Make is a visual, no-code workflow automation platform for connecting apps, data, and AI logic via a drag-and-drop scenario canvas; SQLMesh is an open source…
Best for Make: Business teams and no-code builders wanting to connect CRMs, spreadsheets, email tools, and e-commerce platforms via a visual, branching flowchart canvas, starting with a free plan and going up to Core from around $9/month.
Best for SQLMesh: Data engineers and analytics engineers who write SQL or Python models, want dbt-compatible pipelines, and need virtual data environments plus a Terraform-style Plan/Apply workflow to preview changes before deploying to a warehouse.
At a Glance
Make
SQLMesh
Primary category
Automation
Automation
Rating
Not documented
Not documented
Pricing model
Freemium
open-source
Starting price
Free (paid plans from $9/month)
Free (open-source); Tobiko Cloud is custom priced
Free plan
Yes
Yes
Free trial
Not documented
Not documented
Platforms
Not documented
Not documented
Team collaboration
Not documented
Not documented
AI features
Not documented
Not documented
Public API
Yes
Yes
Key Differences
Product Category
Make: A no-code visual workflow and app-integration automation platform.
SQLMesh: A SQL/Python data transformation framework for warehouse pipelines.
They solve different problems (app automation versus data modeling), so the choice usually isn't either/or.
Pricing Model
Make: Freemium with a limited free plan, then Core from around $9/month, Pro from around $16/month, Teams from around $29/month (billed annually), and custom Enterprise, priced on operations/credits usage.
SQLMesh: Core framework is fully free and open source under Apache License 2.0 with no seat pricing; the commercial Tobiko Cloud layer is custom-priced (platform fee plus consumption) and not publicly listed.
Make's cost scales with automation volume and credits, while SQLMesh's core has zero licensing cost and only the optional cloud add-on carries (unpublished) pricing.
Skill Requirement
Make: Designed as a no-code visual builder where most automations are built by dragging and connecting modules, though comfort with JSON and APIs helps for custom HTTP requests.
SQLMesh: Requires writing SQL and/or Python models directly; it's a developer framework, not a drag-and-drop tool.
Determines who on a team can actually build with each tool.
Change Safety & Testing
Make: Provides execution logs and history to inspect past runs, plus error handlers, retries, and fallback routes, but no documented pre-deploy impact-preview workflow.
SQLMesh: Uses a Plan/Apply workflow to preview the impact of changes before deploying, alongside automated unit test generation, built-in data audits, and column-level lineage.
SQLMesh's Plan/Apply model is specifically built to prevent breaking changes to production data pipelines.
Ecosystem Compatibility
Make: Offers thousands of pre-built app integration modules plus generic HTTP and webhook modules for almost any API.
SQLMesh: Designed to be backward compatible with existing dbt projects and transpiles across 10+ SQL dialects via the SQLGlot library.
Shows each tool's compatibility strategy: Make connects to external SaaS apps, while SQLMesh interoperates with the existing dbt/SQL ecosystem.
Feature-by-Feature
Building & Workflow Model
Feature
Make
SQLMesh
Visual drag-and-drop builder
Available
Unavailable
Branching/conditional logic
Available
Not documented
Pre-built templates
Available
Not documented
dbt compatibility
Not documented
Available
Testing, Reliability & Governance
Feature
Make
SQLMesh
Error handling and retries
Available
Not documented
Pre-deploy change preview (Plan/Apply)
Not documented
Available
Automated unit testing / audits
Not documented
Available
Column-level lineage
Not documented
Available
Execution logs/history
Available
Not documented
Pricing & Access
Feature
Make
SQLMesh
Free plan/tier
Available
Available
Open source
Not documented
Available
Paid managed offering
Available
Available
AI modules/agents
Available
Not documented
Pricing Compared
Starting price reflects the lowest paid tier, not the full cost for every team size or usage level.
Make
Free — $0/month Monthly
Core — $9/month Monthly (10,000 operations)
Pro — $16/month Monthly (10,000 operations)
Teams — $29/month Monthly (10,000 operations)
Enterprise — Custom Contact sales
SQLMesh
SQLMesh Open Source — Free n/a
Tobiko Cloud — Custom (platform fee plus usage-based consumption) custom
Generally more affordable at scale than Zapier for comparable usage
Detailed data inspection at every step makes debugging easier
Backed by Celonis, providing enterprise-grade stability and continued investment
Cons
Steeper learning curve than simpler linear automation tools
Credit/operation-based pricing can be difficult to predict for complex scenarios
Free plan is limited to two active scenarios
Some advanced features require Pro or Teams tier
Enterprise pricing requires contacting sales rather than transparent self-serve pricing
SQLMesh
Pros
Core framework is free, open source, and Apache 2.0 licensed
Meaningfully reduces compute and storage costs versus full DAG re-runs
dbt-compatible, easing migration from existing dbt projects
Compile-time SQL validation catches errors before they run in production
Built by experienced data engineers from major tech companies
Cons
Smaller community and ecosystem than the more established dbt
Tobiko Cloud pricing is not publicly listed and requires a sales conversation
Small company size (2-10 employees at Tobiko Data) relative to larger data tooling vendors
Fewer third-party integrations and pre-built packages compared to dbt's package hub
Learning curve for teams unfamiliar with virtual environments and its plan-based workflow
Use Cases
Choose Make: Business teams and no-code builders wanting to connect CRMs, spreadsheets, email tools, and e-commerce platforms via a visual, branching flowchart canvas, starting with a free plan and going up to Core from around $9/month.
Choose SQLMesh: Data engineers and analytics engineers who write SQL or Python models, want dbt-compatible pipelines, and need virtual data environments plus a Terraform-style Plan/Apply workflow to preview changes before deploying to a warehouse.
Need both: A data team could use SQLMesh to build and version-control the SQL/Python transformation models that populate their warehouse, while using Make's generic HTTP or webhook modules to trigger downstream automations off that data, such as pushing a newly materialized customer segment into a CRM or Slack channel.
Make
Marketing and sales workflow automation — Marketing and sales teams use Make to sync leads between CRMs, email tools, and spreadsheets automatically without manual data entry.
Business process automation for SMBs — Small business owners use Make to automate repetitive administrative tasks like invoicing, notifications, and data syncing across tools.
Enterprise process integration — Enterprise IT and operations teams use Make's Enterprise tier to connect internal systems and orchestrate mission-critical automated processes at scale.
SQLMesh
Migrating off dbt for cost savings — Data teams move existing dbt projects to SQLMesh to cut compute and storage costs through smarter incremental processing and virtual environments.
DevOps-style data pipeline management — Teams use blue-green deployments and compile-time validation to ship data model changes safely without breaking production tables.
Enterprise governance with Tobiko Cloud — Organizations use Tobiko Cloud's RBAC, SSO, and managed scheduler to run SQLMesh at scale with enterprise access controls.
Frequently Asked Questions
Is Make free to use?
Yes, a free plan offers a limited monthly operations/credits allowance; paid plans start around $9/month (Core, billed annually).
Is SQLMesh free?
Yes, the core framework is open source under Apache License 2.0; the commercial Tobiko Cloud layer is custom-priced.
Do I need to know how to code to use Make?
No, it's designed as a no-code visual builder, though familiarity with JSON and APIs helps for custom HTTP requests.
Is SQLMesh compatible with dbt?
Yes, it's designed to be backward compatible with existing dbt projects.
Who built SQLMesh, and is that a concern?
Tobiko Data built SQLMesh, and the company was acquired by Fivetran in September 2025 — a factor worth weighing since it may affect SQLMesh's future roadmap.
Can Make and SQLMesh work together?
There's no documented native integration, but Make's generic HTTP and webhook modules could trigger workflows off events from a data pipeline SQLMesh manages, since Make is designed to integrate with virtually any API.