Fivetran vs dlt: Quickstart vs Endgame

Data integration is a broken space. Vendors ship half-baked APIs with outdated patterns, and it’s up to data engineers to make them work — often with duct tape and CLI magic. While OpenAPI and other standards exist, most large platforms ignore them and reinvent the flat tire. Some are still stuck in design patterns from 2010.

This is why tools like Fivetran and other connector catalogs still thrive: not because they’re good, but because they’re less painful than starting from scratch. When the alternative is debugging undocumented webhooks at 2am, even a black box that kinda works, looks like a win.

But here’s the thing: you shouldn’t have to choose between “do everything manually” and “hand your future to a vendor”.

The excuse department is closed, now what?

It’s time to stop blaming rigid vendor catalogs, slow feature roadmaps or broken apis.

Instead of hiding complexity behind an opaque UI, dlt embraces it — and automates what can be automated, while exposing what must be customized. You write code when it matters, and skip it when it doesn’t. The result? Faster pipelines, fewer bugs, and actual control.

This isn’t another vendor comparison that ends with “it depends.”

Fivetran and dlt represent two entirely different philosophies about how data teams should work.

• One bets on SaaS abstractions and locked-down templates.
• The other bets on developers, flexibility, and open infrastructure.

The choice you make determines whether you'll be explaining vendor limitations to frustrated stakeholders 6-24 months from now, or shipping new integrations in hours while others wait for roadmap updates.

Fivetran vs dlt: Two Different Approaches to Data Integration

Fivetran: The Managed Data Pipeline Service

Back in 2012, Fivetran's approach was genuinely smart. The data engineering landscape was brutal, teams were either writing fragile custom scripts or paying enterprise prices for bloated ETL platforms. Fivetran offered something simpler: pre-built connectors wrapped in a clean UI, sold as a managed service.

Their pitch was compelling: "Stop building data pipes, start using data." Zero maintenance, zero configuration, zero technical headaches. Just point, click, and watch your data flow.

Crucially, the API landscape in 2012 was dramatically smaller than today. A relatively small catalog of connectors could cover most organizations' integration needs. Salesforce, a database, maybe Google Analytics, that was often sufficient. The connector catalog approach made economic sense when there were fewer sources to cover.

But the API economy exploded. Every SaaS tool now has an API. Internal tools expose APIs. Microservices create dozens of data sources within single organizations. What was once a manageable catalog problem became a long tail problem.

The real issue isn't that Fivetran's approach was wrong, it made perfect sense when it was built. But the world changed faster than their model could adapt. What worked when APIs were scarce doesn't work when every business process generates data through dozens of different endpoints.

The professional landscape changed too. In 2012, many data people started with Excel and SQL. Today, most data people know Python. The tools that felt accessible then can feel limiting now.

The real question isn't whether Fivetran was a good idea. The question is whether paying vendor prices for what's increasingly become a commodity makes sense in 2025.

dlt: Open Source Python-First Data Integration

Here's what I learned building data systems for a decade: every abstraction that "eliminates coding" eventually forces you back into coding anyway, but now you're debugging someone else's abstractions instead of your own logic.

dlt takes a different approach. Instead of hiding Python behind UIs and configuration files, we made Python data pipelines simple to write and reliable to run. You write standard Python. dlt handles schema evolution, state management, incremental loading, parallelism, retries and all the production complexity that usually takes weeks to get right.

The philosophy is simple: empower developers, don't replace them. With AI assistants like Cursor and GitHub Copilot, writing Python is faster than ever. The bottleneck isn't coding, it's dealing with all the operational complexity that comes after the code works.

That's what dlt solves. You get the full power of Python without the traditional headaches of data engineering.

The Real Trade-offs: Fivetran vs dlt Cost, Flexibility, and Team Impact

Development Speed: Time to Value Comparison

Here's how these tools actually perform when you need to get data flowing:

1. Fivetran recently added AI support for their Connector Development Kit, but success rates and real-world performance data aren't yet available.
2. Being open source and public, with dlt used to date in over 1k public repositories, dlt's documentation and code patterns are extensively indexed by modern LLMs (GPT-4, Claude, Cursor), enabling AI assistants to generate working pipelines with remarkable accuracy.

Fivetran Pricing: Why Consumption-Based Costs Become Unpredictable

The hard truth: Fivetran's pricing model works great for Fivetran. For their customers, it creates incentives where you avoid loading potentially valuable data because of cost concerns.

Yummy.eu learned this the hard way. A new log table started replicating every 15 minutes by default, turning their manageable monthly bill into a budget emergency. These were the vendor defaults, that they cannot control, and they only got notified at the end of the month. Where did they go wrong? in this situation, there’s no arguing who’s to blame for this money grab.

Data grows exponentially, not linearly. That "affordable" pilot project becomes a budget crisis as your organization succeeds.

That pilot project that cost $200 in month one? It's $2,000 by month six because your marketing team started logging more events. That new table you thought would be "just a small addition"? It turns out to be 80% of your bill.

Now the real problem isn't just the cost. It's that you start making decisions about what data to load based on cost instead of value. Your analytics become less complete because including certain tables or increasing sync frequency becomes too expensive.

With dlt, you pay for the actual resources you use. No vendor markup, no surprise bills, no incentives to avoid loading useful data. When Taktile switched from a similar vendor, they saw 20x cost savings while processing more data and giving their whole team the ability to build integrations instead of just a few specialists.

Data Pipeline Flexibility: Where Fivetran's Marketing Claims Break Down

Here's where Fivetran's marketing message becomes most compelling: "But it's so easy to set up!" "Look at all the connectors!" "No coding required!"

And honestly, this marketing is effective because it contains real truth. Fivetran can be remarkably easy to set up for standard use cases. The connector catalog is genuinely impressive. For many scenarios, you really don't need to write code.

The problem isn't that the marketing is false, it's that it's incomplete. It describes the best-case scenario perfectly while glossing over what happens when you inevitably move beyond that scenario.

I've noticed a pattern in conversations with data teams: those who haven't hit Fivetran's limits yet often enthusiastically share their positive experiences. They'll highlight the time savings ("it freed up our engineering team"), praise the simplicity ("anyone can configure it"), and dismiss flexibility concerns ("we haven't needed anything custom yet").

This isn't marketing speak, these are often genuine experiences from smart, competent teams. The problem is that business requirements evolve, and when they do, the same teams find themselves in a very different situation.

Taktile needed connectors for Linear, Github, Betteruptime, and AWS Cost Explorer. None existed in their cloud ETL vendor's catalog. Their options were: wait months for potential development, pay for custom connector development, or build workarounds.

This isn't edge case territory, this is normal business requirements. Every growing company eventually needs integration with internal tools, specialized APIs, or custom data sources that aren't in any vendor's catalog. Growing businesses develop more processes and systems, which naturally increases their data integration needs.

The honest assessment: If your needs fit perfectly within Fivetran's existing connectors, and those needs never change, Fivetran works fine. But "never change" isn't how successful businesses operate.

With dlt, custom sources aren't exceptions requiring vendor negotiations, they're standard Python code that any data engineer can write and maintain. You own the pipeline logic and can fix or extend it whenever needed.

Team Dynamics: The Gatekeeper Problem

This is where the real organizational costs emerge. Fivetran's model creates dependency relationships that look convenient on paper but become problematic in practice.

At Taktile, their small data team became a bottleneck. "Any time anyone in the company needed access to particular data, they had to go through the data team," their Lead Data Engineer Simon Bumm explained.

This isn't Fivetran's fault, it's how proprietary platforms work. When integration requires vendor-specific knowledge and UI access, you naturally centralize control. What looks like "simplicity" from a management perspective becomes "dependency" from a team productivity perspective.

After switching to dlt, Taktile expanded data integration capability from a few specialists to tens of contributors. Their customer support team added a connector in a day. As Simon put it: "I stopped being the gatekeeper."

This shift matters more than most managers realize. It's not just about technical capabilities, it's about team scalability and avoiding single points of failure. Instead of engineering teams explaining vendor limitations to frustrated stakeholders, or analysts waiting for someone else's priorities, you create a culture where data problems get solved at the speed of business needs.

When Each Tool Actually Makes Sense

Where Fivetran Still Shines (And Why It's Often Not Enough)

Let's be fair: Fivetran became a giant for good reasons. If your company has minimal in-house Python expertise, a pressing need to integrate a handful of very common SaaS tools (think Salesforce, Google Analytics, standard databases), and a budget that can absorb premium pricing for convenience, Fivetran can get you data flowing quickly. Their UI is genuinely slick, their standard connectors work reliably, and for that initial setup, it can feel like magic.

Fivetran makes the most sense when:

• Your team lacks Python expertise and has no plans to develop it
• Your data sources are entirely within their 300+ connector catalog
• Your data volumes are predictable and relatively modest
• Vendor dependency isn't a concern for your organization
• You need immediate results and can accept long-term constraints

Many teams have successfully used Fivetran to get their first data warehouses populated and running. For managers under pressure to show quick wins, analysts needing immediate access to standard reports, and small teams without dedicated data engineering resources, Fivetran's "anyone can do it" promise has real appeal.

The moment your needs evolve beyond that initial setup, a niche API, a custom transformation, cost optimization pressures, or simply the realization that your data volumes are growing exponentially, the Fivetran model starts to show its limits. That initial convenience can quickly turn into an expensive bottleneck that frustrates engineers trying to solve real problems and leaves analysts waiting for the data they actually need.

When dlt becomes the clear choice

Wherever Python skills exist or can be developed, dlt is king, which increasingly means most data teams. The math is simple: if someone on your team can write df.to_sql(), they can build production-grade data pipelines with dlt.

dlt is the optimal choice when:

• Flexibility and ownership are more important than convenience
• You're planning for speed and scale and don't want vendor lock-in
• You need custom data sources or complex transformations
• Cost control and predictability matter to your organization
• You want to leverage AI-assisted development (Cursor, GitHub Copilot)
• Your team has or wants to develop Python expertise

The AI development factor: This is where dlt really shines since it got indexed by LLMs in 2024. Modern AI assistants like Cursor can generate working dlt pipelines in minutes because dlt's patterns are extensively indexed by LLMs. You can literally describe what you want in plain English and get production-ready code. What's more, dlt offers LLM-first tools to supercharge LLM development, maintenance and usage.

The real question isn't technical capability, it's strategic control. Do you want your data infrastructure decisions made by a vendor's product roadmap, or by your team's actual needs? Do you want costs that scale predictably with your success, or pricing that creates perverse incentives to avoid loading valuable data?

For engineering teams tired of explaining vendor limitations to stakeholders, for analysts who want their data problems solved in days not months, and for managers who need predictable costs and scalable team capabilities, dlt represents a fundamentally different approach: you own the pipes, you control the flow, and you adapt at the speed your business actually operates.

"But isn't building custom connectors hard and slow?"

This is Fivetran's strongest defense: "Sure, you could build it yourself, but who has time for that complexity?" In the old world of brittle APIs and mountains of boilerplate code, they had a point.

But that's exactly the problem dlt was designed to solve. You're not starting from scratch, you're writing simple Python that dlt makes robust and production-ready. With modern AI assistance (GitHub Copilot, Cursor), building a new connector often takes minutes, not weeks. dlt handles the tedious parts: schema inference and evolution, state management, incremental loading, and data consistency enabling your junior python users to build better-than-commercial pipelines in minutes or hours.

When an API changes, you don't file a support ticket and wait. Any Python developer on your team can fix it directly, often in minutes. You end up with less firefighting, not more, because you control the code instead of depending on a vendor's interpretation of your needs.

Think of it this way: Fivetran sells you expensive, single-purpose taxis for each destination. dlt gives you a vehicle and teaches you to drive.

The 5 Stages of Data Integration Grief

Here's how teams typically handle migration pains:

1. Denial: "Fivetran is perfect, we won't need anything custom."
2. Anger: "Wait, Fivetran doesn't support that endpoint? We have to build it ourselves?!"
3. Bargaining: "Maybe if we just write a few small custom scripts alongside Fivetran, it'll be fine."
4. Depression: "Why are we paying premium prices AND maintaining all these scripts?"
5. Acceptance: "Time to migrate to something flexible, like dlt."

You're not avoiding engineering effort; you're just deferring it, while paying a premium.

The Bottom Line: Choose Based on Reality, Not Marketing

If you're looking for the most honest recommendation:

For small teams with basic, stable needs and generous budgets: Fivetran can work fine. You're paying a premium for convenience, which might be worth it and cost less than hiring someone with python competence.

For everyone else: dlt provides better cost efficiency, flexibility, and team empowerment. The initial learning curve pays dividends quickly.

The uncomfortable truth: The data engineering field is moving toward code-first approaches. You can adapt now on your timeline, or later when vendor limitations force your hand.

The choice isn't really between "easy" and "hard", it's between "simple now, complex later" and "investment now, simple forever."

Most data teams eventually choose simplicity of a single tool with flexibility. The question is whether you want to make that choice proactively or reactively.

What This Means for Your Role

If You're a Data Team Manager

Fivetran sells a compelling story to managers: "Your team can focus on high-value work instead of plumbing." But here's what they don't mention:

• Your team still needs to understand the platform, debug issues, and work around limitations
• You're creating vendor dependency that limits your hiring options (pay Fivetran instead of developing engineering capabilities)
• Budget forecasting becomes nearly impossible with consumption-based pricing
• You'll likely end up paying for Fivetran AND building custom solutions for gaps

With dlt: Your team builds transferable Python skills, you have predictable costs, and you're not dependent on vendor roadmaps. The shallow learning curve, skill reuse, and built-in best practices remove the usual data engineering headaches. Building Python competency also enables your team to leverage modern analytics and machine learning techniques.

If You're a Data Analyst

The promise is tempting: "All your data sources available without bothering engineering." The reality is different:

• You're still dependent on someone (now Fivetran) for new connectors and customizations
• Data quality issues require vendor support tickets instead of direct fixes
• Custom business logic still requires engineering work, just in a more constrained environment

With dlt: If you can write Python (and you probably should), you can solve your own data problems. If you can't, at least your engineering team can fix issues immediately.

If You're a Data Engineer

You know the pain of explaining to stakeholders why a "simple" integration request will take weeks because it requires vendor coordination. You've submitted support tickets for bugs in connectors you can't fix yourself.

Fivetran advocates will tell you this is worth it because you avoid "undifferentiated heavy lifting." But data integration isn't undifferentiated, it's core to your organization's success. Outsourcing control over critical infrastructure rarely ends well.

With dlt: You write Python code. When something breaks, you fix it. When requirements change, you adapt. When stakeholders have questions, you have answers.

Why This Choice Matters More Than You Think

I've watched the same pattern play out at dozens of companies: team starts with Fivetran for quick wins, new requirements emerge that don't fit the catalog, team builds custom solutions alongside Fivetran, bills grow faster than usage, team realizes they're paying for convenience while doing the hard work themselves anyway.

The timeline varies, but the outcome is usually the same. Teams that stick with managed ETL either accept higher costs and limited flexibility, or eventually migrate to more flexible tools.

The choice comes down to this: do you want to invest in building capability within your team, or do you want to rent access to someone else's catalog? Both approaches work, but they lead to very different outcomes over time.

Companies building innovative data products tend to choose tools that make their teams more capable rather than more dependent. When your data needs are evolving quickly, you need tools that can evolve with you.

Most data teams end up needing the flexibility that comes with code. The question is whether you want to develop that capability gradually on your timeline, or scramble to build it later when vendor limitations are blocking important projects.

Skip Fivetran: Start Building Better Pipelines Today

The best way to understand the difference is to experience it yourself. Most data teams that switch to dlt wish they'd made the move sooner, the combination of lower costs, higher flexibility, and faster development is immediately apparent.

Get Started in 5 Minutes

Option 1: Try dlt instantly in your browserOpen our Colab notebook and build your first pipeline without installing anything. You'll have a working pipeline extracting and loading data in under 5 minutes.

Option 2: Build a real pipelineFollow our quickstart guide to deploy a production-ready pipeline using your own data sources. Pick any API your team uses for your CRM, support system, marketing platform.

The 30-Minute Challenge

Here's what we recommend: spend 30 minutes building the same integration with both approaches. Start with dlt (it'll take you 15 minutes), then try configuring the equivalent in Fivetran if a connector exists.

Ask yourself these questions:

• Which approach gave you more control over the data transformation?
• Which would be easier to modify when requirements change?
• Which would you rather debug at 3 AM?
• Which approach would scale better with your team's growth?

The truth is, most teams know within those 30 minutes which approach fits their needs better.

Join 3000+ Data Teams Who went code first

Companies like Taktile, Yummy.eu, and hundreds of others have already migrated from expensive managed ETL to dlt. They're seeing 20-200x cost reductions while building better data products faster.

Try dlt: Try it in Colab without installing anything, or go to production with our deployment guides to get a real pipeline running.