> ## Documentation Index
> Fetch the complete documentation index at: https://docs.langchain.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Cloud platform features
> Cloud-only platform features for LangSmith Deployment, including data regions, static IP addresses, payload limits, and deployment types.
This page describes the platform features that apply only to [Cloud](/langsmith/cloud) deployments. For self-hosted equivalents, see [Deploy to self-hosted](/langsmith/deploy-to-self-hosted-overview).
## Data region
Deployments can be created in two data regions: US and EU.
The data region for a deployment is implied by the data region of the LangSmith organization where the deployment is created. Deployments and the underlying database for the deployments cannot be migrated between data regions.
## Static IP addresses
All traffic from deployments created after January 6, 2025 comes through a NAT gateway. This NAT gateway has several static IP addresses depending on the data region. For the list of static IP addresses, see the [Allowlist IP addresses table](/langsmith/deploy-to-cloud#allowlist-ip-addresses).
## Payload size
The maximum payload size for all requests sent to Cloud deployments is 25 MB. A request with a payload larger than 25 MB returns a `413 Payload Too Large` error.
## Deployment types
For simplicity, the control plane offers two deployment types with different resource allocations: `Development` and `Production`.
| **Deployment Type** | **CPU/Memory** | **Scaling** | **Database** |
| ------------------- | --------------- | ----------------- | -------------------------------------------------------------------------------- |
| Development | 1 CPU, 1 GB RAM | Up to 1 replica | 10 GB disk, no backups |
| Production | 2 CPU, 2 GB RAM | Up to 10 replicas | Autoscaling disk, automatic backups, highly available (multi-zone configuration) |
CPU and memory resources are per replica.
**Immutable deployment type**
Once a deployment is created, the deployment type cannot be changed.
### Production
`Production` type deployments are suitable for production workloads. For example, select `Production` for customer-facing applications in the critical path.
Resources for `Production` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. Contact support via [support.langchain.com](https://support.langchain.com) to request an increase in resources.
### Development
`Development` type deployments are suitable for development and testing. For example, select `Development` for internal testing environments. `Development` type deployments are not suitable for production workloads.
**Preemptible compute infrastructure**
`Development` type deployments (API server, queue server, and database) are provisioned on preemptible compute infrastructure. This means the compute infrastructure **may be terminated at any time without notice**. This may result in intermittent:
* Redis connection timeouts/errors
* Postgres connection timeouts/errors
* Failed or retrying background runs
This behavior is expected. Preemptible compute infrastructure **significantly reduces the cost to provision a `Development` type deployment**. By design, Agent Server is fault-tolerant. The implementation automatically attempts to recover from Redis/Postgres connection errors and retry failed background runs.
`Production` type deployments are provisioned on durable compute infrastructure, not preemptible compute infrastructure.
Database disk size for `Development` type deployments can be manually increased on a case-by-case basis depending on use case and capacity constraints. For most use cases, [TTLs](/langsmith/configure-ttl) should be configured to manage disk usage. Contact support via [support.langchain.com](https://support.langchain.com) to request an increase in resources.
## Database provisioning
The control plane and [data plane](/langsmith/data-plane) listener application coordinate to automatically create a Postgres database for each Cloud deployment. The database serves as the [persistence layer](/oss/python/langgraph/persistence#memory-store) for the deployment.
When implementing a LangGraph application, a [checkpointer](/oss/python/langgraph/persistence#checkpointer-libraries) does not need to be configured. A checkpointer is automatically configured for the graph. Any checkpointer configured for a graph is replaced by the one that is automatically configured.
There is no direct access to the database. All access to the database occurs through the [Agent Server](/langsmith/agent-server).
The database is never deleted until the deployment itself is deleted.
For self-hosted deployments, see [custom PostgreSQL configuration](/langsmith/self-hosted-platform-features#custom-postgresql).
## Scaling
Cloud deployments autoscale automatically; you don't configure queue workers, replicas, or pool sizes directly. `Production` deployments scale up to 10 replicas based on three metrics:
* **CPU utilization** — autoscaler targets 75%.
* **Memory utilization** — autoscaler targets 75%.
* **Pending runs** — autoscaler targets 10 pending runs per container.
Each metric is computed independently and the scaling action follows the metric that requires the largest number of containers. Scale-down actions wait 30 minutes before taking effect to avoid thrashing under bursty load. [Queue workers](/langsmith/agent-server#runtime-architecture) scale on pending run count while [API servers](/langsmith/agent-server#runtime-architecture) scale on CPU and memory, so read traffic does not slow down run submission and vice versa.
Application-level scaling levers (durability modes, async patterns, avoiding synchronous blocking, using `/join` instead of polling) apply to Cloud the same as to self-hosted. See [Scaling on self-hosted](/langsmith/agent-server-scale) for the underlying concepts; the Helm and resource configurations there do not apply to Cloud.
***
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