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These settings size the API and Brainstore compute for your workload. The defaults are suitable for most deployments. Tune them for high ingestion volume, heavy eval workloads, or cluster capacity constraints.

Configure AWS API ECS services

On AWS, Terraform module v6.0 introduces ECS as the target runtime for the API, split into three services by workload type. Each service scales automatically through Application Auto Scaling, adjusting its running task count between a minimum and maximum based on multiple metrics, including CPU target tracking and event loop timing. The module exposes optional variables to tune per-task size and task counts. The defaults are suitable for most deployments. See Upgrade to Terraform module v6.0 for the migration. 1024 CPU units equal 1 vCPU. braintrust-api handles general API traffic. braintrust-api-ingest handles ingestion paths (/logs3, /otel/v1/traces). braintrust-api-background handles background paths (evals, function invocation, proxy). For example, to raise the ingestion service’s floor for a high-volume deployment:
Raise braintrust_api_ingest_min_count for deployments with spiky ingestion volume or if you expect a launch that will suddenly increase log traffic, and braintrust_api_background_min_count for heavy eval workloads.

Configure Helm API workload isolation

This feature is available to Kubernetes deployments using Helm chart 6.13.0+. AWS ECS deployments use the Terraform-managed routing described above.
Setting api.workloadIsolation.enabled: true creates dedicated braintrust-api-ingest and braintrust-api-background Deployments and Services alongside the default braintrust-api pool. The pools share the same image and base configuration while allowing independent replica counts, resources, probes, rollout settings, and disruption budgets, so classified ingestion or background load does not consume the default API pool’s capacity. Your ingress or gateway must keep braintrust-api as its default backend and route the following paths to the specialized pools: If your ingress cannot match requests by HTTP method (for example, GKE Ingress), route the listed paths for all methods instead. Workload isolation uses fixed capacity and does not enable autoscaling. Configure replica counts with api.replicas, api.workloadIsolation.ingest.replicas, and api.workloadIsolation.background.replicas.

Roll out workload isolation for an existing deployment

For an existing deployment, stage the rollout so you can verify each pool before routing traffic to it:
  1. Create the pools without routing traffic to them or switching Brainstore’s internal AI proxy. Set api.workloadIsolation.brainstoreAiProxyToBackground: false and apply:
  2. Verify the ingest and background pools are ready. Then update your ingress or gateway to route the classified paths to the new Services, and set brainstoreAiProxyToBackground: true to switch Brainstore’s internal AI proxy to the background pool:
To roll back, first return the classified paths and brainstoreAiProxyToBackground to the default API Service and verify it is serving them, then disable workload isolation in the chart. The chart cannot update an external ingress or gateway on its own, so disabling isolation before rerouting would leave the classified paths pointed at Services that no longer exist.

Integrate with the Istio VirtualService

When using the chart-managed Istio VirtualService, set virtualService.workloadIsolation.enabled: true after the isolated pools are healthy. The chart then renders the ingest and background route contract before your existing virtualService.http rules. The classified routes take precedence, so do not use virtualService.http to override a classified path.
Enable this only after the ingest and background pools are running and ready.
This option requires both virtualService.enabled: true and api.workloadIsolation.enabled: true. The chart fails to render if either is missing.

Configuration reference

Default pool availability settings

Helm chart 6.13.0 or later also exposes availability settings for the default braintrust-api pool. The defaults preserve the chart’s previous behavior, so no configuration changes are required when upgrading. The ingest and background pools inherit these values as their base configuration.

Configure Brainstore fast readers

Fast readers are isolated Brainstore nodes dedicated to serving predictable UI queries (paginated viewers, span and trace lookups), preventing resource-intensive ad-hoc queries from making the UI unresponsive.
  • GCP and Azure: Fast readers are enabled by default starting in Helm chart v5.0.0. See the configuration reference below.
  • AWS: Fast readers are enabled by default (2 nodes) starting in Terraform module v5.5.0. On earlier module versions they are disabled by default. Set brainstore_fast_reader_instance_count in your Terraform configuration to control the node count, or set it to 0 to opt out (recommended for sandbox or non-production deployments).
Upgrading to Helm chart v5.0.0 from an earlier version automatically creates fast reader nodes. By default, 2 fast reader nodes are created with the same resource profile as standard reader nodes (CPU: 16, memory: 32Gi). Verify that your cluster has capacity for these additional nodes before upgrading.
If you have custom brainstore.readinessProbe overrides pointing to /status, remove them before upgrading to Helm chart v5.0.0+. The /status readiness endpoint has a bug where it never recovers after a failure, which can permanently mark Brainstore nodes as not ready. Remove any brainstore.readinessProbe or brainstore.fastreader.readinessProbe customizations and rely on the chart defaults.

Configuration reference

Fast readers are configured under the brainstore.fastreader key in your values.yaml. If you have customized brainstore.reader settings, mirror those customizations to brainstore.fastreader.
Azure users must explicitly set brainstore.fastreader.volume.size when using Azure Container Storage (enableAzureContainerStorageDriver: true):

Brainstore resource configuration

This section applies to GCP and Azure deployments using the Helm chart (v5.0.1+). AWS deployments manage Brainstore resources automatically.
Starting in Helm chart v5.0.1, the resources block for each Brainstore component (brainstore.reader, brainstore.writer, brainstore.fastreader) is passed through as-is to the Kubernetes pod spec. You can omit limits entirely, set them to {}, or supply any valid Kubernetes resource spec.
Omitting limits sets the pod QoS class to Burstable, which prevents CPU throttling and allows pods to use available node capacity. This can improve query performance on nodes with spare capacity, but increases the risk of resource contention if multiple pods compete for the same node.

Auto-derived Brainstore environment variables

As of Helm chart v5.1.0, BRAINSTORE_RESPONSE_CACHE_URI and BRAINSTORE_CODE_BUNDLE_URI are automatically populated from your objectStorage configuration and do not need to be set manually. The chart derives these values as follows: If you previously configured these via extraEnvVars, remove those overrides after upgrading to v5.1.0 to avoid conflicts.