Deploy model inference, durable task queues and isolated agent sandboxes on Beam or your own cloud infrastructure.
Beam provides compute infrastructure for AI applications rather than a single end-user model. Developers can deploy inference endpoints, queue background work and create isolated environments for agents. The service manages execution and scaling while application code defines the models, libraries and resources required by the workload.
Serverless endpoints can run custom models on CPUs or GPUs and scale according to demand. Developers specify dependencies and hardware through the supported tooling, with options for custom container images. Task queues add retries, callbacks and scheduled execution for work such as transcription or data processing. Logging, monitoring and secrets management support the operational side of an application.
Beam distinguishes serverless execution from on-demand machines and reserved clusters. Serverless pricing charges for active compute, while an on-demand machine includes its stated hardware resources. A GPU-only headline rate should therefore not be treated as the complete price of a serverless endpoint with additional CPU and memory.
Agent sandboxes provide stateful execution environments, persistent storage and filesystem snapshots. A saved environment can be restored for repeated or parallel work. The platform also supports bringing an existing cloud account, allowing organisations to use their own infrastructure and cloud credits while Beam manages orchestration. This arrangement carries management fees in addition to the underlying cloud bill.
The Developer tier has no monthly platform subscription but still charges for compute usage. Paid team tiers expand collaboration or concurrency limits, and larger deployments can use custom arrangements. Resource choice, active duration and whether a service stays warm all affect cost. Teams should measure a representative request, inspect startup and processing time, and test failure behaviour before setting production scaling limits. Beam supplies the execution layer, but model licensing, application security and the appropriateness of generated output remain the responsibility of the team deploying the workload. This makes it a fit for engineers who need flexible infrastructure rather than a ready-made consumer assistant.
