Pricing Overview
dlt (data load tool) follows a freemium model that separates the open-source library from the managed platform. The core dlt library is free and Apache 2.0 licensed permanently, giving teams full control over their data pipelines without any cost. You can install it via pip and run it anywhere Python runs, including Airflow, serverless functions, and Jupyter Notebooks, with no backend or container dependencies. For teams that need managed infrastructure, dltHub offers three paid tiers starting at $100/month. The platform adds runtime execution, observability dashboards, data quality checks, and AI-assisted tooling on top of the open-source foundation. Annual billing saves 17%, bringing the Pro plan to $1,000/year and Scale to $10,000/year. Enterprise organizations get custom pricing with governance controls and SLA guarantees. With over 6 million PyPI downloads and 8,000 OSS companies in production, dlt has established itself as the leading Python-native data loading library.
Plan Comparison
| Feature | dlt OSS | dltHub Pro | dltHub Scale | Enterprise |
|---|---|---|---|---|
| Price | Free | $100/month | $1,000/month | Custom |
| Annual Price | Free | $1,000/year | $10,000/year | Custom |
| Credits/Month | N/A | 100 | 1,000 | Custom |
| Developers | Unlimited (self-managed) | Up to 3 | Up to 30 | Custom |
| View-Only Users | N/A | Up to 10 | Up to 100 | Custom |
| Managed Runtime | No | Yes | Yes | Yes |
| AI Tooling | Community support | Basic | Advanced | Advanced |
| Data Quality Checks | No | Yes | Yes | Yes |
| Observability Dashboard | No | Yes | Yes | Yes |
| Free Trial | N/A | 30 days | 30 days | N/A |
| Security & Governance | Self-managed | Standard | Standard | Enterprise-grade |
The dlt OSS tier covers everything a solo developer or small team needs for data ingestion, including schema inference, incremental loading, and data normalization. We consider dltHub Pro the sweet spot for small data teams that want managed runtime without operational overhead. At $100/month with 100 credits included, it delivers observability and data quality monitoring that would otherwise require significant engineering investment to build in-house. The Scale tier unlocks higher concurrency, advanced AI tooling, and support for larger teams of up to 30 developers with 1,000 monthly credits. Enterprise adds custom onboarding, architecture guidance, and tailored SLA options for security-focused organizations in regulated industries like finance and healthcare.
Hidden Costs and Considerations
The free OSS tier requires teams to manage their own infrastructure, scheduling, and monitoring, which translates to real engineering hours that should be factored into any total cost of ownership calculation. Credit-based pricing on Pro and Scale plans means heavy pipeline workloads could push teams into overage territory or force an upgrade sooner than planned. The 3-developer cap on Pro is particularly restrictive for growing teams and may accelerate the jump to the $1,000/month Scale tier sooner than expected. Annual contracts lock in savings at 17% but require upfront commitment. Teams should also consider that dltHub is still building out its managed platform, with the individual developer release coming in 2026, so feature availability may evolve as the product matures.
Cost Estimates by Team Size
| Team Size | Recommended Plan | Monthly Cost | Annual Cost | Notes |
|---|---|---|---|---|
| Solo developer | dlt OSS | $0 | $0 | Self-hosted, full control |
| 2-3 developers | dltHub Pro | $100 | $1,000 | 100 credits, managed runtime |
| 5-15 developers | dltHub Scale | $1,000 | $10,000 | 1,000 credits, advanced AI tooling |
| 20-30 developers | dltHub Scale | $1,000 | $10,000 | Nearing developer cap, evaluate Enterprise |
| 30+ developers | Enterprise | Custom | Custom | Custom credits, governance, SLA |
For a small data team of 2-3 engineers running standard ingestion workloads, the Pro plan at $100/month delivers strong value with managed runtime and observability included. Teams outgrowing the 3-developer limit face a 10x price increase to Scale at $1,000/month, which we recommend planning for early in the budgeting cycle. The annual billing option can soften this transition, reducing Scale to $10,000/year compared to $12,000 on monthly billing. Solo developers and data scientists exploring data loading should start with the free OSS tier, which has no usage limits and supports 60+ pre-built verified sources.
How dlt Pricing Compares
| Tool | Pricing Model | Starting Price | Best For |
|---|---|---|---|
| dlt | Freemium (open-source core) | Free / $100/mo (managed) | Python-first teams wanting full pipeline control |
| Airbyte | Freemium (open-source core) | Free / $10/mo (cloud) | Teams needing 600+ pre-built connectors |
| Stitch | Freemium | $25/mo | Small teams wanting simple, managed ELT |
| Hevo Data | Freemium | $25/mo | Teams needing no-code data integration |
dlt stands apart as the only Python-native library in this comparison, meaning teams write and own their pipeline code directly rather than configuring connectors through a web interface. Airbyte offers the lowest cloud entry point at $10/month with a massive catalog of 600+ connectors, but its managed plans can scale up to $5,000/month for heavy workloads. Stitch and Hevo Data both start at $25/month with more traditional managed approaches and row-based pricing models. We find dlt most compelling for teams already invested in Python tooling who want to avoid vendor lock-in, since the open-source core runs anywhere Python does, from Airflow to serverless functions to Jupyter Notebooks. The trade-off is clear: dlt gives you maximum flexibility and code ownership at the cost of more hands-on pipeline management, while competitors like Stitch and Hevo Data prioritize ease of setup with less customization. For teams that value Python-first workflows and want to keep infrastructure costs predictable, dlt delivers the strongest value proposition in this category.