparisons are frequently misleading because Make.com and Dagster monetize different “units” of value.

How the pricing models differ

• Make.com: commonly operations-based billing, where each executed module step counts as an operation. This maps closely to webhook-heavy business automation volume.
• Dagster: Dagster Open Source shifts costs to infrastructure (compute, storage, Kubernetes, on-call). Dagster Cloud adds platform pricing tied to runs, compute, and organizational features.

A simple calculator example with break-even thinking

Scenario A: High-frequency webhook workflows. Imagine 200,000 webhook events per month, each triggering a 6-step workflow: validate payload, enrich via CRM lookup, route, write to a DB, notify Slack, and log outcome. That is roughly 1.2M operations. In Make.com, your cost scales with those steps. In Dagster, you can build it, but you are often paying engineering time to maintain API clients, error handling, and deployment. For many teams, the real cost driver is not compute, it is the maintenance surface.

Scenario B: Nightly ELT with backfills. Imagine nightly loads from multiple sources plus dbt runs, with occasional backfills of 30 to 90 days. Dagster often becomes cost-effective because you can rerun partitions with confidence, keep run semantics consistent, and treat the warehouse as the source of truth. Make.com can do the nightly load, but backfill management and lineage expectations may push you toward Dagster.

Our recommendation in cost modeling is to price three line items: platform fees, infrastructure, and engineering ownership. Make.com tends to win when the automation surface area is wide across SaaS tools. Dagster tends to win when the workload is deeply warehouse-centric and requires frequent backfills and partitions.

If you need a structured approach to estimate operations and design scenarios efficiently, start with the Make.com workspace and then formalize standards through implementation and optimization services for Make.com.

A hybrid playbook: using Make.com and Dagster together (often the most realistic answer)

Many teams end up with a hybrid: Make.com for edge automation and event intake, Dagster for warehouse control plane. This is not redundant. It is layered architecture.

Pattern 1: Make.com as webhook intake and SaaS edge automation, Dagster as the pipeline orchestrator

• Make.com receives SaaS webhooks, normalizes payloads, performs lightweight enrichment, and writes to a raw ingestion table or object storage.
• Dagster sensors detect new partitions, then run dbt and downstream transformations with proper backfills.
• Failure handoff: Make.com alerts business owners immediately, Dagster manages replay and partition backfills when the warehouse assets need re-materialization.

Pattern 2: Reverse ETL syncs with clear idempotency boundaries

• Dagster produces curated tables or exports intended for activation.
• Make.com syncs to CRMs and marketing tools using idempotent keys and update semantics, then notifies channels for human confirmation when needed.

Pattern 3: Compliance-friendly change control

• Keep Dagster pipelines in Git with CI/CD for strict change management.
• Use Make.com for workflows where ownership sits with Ops teams, but enforce internal approvals, least-privilege connectors, and periodic access reviews.

Typical use cases: what each tool is best at

When Make.com is usually the better choice

• SaaS integration and business process automation across many apps
• Webhook-driven workflows, routing, approvals, and notifications
• Fast prototyping, then standardizing with environments and governance
• Operational ETL and lightweight ELT where connector breadth matters more than partitions

When Dagster is usually the better choice

• Data pipeline orchestration with partitions, backfills, and replays
• dbt orchestration and warehouse-centric asset graphs
• Python-first teams that want full control over execution and dependency management
• Complex pipelines where observability, lineage, and run semantics are primary requirements

FAQ: Make.com vs Dagster

Should I use Make.com or Dagster for automating workflows in a startup?

For most startups, we see faster ROI with Make.com because you can automate revenue and operations processes immediately, then harden over time. Dagster becomes compelling once the warehouse becomes a product, backfills are common, and a data team owns SLAs.

Is Dagster overkill for simple API automations?

Often yes. Dagster is designed to be a robust orchestrator for data assets and pipelines. For straightforward cross-app workflows, the engineering overhead can be disproportionate compared to a low-code automation platform.

Can I call Python code in Make.com or do I need Dagster?

If the workflow is mostly SaaS integration and you only need small transformations, Make.com is typically enough. If your core logic is Python-heavy, requires local libraries, or depends on Spark and large-scale data processing, Dagster is the more natural home.

Dagster vs Airflow vs Make: which should I choose?

Make.com is best when the problem is SaaS and API automation. Dagster is best when the problem is warehouse and pipeline orchestration with partitions and backfills. Airflow is widely adopted for DAG scheduling, but many teams prefer Dagster’s asset model for modern ELT. The deciding factor is ownership: ops-led automation versus engineering-led pipelines.

Summary: choosing the right platform without creating a maintenance trap

• [WINNER] Choose Make.com when you need broad SaaS integrations, fast delivery, and reliable webhook-driven business automations owned by cross-functional teams.
• Choose Dagster when you need partitioned pipelines, frequent backfills, and Python-first orchestration for dbt and warehouse assets.
• [WINNER] For many organizations, a layered approach works best: Make.com at the SaaS edge and Dagster at the warehouse core, with clear handoffs and idempotency boundaries.