Breakthrough serverless platform makes data pipelines durable, scalable, and observable by default.
From the creators of Postgres and Apache Spark
Build Python or TypeScript data pipelines that extract, transform, load, and analyze data.
DBOS makes them crash-proof, observable, and scalable for you.
We’re not kidding, DBOS is effortless.
DBOS executes Python and TypeScript data pipelines orders of magnitude faster than other serverless platforms. Perfect for scheduled cron jobs, event-driven workflows, AI RAG applications, and more.
"With DBOS, developers can build applications in days that now take months on conventional platforms."
Build data pipelines in Python or TypeScript; schedule them to run using crontab syntax.
Consume events from Apache Kafka topics with guaranteed exactly-once processing.
Durability eliminates AI data pipeline headaches like LLM timeouts and rate limiting.
Include manual steps in your data pipeline workflows. DBOS handles async waiting and timeouts for you.
Unlike AWS Lambda, DBOS does not charge for time your code spends waiting for LLM responses...that adds up to big savings. This benchmark shows the cost difference.
Tutorial showing how to use DBOS and LlamaIndex to build an interactive RAG pipeline and serverlessly deploy it to the cloud in just 9 lines of code.
We’ll do our best to cover all bases.
In case you have additional questions about agentic AI, speak with our team.
A data pipeline is a series of programmatic processes that move data from various sources, transform it as needed, and store it in a format or system, where it can be analyzed. .
WIth DBOS, you code your data pipelines and other data engineering workflows in Python or TypeScript, just as you normally would, and then add simple decorators which instruct DBOS on how to provide access to them (endpoints) and how to execute them durably with guaranteed exactly-one processing.
Data pipelines are often used to automate the delivery and analysis of data used in real-time automation and mission-critical decision making.
With so much riding on the success of your data pipelines, durability ensures that they execute the way they are intended to, even if they are interrupted by technical glitches, or if they have to wait a long time for humans in the loop.
DBOS makes your data pipelines durable, simply by adding a few annotations to your Python or TypeScript code, DBOS ensures that workflows execute durably. If they are interrupted, they automatically resume executing where they left off when restarted. Besides ensuring that your data pipelines execute durably, DBOS reduces the amount of coding and technical debt it would normally require to ensure durable execution.
DBOS is a serverless compute platform built to radically simplify data pipeline development and execution. Besides providing a platform on which to host and run your data pipelines, DBOS makes the following automatic:
Scalability - Scale from zero to millions of requests in seconds--and back to zero when not in use. Only pay for what you use.
Durability - DBOS ensures your data pipelines execute the way the are intended to, no matter what. If they are interrupted by a technical glitch, DBOS automatically restarts them and resumes execution from where it left off before the interruption.
Observability - DBOS outputs OpenTelemetry traces and metrics for your data pipelines, so you have complete visibility into their execution history. It makes troubleshooting, auditing, and performance optimization much easier.
In addition to making your data pipelines more durable, scalable, and observable, DBOS is the only serverless platform that does NOT not charge you for time your code spends waiting for backend responses. This saves you a lot of money compared to other platforms like AWS Lambda.
DBOS runs standard Python or TypeScript code. Your applications can interface with any backend system via API calls, or directly access Postgres-compatible databases via SQL.
DBOS ensures that workflows orchestrating calls to remote services and backend databases always execute durably, with OpenTelemetry observability traces output by default for easy troubleshooting and auditing.