AWS DMS VS dlt

AWS Database Migration Service (DMS) was introduced as a tool to help migrate legacy databases into AWS. While it supports full-load and CDC replication, DMS is not a modern integration platform. It lacks support for modern SaaS APIs, streaming destinations, and developer-friendly deployment models.

• Available in AWS: Works within AWS without provisioning servers.
• Basic CDC support: Handles incremental replication from supported databases.

• Not real-time: DMS is not a streaming system. Latency varies and is hard to monitor in production.
• No extensibility: No community ecosystem, no custom connectors, no plugin support.
• Operational overhead: Task failures are common and require manual troubleshooting. Configuration and credential management are fragile.
• Rigid delivery options: Cannot deliver to modern analytics stacks or streaming endpoints.
• Expensive at scale: Costs add up with replication instance hours, storage, and logging. Lacks predictability for long-term CDC jobs.

Costs are based on replication instance size and duration (e.g., t3.medium ~$0.036/hr), plus storage and logs. Tasks that run continuously or process high volumes can become costly without offering the capabilities of modern platforms. There is no free tier, and pricing becomes opaque with additional monitoring and retries.

dlt is an open-source Python library (Apache 2.0) from dltHub for loading data from APIs, databases and files into warehouses, lakes and vector stores. You write pipelines as Python code, and dlt handles schema inference, normalization, incremental loading and load state. dltHub is the company's commercial platform, adding a managed runtime, data quality checks, transformations and an AI harness for coding agents.

dlt does not run a continuous service. You schedule and operate pipelines yourself, or pay for dltHub's managed runtime. That makes it a strong fit for Python-first teams that want code-level control, and a different category from a managed CDC and streaming platform.

• Code-first flexibility: Any Python generator can become a source, so long-tail APIs and custom logic are easy to model.
• Open source, Apache 2.0: The core library is free, with no per-row or per-connector fees.
• Schema inference and evolution: dlt infers schemas, normalizes nested data and handles evolution, with data contracts for control.
• Broad destination coverage: Warehouses, lakes (Delta, Iceberg), databases, DuckDB/MotherDuck, ClickHouse and vector stores.
• Fits existing workflows: Runs in Airflow, Dagster or CI and works well with coding agents for building pipelines quickly.

• You run it: The open-source library has no scheduler, monitoring or hosted runtime; production operations are your team's job.
• Batch-run, limited CDC: Freshness is bounded by pipeline schedules. Log-based CDC exists for Postgres only, and there is no sub-second delivery model.
• No durable replay: Adding a destination or backfilling means re-extracting from the source.
• Key capabilities are paid: Data quality checks, managed runtime, premium sources, and dbt/Python transformations sit in dltHub, which lists at $12,000/month with a 12-month minimum.
• Every connector is code: Maintenance, testing, and upgrades per pipeline fall on your engineers, and there is no no-code setup.

dlt the library is free under Apache 2.0, so you pay only for the infrastructure you run it on and the engineering time to maintain it. dltHub, the commercial platform, bills in credits, where one credit is roughly one hour of runtime. The managed plan lists at $12,000 per month for 5,000 credits on a 12-month minimum, with overage priced per credit on volume tiers. Enterprise is custom.

For those looking for dlt alternatives, it's worth considering platforms with managed connectors, continuous CDC, built-in replay and transformations without writing pipeline code.

Estuary is a managed data integration platform for CDC, streaming, and batch pipelines. It supports database CDC, SaaS and API ingestion, event streams, files, warehouses, data lakes, operational systems, and AI destinations, with delivery ranging from real-time streaming to scheduled batch intervals.

At the center of Estuary is its runtime, where captured data is written to durable collections and can be independently read by multiple destinations and transformations. This separates source capture from downstream delivery and allows data to be reused for additional destinations, backfills, and reprocessing without repeatedly extracting it from the source.

Estuary offers 200+ fully managed connectors, no-code capture and materialization setup through the web application, and streaming transformations using SQL, Python, and TypeScript. It also supports dbt Cloud, materialization triggers, Agent Skills for AI-assisted pipeline development, and Public, Private, and Bring Your Own Cloud (BYOC) deployment options.

• CDC, streaming, and batch in one platform: Estuary supports log-based CDC, SaaS and API ingestion, event streams, files, and scheduled batch movement for analytical and operational use cases.
• Real-time runtime with durable collections: Estuary's runtime separates captures from materializations through durable collections, allowing data to be reused for multiple destinations, transformations, backfills, and replay without re-extracting it from the source.
• Low-latency delivery: Supported real-time pipelines can deliver data with sub-second latency, while delivery can also be scheduled when lower freshness is sufficient.
• No-code setup with advanced transformation options: Pipelines can be configured through the web application, while SQL, Python, and TypeScript transformations, dbt Cloud, APIs, and flowctl support more advanced workflows.
• Built-in monitoring and AI-assisted operations: Estuary includes logs, latency and usage metrics, pipeline alerts, OpenMetrics integrations, and Agent Skills for AI-assisted pipeline development and troubleshooting.
• Flexible deployment and predictable pricing: Public, Private, and BYOC deployments provide different levels of infrastructure control, while pricing is based on data volume and prorated connector task hours rather than Monthly Active Rows.

• Smaller managed connector catalog than some large ELT platforms: Estuary offers 200+ fully managed connectors, but some larger platforms have broader packaged coverage, particularly for long-tail SaaS and legacy enterprise applications. Custom connector development is available with Enterprise.
• No visual transformation canvas: Pipeline setup is no-code, and fields can be selected or renamed in the UI, but more complex streaming transformations use SQL, Python, or TypeScript rather than a drag-and-drop transformation canvas.
• Advanced workflows have a learning curve: Basic capture and materialization pipelines can be built through the web application, but advanced derivations, schemas, CLI workflows, and GitOps-style deployments require more data engineering familiarity. Some Estuary users on G2 also mention a learning curve for more advanced configurations.

Estuary pricing is based on data volume moved and connector instance usage. Data movement is priced at $0.50 per GB. The first six connector instances are priced at $100 per month each, while additional connector instances are $50 per month each, with usage prorated based on active connector instances.

The permanent free tier includes up to 10 GB of data movement per month and two concurrent connector instances, while the 30-day trial provides access to the full Cloud plan.

Enterprise plans can include volume discounts, Private or BYOC deployments, custom SLAs, private networking, dedicated support, and custom connector development.

Because pricing is based on GB moved plus connector usage rather than row-change metrics such as MAR, costs are relatively predictable from data volume and connector usage.