Accelerate your AI R&DExplore pre-built apps and functions, or work with the Sieve team to tackle your exact problem.
Scale to productionMinimize infrastructure setup while maintaining full flexibility on how you run your workloads at scale.
Collaborate with your entire teamEnable PMs, data scientists, and software engineers to interact with ML apps collaboratively.
a fully featured ML runtimeDeploy your own apps
Define some codeDon't worry about GPU configuration, CUDA versions, or Docker. Take regular Python code and add a simple decorator to it.
Deploy instantly, call from anywhereDeploy code to Sieve in a single command. Call it via API, Python SDK, or the dashboard.
Call pre-existing functions and modelsLeverage the library of functions and models built by the Sieve community. Call them from your own code, the Sieve dashboard, or use them in your custom workflows.
Build complex workflowsMost apps require many functions and models chained together in non-trivial ways. Use a combination of existing building blocks and your own code to build the solution you want.
Built to scaleForget about infrastructure
AutoscalingSieve scales to meet your demand while giving you granular control over replica counts and other useful metrics.
Fast data transferQuick processing across clouds due to specialized rich data transfer via Sieve data types.
Stream and batch processingUse Sieve's iterator support to stream results from jobs, or launch massively parallel jobs to process large datasets.
Full featured observabilitySeamless viewing of logs, traffic, and latency by jobs, apps, models, or functions.
Built for teamsFor engineers and non-engineers alike
Organization SupportAdd your team members to a shared workspace of Sieve apps.
Single source of truthAll your functions and models in one place: reusable, version controlled, and shared across applications.
Cross-functional collaborationTrigger apps, visualize results, and share with your entire team using Sieve's web-based dashboard or CLI.