Build vs buy is over. A connector now costs $100 a year.

Build vs buy doesn't need to be a trade-off anymore. By delegating the work to LLM agents while keeping judgment and ownership in house, you can have the best of both worlds.

You own the code, so you aren’t at the whim of a renewal price hike. The bill tracks cheap compute instead of row count, and usage becomes predictable.

Bill shock also disappears: a 100x data spike becomes a minor blip in infra cost, not a 100x bill.

The real cost of a connector, today is closer to $100/y

In the new paradigm, creating a pipeline takes less than 2h of developer time and $5 worth of tokens. The cost of maintenance of those pipelines is similar - minutes, and a few tokens.

Let’s run the maintenance math: looking at a recent sample of dlt OSS telemetry (last 50m production runs), we see 99.83% success rate and only around 0.12% non transient errors leading to human maintenance events.

That's about 2 maintenance events in 5 years. At a couple of hours each plus a few dollars in tokens, the upkeep rounds to roughly $100 a year.

That’s a small price to pay to keep control of your bills, knowledge and stack.

Are you a practitioner? Try dlthub (2 weeks free).

Are you looking for help migrating? We offer a migration service.

The value of switching is control

You have a budget to get the job done, but row-based metering takes control out of your hands. Events like adding data to a source or running a broad marketing campaign can suddenly blow up row counts without adding utility.

And vendors have agendas that may not line up with yours. The next renewal might be blistering, and when leaving means rebuilding every pipeline, a price hike isn't a negotiation, but a business model that churns the price sensitive users and keeps the big spenders.

dltHub is different: you pay for compute and own your code. There's no lock. You are in control.

An example of bill shock you would now avoid.

One dlt user saw just this scenario: The CTO of a startup used a saas vendor to sync SQL databases, until one day a newly added log table synched by default by the vendor caused their data volume to spike 20x - silently, until the end of the month bill came looking like the entire yearly budget. The vendor didn’t want to drop any of the bill, so the CTO dropped the vendor. Moving cut their bill 182x, increased speed 10x, and improved reliability. This has several silent benefits

• A vendor default that might have caused, for example, a 18k(2k to 20k, or 10x) bill shock would now only be an inconsequential $100 blip.
• Such mistake would likely not have happened in the first place when you control the default behavior.

dltHub offers managed serverless runners, affordably priced as infrastructure. On SQL copy, it comes out a literal thousand times cheaper than some saas offerings.

Read what an hour ($1) of dlthub moves in this benchmark.

The whole team can do it now, not only specialists.

What used to be an engineering job, becomes a team self-service data democratization motion.

Agentic building removes the bottleneck while maintaining high quality. The agent does the part that needed a specialist: reads the docs, writes the code, and dlt handles the plumbing underneath. What's left is review, and any analyst or engineer from the team can do that.

"Ingestion moved from being owned by a small group with deep knowledge of specific tools to something any Python developer on the team could author, review, and ship. The question changed from 'who knows the tool?' to 'what data do we need next?'"
— Euan Johnston, Senior Data Engineer, dentolo

"Five of our senior analysts now author and maintain pipelines themselves. Data engineering is no longer a bottleneck for our analytics work."
— Stefan Szegeny, Senior Data Engineer, Hiveapp

The knowledge stops living in one person's head, so an engineer leaving no longer means a pipeline goes dark.

The people closest to the data, who know what "correct" looks like, are the ones building it. The agent does the work, the right way.

"But switching is expensive."

It isn't anymore. The agent rebuilds each pipeline in hours, not weeks, and makes testing easy.

Yespark's CTO (again not a data engineer by trade) used Claude Code to migrate their entire ingestion stack from Airbyte to dlt in five days, rebuilding their Postgres sync plus a dozen-odd sources (Zendesk, Google Sheets, GA4, HubSpot, Google Search Console, app-store reviews, and more) one at a time.

The biggest payoff: one less piece of infrastructure to maintain, pipelines now running clean at a 0% failure rate, and full control over connectors, no waiting on a vendor to ship an integration.

Need help to migrate? Read more about how dltHub migration services give Navit production-grade data and Chat-BI, without hiring

dltHub adds context and end to end operational readiness to dlt

dlt OSS is the most popular data ingestion solution for data engineers. dltHub operationalizes dlt for the whole team: observability, data quality, transformation support, and custom notebooks for monitoring or analysis that turn a working pipeline into one you can run in production.

What makes it so efficient is an agentic toolkit, built in sync with how we always imagined dlt being used, essentially our best practices handed to your agent directly. The toolkit runs the full cycle, deployment to dltHub and maintenance included, which is where the efficiency gains from earlier in this post come from.

Take a maintenance event: The agent connects to dltHub, reads the logs, creates a fix, tests it locally and iterates until the problem is solved. It then waits for your review and OK to deploy. No particular expertise needed besides common sense human judgment, and the whole workflow takes under 10 minutes. Thanks to the continuous context of dltHub, you stay at the level of the ask and let the agent run the errands it used to send you on.

Build vs buy is over. Start enjoying the best of both worlds

For twenty years the choice was pay engineers to build, or rent connectors and live with the bill. Agentic building removes the reason that tradeoff existed. You own the code, the agent does the work, and the bill tracks compute instead of rows. Building is cheap, maintenance is minutes, and switching takes days, not quarters.

So the question isn't build or buy anymore. It's: do you want to own this, or keep renting it?

• Got someone who writes Python? Try dltHub free for 2 weeks — rebuild your most expensive pipeline and watch the bill drop.
• Don't want to run it yourself? Book a migration — we'll move it for you.