Every SQL query, retry and schema change, live in the UI and the API. Connectors are managed and maintained for you, with credit pricing that has no monthly active rows.
14:02:11GET/v2/campaigns?updated_since=2026-08-19T13:00Z200412ms
14:02:12GET/v2/campaigns?page=2429 rate limitedretry in 2s
14:02:14GET/v2/campaigns?page=2200388ms, attempt 2
14:02:15SCHEMAnew field detected: campaigns.bid_strategy (string)applied
14:02:16SQLMERGE INTO analytics.campaigns USING _stage ON id18,442 rows
14:02:16RUNcompleted4.9s, 17.2MB peak RSS
This is what "fully managed" should have meant.
It fails at 3am and gives you nothing to debug with: no visible API call, no query, no retry log.Open source hands you the code instead, and the next upstream API change becomes your problem to fix.
Managed
When a sync fails, there is no request to inspect, just silence from a system you cannot see inside.
You cannot tell what SQL ran, what retried, or why, only that the table is stale.
You file a ticket and wait, debugging a system you are not allowed to look inside.
Managed is supposed to mean simple, but it can also mean zero visibility the moment something goes wrong.
Open source
Developing a codebase internally, or learning someone else's open-source code, takes time and the upfront cost is high.
APIs change, data breaks, partners update formats, and your team spends more time fixing than shipping.
Instead of strategic work, your best developers are stuck wrangling auth credentials, callback URLs, and dozens of APIs.
You do not need to be the hundredth team rebuilding a Salesforce or Google Ads connector.
Extract's logs expose the work itself: every API call, SQL query, retry, and schema change, live in the product and the API. This is what fully managed should have meant.
Engineers who move to Extract get:
Full request and response logsEvery SQL query, in and outAutomatic schema change detectionVerbose Mode for deep debuggingConnectors built and maintained in RustNamed controls over extraction and loadWhy debug a black box?
Extract pairs the visibility of open source with connectors it maintains for you.
Full request logsSee the request, the response code, and every retry Extract attempts after a rate limit.
Live in the UI and the APIThe same log data is available in the product and can be pulled programmatically.
See how Extract reads and writesEvery query Extract runs against your source and your destination is logged.
Schema drift, never silentNew fields and tables are detected automatically, with a full change log in the product and the API.
Control extractionChoose which tables and fields sync, set custom schedules including crontabs, and pick incremental, partitioned, or full syncs.
Control loadingChoose replace, upsert, Soft Delete, or Time Travel, with automatic schema evolution and configurable backfill windows.
~17MB per sync workerEach Extract sync worker runs on roughly 17MB of RAM, against 300MB or more for a typical Python or Java worker.
Up to 70% lower costHigher utilization per node means a genuinely lower cost per row, so pricing stays low instead of resetting at renewal.
Push audiences and conversionsSend warehouse data to Meta CAPI, Google Enhanced Conversions, CRMs, and internal services.
No second vendorNo-code mapping and built-in PII hashing, with the same complete logging you get on ingestion.
~17MB
RAM per Extract sync worker
300MB+
Typical Python or Java worker
Up to 70%
Lower cost than legacy ELT
A credit meters the work your pipelines do, published per tier. Unlimited sources and destinations on every plan, so adding a connector never adds to the bill.
| Free | Starter | Standard | |
|---|---|---|---|
| Price | $0 | from $15/mo | $1,500 – $3,000/mo |
| Credits | 1M/mo | 1M–100M/mo | 100M–200M/mo |
| Connections | 5 | 10 | 15 |
| Sync frequency | 60 min | 60 min | 15 min |
| Users | 2 | 10 | Unlimited |
From radical affordability to enterprise-grade performance, Extract outshines the competition across every critical dimension.
| FEATURE |  | Fivetran | Airbyte | Stitch |
|---|---|---|---|---|
| Cost | $ | $$$$ | $$$ | $$ |
| Language | Rust | Python / Java | Python / Java, additional languages (relies on open source connectors) | Java |
| Customizability | High | Medium | Medium | Low |
| Connector Reliability | High | High | Most connectors are open source, and there are no guarantees | Medium |
| Log Quality | Real-time, highly curated, in-product visibility | Not real-time, lacks in-product visibility | Mixed, open-source connectors may not have logs at all | Low, separates the “extract” logs from the “load” logs |
| Schema evolution | Automatic, full change log in the product/API | Automatic, change log not accessible in product/API | Automatic | Automatic, change log in the product |
| Monitoring | Custom trigger expressions. Supports slack, email & webhooks. | Limited hard-coded triggers, only supports email. | Basic monitoring via UI and API. Lacks custom alert rules. | Basic email alerts |
| Transparency | System event log, schema changes, run history, query interface across all runs, API | Limited, logs not in product, schema changes not visible, no audit log | The UI shows the job history and run status, but no audit log exists. Logs viewable per-connector and per-job | Limited, very hard to associate extracts with loads |
| Scalability | Enterprise-grade at reasonable costs | Enterprise-grade but prohibitively expensive | May vary, no guarantee or support on a lot of their connectors | Slower development pace, many missing connectors |
Push audiences and conversions from the warehouse to Meta CAPI, Google Enhanced Conversions, CRMs and internal services. No-code mapping, built-in PII hashing, the same complete logging you get on ingestion.
No second vendor.
warehouse.high_ltv_users→Meta CAPI
email→emsha256
phone→phsha256
net_revenue→value
purchased_at→event_time
last sync 14:00, 42,118 rows, 0 errors
Extract really frees up our time to take on more tasks that otherwise we wouldn’t have the capacity to handle, which is where the bread and butter lies for us.
We also evaluated Salesforce’s Datorama, but Extract kept it simpler and easier. We need the data to be pulled and customized, and I can do that without the need for a Data Engineer.
Yes. Every API call, SQL query, retry and schema change is exposed live in the UI and the API, so you can see exactly what a sync did and why.
Each sync worker runs on roughly 17MB of RAM against 300MB or more for typical Python or Java stacks, so the cost per row is genuinely lower. Pricing is credit-based with published tiers, not a meter that resets at renewal.
A sealed pipeline hides the request, the query, and the retry log, leaving you a stale table and a ticket. Extract manages the pipeline and leaves the log open.
It gives you the code, and makes reliability your problem. Extract pairs the same visibility with connectors it builds and maintains for you.
Yes. Every connector is written and maintained in house in Rust, rather than inherited from an open-source project, and adapted as upstream APIs change.
Most incumbents bill monthly, so there is no lock-in penalty, and the work is closer to swapping an API than a migration project.
No. Start free with 1M credits, 5 connections and 2 users, no credit card required.
Simply integrate your data sources with Extract through our intuitive, self-service interface. No coding required—just a few clicks to link your systems.
Set up data pipelines tailored to your needs. With powerful customization options, Extract gives you complete control over data flow.
Once configured, Extract automates the extraction and loading of your data. You can trust that it runs efficiently and securely in the background.
Keep an eye on your data pipelines with full visibility. Track progress, monitor performance, and adjust as needed—all from a user-friendly dashboard.
Stop filing tickets for a pipeline you cannot see inside.Extract gives you full visibility and connectors that are actually maintained.
Connect a source, pick a destination, run it.1M credits a month, 5 connections, no credit card.
Start free