Comparison
Make is visual app-to-app automation, now part of Celonis, priced by the operation. Etlworks handles the data side (CDC, bulk ETL, warehouses, and EDI) where counting operations stops making sense.
The verdict
Feature breakdown
| Capability | Etlworks | Make |
|---|---|---|
| Pricing & commercial | ||
| Starting price (monthly) | $300 | Free (1,000 credits) / Core from $9 |
| Pricing model | Fixed per tier | Credit per module run |
| Cost transparency | High, flat rate | Low, credit burn scales with record count |
| Vendor lock-in | Monthly or annual, no contract | Monthly or annual |
| Ownership | Independent, founder-run | Celonis |
| Integration scope | ||
| Sources | 270+ | 2,000+ app connectors |
| Destinations | Warehouses, databases, SaaS, NoSQL, files, APIs, queues, IoT, email | Same app catalogue |
| ETL capabilities | ETL, ELT, Reverse ETL, wildcard processing | Partial: per-record scenario logic, not bulk ETL |
| API management | Full | HTTP module and webhooks |
| EDI processing | X12, EDIFACT, HL7, FHIR | |
| On-prem deployment | cloud only | |
| Embeddable | ||
| Transformations | ||
| Visual mapping | drag-and-drop designer with live preview | Partial: field mapping per step |
| Scripting languages | SQL, JavaScript, Python, XSLT, shell | JavaScript or Python in code steps |
| Nested and hierarchical data | JSON, XML, Avro, Parquet: read, write, normalize, flatten by dragging | Partial: JSON per item, no bulk flattening |
| Warehouse pushdown (ELT) | transform before load or in the warehouse, same engine | |
| dbt integration | dbt Core as a native flow type, Git-pinned, plus dbt Platform job triggers | no native dbt |
| Reusable logic | macros, templates, and 3,900+ prebuilt flow templates | reusable sub-workflows |
| Lookups and enrichment | Lookup Builder for cross-source lookups | Partial: lookup via extra API calls |
| Data validation | validation rules with per-step error handling | Partial: conditional branches |
| Orchestration & workflow | ||
| Scheduling | Cron expressions and fixed intervals, with per-schedule parameters | Interval and cron-style schedules |
| Event-driven triggers | HTTP listeners and webhooks, message queues, file and email events | webhooks and app triggers, the primary model |
| Continuous execution | looping schedules for CDC and queue consumers | Partial: polling triggers |
| Visual workflow builder | Composer canvas, 200+ flow types | the core of the product |
| Nested workflows | nested flows with conditional and looped steps | sub-workflows |
| Run external tools | shell and SSH scripts, CLI, JavaScript, Python, SQL, HTTP calls | Partial: HTTP modules only, no shell |
| Parallel execution | overlapping schedules run as independent, separately cancellable instances | Partial: limited by plan concurrency |
| Retries and error handling | per-step exception handling with notifications | per-step retry and error branches |
| Run monitoring | per-schedule status, run history, automatic Flow Findings reports | execution history |
| Lineage and audit trail | run-level lineage (trigger, flow, dbt commit, outcome) plus a filterable audit trail | Partial: scenario execution history |
| CDC & Streaming | ||
| CDC engine | Debezium-compatible, built-in (no Kafka required) | |
| Database CDC sources | MySQL, Postgres, SQL Server, Oracle, MongoDB, DB2, others | |
| Streaming queues | Kafka, EventHubs, Kinesis, SQS, PubSub, ActiveMQ, RabbitMQ | Partial: limited queue modules |
| IoT brokers | MQTT brokers | |
| Real-time replication | Log-based CDC, full, incremental | polling and webhooks |
| Change tracking modes | Log-based, trigger-based, timestamp/high-watermark | Polling, webhooks |
| Developer experience | ||
| REST API | full API for flows, connections, schedules, and runs | |
| CLI | full CLI with built-in SQL | Partial: limited or community CLI |
| MCP server | built-in: connect Cursor, Claude, or ChatGPT to your instance | |
| Client libraries | Python, Bash, and PowerShell clients | Partial: community SDKs |
| Version control | built-in: automatic history, diff, and revert on every artifact | Partial: change history, no diff or revert |
| Embeddable / white-label | Partial: white-label on higher tiers | |
| Compliance & security | ||
| SOC 2 Type 2 | audited; report under NDA, SOC 3 public | |
| HIPAA | supported with a BAA | Partial: higher tiers only |
| GDPR / DPA | compliant, DPA available | |
| SSO and MFA | SAML SSO, optional 2FA, JWT stateless auth | Partial: enterprise tier |
| Role and artifact-level access | six roles plus tag-based scoping of flows, connections, and schedules | Partial: role-based, no artifact-level scoping |
| Encryption and data handling | TLS in transit, encrypted at rest, customer-managed PGP, SSH tunnels, IP allowlisting; rows are not persisted by default | encrypted in transit and at rest |
| Audit logging | admin actions logged, access logs monitored | Partial: enterprise tier |
| Security testing | monthly vulnerability and penetration scans, static analysis blocking every build, periodic third-party audits | vendor-managed, details on request |
| Gen AI | ||
| AI agent | Built-in agent (Simba) that builds and edits flows from chat | Partial: AI agents and a generative scenario builder |
| Agent capabilities | Reads metadata, reads/samples data, writes JS & SQL, schedules, deploys, monitors | Composes scenarios from a prompt |
| Natural-language flow building | ‘Vibe-build’, create flows by describing what you want | generative scenario builder |
| AI-driven mapping | Auto-suggests source-to-destination mappings | |
| Built-in analytics | Agent runs analysis on flow data and pipeline behavior | |
| Chat across product | Same agent context on every screen | |
| CLI for agent | Full CLI access for run/deploy/monitor/manage | |
| Trains on customer data | Never | Per Make terms |