The Fivetran-dbt Labs merger in October 2025 created a combined entity approaching $600M ARR. For data teams already dealing with Fivetran’s March 2025 pricing overhaul, this consolidation raised uncomfortable questions about vendor dependency and long-term costs.
Three tools dominate the data ingestion conversation: Fivetran (the incumbent), Airbyte (the open-source challenger), and dlt (the Python-native newcomer). Each takes a different approach to moving data from sources to warehouses.
How each tool approaches data loading
Fivetran pioneered the fully-managed ELT model. You configure connectors through a web UI, and Fivetran handles everything: extraction, schema detection, incremental loading, and delivery to your warehouse. No infrastructure to manage, no code to write. The trade-off is cost and control.
Airbyte started as an open-source alternative with a familiar connector-based architecture. You can self-host it on Kubernetes or use their cloud offering. The open-source model means community-contributed connectors alongside official ones, with the flexibility (and responsibility) that entails.
dlt takes a different path entirely. It’s a Python library you pip install. No UI, no containers, no orchestration servers. You write Python scripts that define your pipelines, and those scripts run anywhere Python runs: your laptop, an Airflow DAG, a Cloud Function, or a GitHub Action. The library handles the hard parts (schema inference, incremental loading, type coercion) while you maintain full control over the code.
Pricing: the elephant in the room
Fivetran’s MAR model
Fivetran charges based on Monthly Active Rows (MAR). The critical March 2025 change shifted from account-wide to per-connector MAR tiering, eliminating bulk discounts that many customers relied on.
Plans range from Free (500K MAR/month) to Business Critical (~$1,067 per million MAR). There’s also a $5 base charge per standard connection and a minimum annual contract of $12,000.
Community feedback has been harsh. Reddit users report “4-8x cost increases” and “70% cost increase for businesses with many connections.” Marketing data is particularly affected because ad-level data generates high update frequency, causing MAR counts to balloon.
Airbyte’s capacity-based pricing
Airbyte introduced capacity-based pricing in February 2025. The open-source Core remains free. Cloud Standard costs $10/month base plus volume-based fees: $15 per million rows for API sources, $10 per GB for database and file sources.
For a workload of 50 million API rows plus 50GB of database data, you’d pay roughly $1,250 on Airbyte versus approximately $4,445 under a MAR-based model.
dlt’s infrastructure-only costs
dlt itself costs nothing. It’s Apache 2.0 licensed open source. Your only costs are the infrastructure you choose to run it on and your destination warehouse. For serverless deployments on Cloud Functions or Lambda, this can mean single-digit dollars for moderate workloads.
The catch: you’re responsible for deployment, monitoring, and maintenance. dltHub has a platform offering planned for 2026 that will provide enterprise features, but the library remains free.
Connector coverage and quality
| Tool | Total Connectors | Marketing Coverage | Quality Notes |
|---|---|---|---|
| Fivetran | 700+ | Comprehensive | Professionally maintained, automatic schema detection |
| Airbyte | 600+ (350+ official) | Good coverage | Mix of official and community, variable reliability |
| dlt | 60+ verified | Growing | REST API builder can generate from any API docs |
All three support the major marketing sources: Google Ads, Meta/Facebook Ads, GA4, HubSpot, and Shopify. Where they differ is in connector quality and edge cases.
Fivetran’s connectors are their product. They’re maintained by paid engineers who respond to API changes quickly. When Google Ads releases a new API version, Fivetran updates their connector.
Airbyte’s connector quality varies. Official connectors are generally solid, but community-contributed ones can lag behind API changes.
dlt takes a builder approach. The 60+ verified sources cover common cases, and the REST API builder handles the rest. Point it at API documentation (or let an LLM help parse it), and you can generate a working pipeline for almost any REST API. Users created over 50,000 custom connectors in September 2024 alone.
The self-hosted trade-off
Airbyte’s hidden infrastructure costs
Self-hosting Airbyte requires a Kubernetes cluster (EKS, GKE, or AKS) with minimum 2 cores and 8GB RAM per node, 30GB disk space, external PostgreSQL, and S3/GCS for log storage.
The sticker shock often comes later. Practitioners report that NAT Gateway costs ($0.045/hour plus $0.045/GB) can represent 80% of total infrastructure costs. One user noted that “NAT gateway and EC2 costs will far outweigh the rest.” You also need Kubernetes expertise for ongoing maintenance, Helm chart updates, and connector image management.
dlt’s deployment flexibility
dlt runs wherever Python runs. Teams deploy it on GitHub Actions for scheduled pipelines, on Cloud Functions or Lambda for serverless execution, or alongside Airflow and Dagster when integrating with existing orchestration. Development and testing happen on your laptop.
The infrastructure footprint is minimal. A Cloud Function running a dlt pipeline might cost pennies per run.
When managed makes sense
Self-hosting isn’t always cheaper once you factor in engineering time. If your team lacks Kubernetes experience or you’re optimizing for reliability over cost, Fivetran’s fully-managed approach has real value. The 99.9% SLA uptime and automatic upgrades mean less operational burden on your data engineers.
Day-to-day operations
Monitoring and maintenance
Fivetran provides built-in dashboard monitoring, automatic upgrades, and alerting out of the box. Near-zero maintenance is their selling point.
Airbyte Cloud has low maintenance overhead. Self-hosted requires monitoring your Kubernetes cluster, managing connector versions, and handling failures. The web UI provides visibility, but you’re responsible for the infrastructure underneath.
dlt requires external observability. If you’re running in Dagster or Airflow, you get their UIs. Otherwise, you’ll need custom logging, alerting, and monitoring. Some teams see this as a feature (full control), others as a burden.
Schema change handling
All three handle schema evolution, but with different approaches:
Fivetran automatically propagates schema changes. Removed columns get soft-deleted, data type changes create new columns. You can configure handling as Allow All, Allow Columns, or Block All.
Airbyte offers configurable schema propagation with column selection.
dlt provides built-in schema evolution with schema_contract options. The evolve setting auto-adapts the destination schema, while freeze stores changes as JSON without modifying your warehouse tables.
Sync frequency
Fivetran offers 5-minute minimum syncs on the Enterprise tier. Airbyte matches that on Enterprise Flex, but Standard and Plus plans are limited to hourly syncs. dlt runs on whatever schedule your orchestrator supports.
Enterprise considerations
| Feature | Fivetran | Airbyte | dlt |
|---|---|---|---|
| SOC 2 Type II | Yes | Yes | Inherits from infrastructure |
| HIPAA | Yes | Enterprise only | Inherits from infrastructure |
| RBAC | Yes | Pro tier and up | Custom implementation |
| SSO | SAML 2.0 | Enterprise only | Custom implementation |
| Audit Logs | Yes | Enterprise only | Custom implementation |
Fivetran and Airbyte Cloud provide compliance certifications directly. With dlt, you inherit whatever compliance your infrastructure provides. Running on GCP with proper configuration gives you SOC 2 and HIPAA coverage through Google’s certifications, but you’re responsible for proving your implementation meets requirements.
What practitioners actually say
Community sentiment from Reddit’s r/dataengineering, dbt Slack, and practitioner blogs tells a mixed story.
Fivetran users consistently bring up pricing. “Pricing already strained” and “MAR pricing is deadly for marketing data” are common refrains on Reddit. That said, reliability remains Fivetran’s strongest argument: when you need syncing from multiple SaaS tools to just work, it delivers.
Airbyte is appreciated for lower costs, but concerns about connector reliability come up frequently. One Reddit user wrote: “I have lost credibility with clients… regularly had data not being retrieved.” Slow customer service is another recurring complaint.
dlt gets praise for its documentation and community. “Documentation is clear, Slack community support is outstanding” is representative of the sentiment. Users highlight standardized ingestion scripts and lightweight deployment, with one team reporting a 182x reduction in ETL costs and 10x faster syncs after migrating from Fivetran.
Decision framework
Choose Fivetran when:
- Your data team is small and wants zero maintenance burden
- You have enterprise budget ($12K+/year minimum)
- Compliance requirements demand vendor-provided certifications
- Reliability matters more than cost
- You’re not loading high-volume marketing data (MAR costs add up fast)
Choose Airbyte when:
- You have strong engineering or DevOps capability
- You’re cost-conscious but willing to invest in setup and maintenance
- You need custom connectors and have the skills to build them
- Your team has Kubernetes experience
- You want lower costs than Fivetran with more features than dlt
As one Reddit commenter put it, Airbyte is best for “technical teams who see limitations as challenges to overcome.”
Choose dlt when:
- Your team is Python-proficient
- You want infrastructure-only costs (lowest total cost of ownership)
- You need rapid prototyping and serverless deployments
- You want maximum control over your pipeline code
- You’re starting fresh and can build practices around code-first ingestion
dlt fits best for greenfield projects where you’re willing to trade operational convenience for cost savings and control. If you want to try it, my hands-on dlt guide walks through a first pipeline.
The bigger picture
The data ingestion market is consolidating. The Fivetran-dbt merger signals that vendors see value in owning more of the data stack. For teams watching their budgets, this consolidation and the pricing changes that preceded it make the build versus buy calculation worth revisiting.
Most teams will end up with a hybrid approach. Use managed connectors for stable, high-value sources where reliability justifies the cost. Build custom pipelines with dlt or Airbyte for sources where you need control, have unique requirements, or can’t stomach the MAR charges.
What’s changed in 2026 isn’t that one tool is definitively better. The cost of managed solutions has increased enough that building is no longer obviously the wrong choice. With Python libraries like dlt and AI coding assistants reducing development time, the break-even point has shifted.
Choose based on your team’s skills, your budget constraints, and your tolerance for operational burden. And maybe revisit the decision annually, because this market isn’t done evolving.