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# Configuration: Full Provider and Virtual Key Reference
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# Usage: helm install bifrost ./bifrost -f values-examples/providers-and-virtual-keys.yaml
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#
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# This example demonstrates configuration for every Bifrost-supported provider
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# (23 total) plus 7 virtual-key patterns covering different access-control needs:
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# - Simple API-key providers (openai, anthropic, cohere, groq, gemini, ...)
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# - Deployment-map providers (huggingface, replicate)
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# - URL-based / self-hosted providers (ollama, sgl, vllm)
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# - Cloud-native providers with nested config (azure, vertex, bedrock)
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#
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# Secrets are referenced via env.VAR_NAME (see ENVIRONMENT VARIABLES block
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# below). Provide them via extraEnv (map), env, envFrom, or a Kubernetes Secret
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# mounted on the pod — see values-examples/secrets-from-k8s.yaml for patterns.
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#
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# Field names follow transports/config.schema.json (the Bifrost runtime config
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# contract). VK provider_configs use the helm-native keys:[{name:...}] form,
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# which the helm chart template passes through to config.json.
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# ==========================================================================
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# ENVIRONMENT VARIABLES REFERENCED
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# ==========================================================================
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# This file uses env.VAR_NAME for all secret values. Supply them via extraEnv
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# (map), env, envFrom, or an external secret store. Full list:
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#
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# Provider API keys:
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# OPENAI_API_KEY_1, OPENAI_API_KEY_2, OPENAI_API_KEY_3
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# ANTHROPIC_API_KEY_1, ANTHROPIC_API_KEY_2
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# GROQ_API_KEY_1, GROQ_API_KEY_2
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# COHERE_API_KEY, MISTRAL_API_KEY, GEMINI_API_KEY, OPENROUTER_API_KEY
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# PARASAIL_API_KEY, PERPLEXITY_API_KEY, CEREBRAS_API_KEY
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# ELEVENLABS_API_KEY, XAI_API_KEY, NEBIUS_API_KEY, FIREWORKS_API_KEY
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# RUNWAY_API_KEY, HUGGINGFACE_API_KEY, REPLICATE_API_KEY
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#
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# Azure:
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# AZURE_API_KEY, AZURE_ENDPOINT
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#
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# Vertex (Google Cloud):
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# VERTEX_PROJECT_ID, VERTEX_AUTH_CREDENTIALS (service-account key JSON)
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#
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# Bedrock (AWS) — choose static creds OR STS AssumeRole:
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# AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY
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# AWS_ROLE_ARN, AWS_EXTERNAL_ID
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#
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# Self-hosted endpoints:
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# OLLAMA_URL, SGL_URL, VLLM_URL
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# Image configuration
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image:
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repository: docker.io/maximhq/bifrost
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pullPolicy: IfNotPresent
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tag: "v1.3.55"
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replicaCount: 1
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# Service
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service:
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type: ClusterIP
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port: 8080
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# Storage configuration - using SQLite for simplicity
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storage:
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mode: sqlite
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persistence:
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enabled: true
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size: 5Gi
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configStore:
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enabled: true
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logsStore:
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enabled: true
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# No PostgreSQL needed for this example
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postgresql:
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enabled: false
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# No vector store for this example
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vectorStore:
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enabled: false
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type: none
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# Bifrost configuration
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bifrost:
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appDir: /app/data
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port: 8080
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host: 0.0.0.0
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logLevel: info
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logStyle: json
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client:
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dropExcessRequests: false
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initialPoolSize: 100
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allowedOrigins:
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- "*"
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enableLogging: true
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enforceGovernanceHeader: false
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maxRequestBodySizeMb: 100
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# ==========================================================================
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# PROVIDERS
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# ==========================================================================
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# Every key entry supports the base fields:
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# name (required), value, weight (optional; defaults to 1), models, use_for_batch_api, aliases
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# Providers with nested configs add *_key_config blocks
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# (azure_key_config, vertex_key_config, bedrock_key_config, vllm_key_config,
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# ollama_key_config, sgl_key_config, replicate_key_config).
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providers:
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# ------------------------------------------------------------------------
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# Simple API-key providers (base_key shape)
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# ------------------------------------------------------------------------
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# OpenAI — 3 keys with weighted load balancing.
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# openai-batch is flagged use_for_batch_api so it can serve the Batch API.
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openai:
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keys:
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- name: "openai-primary"
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value: "env.OPENAI_API_KEY_1"
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weight: 2 # 50% of traffic (2 of 4 total weight)
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models: ["*"]
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- name: "openai-secondary"
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value: "env.OPENAI_API_KEY_2"
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weight: 1 # 25%
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models: ["*"]
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- name: "openai-batch"
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value: "env.OPENAI_API_KEY_3"
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weight: 1 # 25%
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models: ["*"]
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use_for_batch_api: true # Allow Batch API with this key
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# Anthropic — 2 keys, equal weight
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anthropic:
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keys:
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- name: "anthropic-primary"
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value: "env.ANTHROPIC_API_KEY_1"
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weight: 1
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models: ["*"]
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- name: "anthropic-secondary"
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value: "env.ANTHROPIC_API_KEY_2"
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weight: 1
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models: ["*"]
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# Groq — 2 keys
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groq:
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keys:
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- name: "groq-primary"
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value: "env.GROQ_API_KEY_1"
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weight: 1
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models: ["*"]
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- name: "groq-secondary"
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value: "env.GROQ_API_KEY_2"
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weight: 1
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models: ["*"]
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cohere:
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keys:
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- name: "cohere-main"
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value: "env.COHERE_API_KEY"
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weight: 1
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models: ["*"]
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mistral:
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keys:
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- name: "mistral-main"
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value: "env.MISTRAL_API_KEY"
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weight: 1
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models: ["*"]
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gemini:
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keys:
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- name: "gemini-main"
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value: "env.GEMINI_API_KEY"
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weight: 1
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models: ["*"]
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openrouter:
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keys:
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- name: "openrouter-main"
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value: "env.OPENROUTER_API_KEY"
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weight: 1
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models: ["*"]
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parasail:
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keys:
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- name: "parasail-main"
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value: "env.PARASAIL_API_KEY"
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weight: 1
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models: ["*"]
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perplexity:
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keys:
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- name: "perplexity-main"
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value: "env.PERPLEXITY_API_KEY"
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weight: 1
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models: ["*"]
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cerebras:
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keys:
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- name: "cerebras-main"
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value: "env.CEREBRAS_API_KEY"
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weight: 1
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models: ["*"]
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elevenlabs:
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keys:
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- name: "elevenlabs-main"
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value: "env.ELEVENLABS_API_KEY"
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weight: 1
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models: ["*"]
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xai:
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keys:
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- name: "xai-main"
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value: "env.XAI_API_KEY"
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weight: 1
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models: ["*"]
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nebius:
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keys:
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- name: "nebius-main"
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value: "env.NEBIUS_API_KEY"
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weight: 1
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models: ["*"]
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fireworks:
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keys:
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- name: "fireworks-main"
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value: "env.FIREWORKS_API_KEY"
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weight: 1
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models: ["*"]
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runway:
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keys:
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- name: "runway-main"
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value: "env.RUNWAY_API_KEY"
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weight: 1
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models: ["*"]
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# ------------------------------------------------------------------------
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# Deployment-map providers (use `aliases` to map logical -> provider IDs)
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# ------------------------------------------------------------------------
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huggingface:
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keys:
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- name: "huggingface-main"
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value: "env.HUGGINGFACE_API_KEY"
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weight: 1
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models: ["llama-3", "mixtral"]
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aliases:
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# Logical model name -> HF repo path used when invoking
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llama-3: "meta-llama/Meta-Llama-3-8B-Instruct"
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mixtral: "mistralai/Mixtral-8x7B-Instruct-v0.1"
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replicate:
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keys:
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- name: "replicate-main"
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value: "env.REPLICATE_API_KEY"
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weight: 1
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models: ["llama-3"]
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aliases:
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llama-3: "meta/meta-llama-3-70b-instruct"
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replicate_key_config:
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use_deployments_endpoint: false # false = /models endpoint (default)
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# ------------------------------------------------------------------------
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# URL-based / self-hosted providers
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# ------------------------------------------------------------------------
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# These providers talk to an HTTP endpoint you operate yourself. They do
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# not typically require API keys (value stays empty).
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ollama:
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keys:
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- name: "ollama-main"
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value: ""
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weight: 1
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models: ["*"]
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ollama_key_config:
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url: "env.OLLAMA_URL" # e.g. http://ollama.svc.cluster.local:11434
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sgl:
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keys:
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- name: "sgl-main"
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value: ""
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weight: 1
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models: ["*"]
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sgl_key_config:
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url: "env.SGL_URL" # e.g. http://sgl-router.svc.cluster.local:30000
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vllm:
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# vLLM instances are model-specific: one key per served model.
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keys:
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- name: "vllm-llama3-70b"
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value: ""
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weight: 1
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models: ["llama-3-70b"]
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vllm_key_config:
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url: "env.VLLM_URL" # e.g. http://vllm.svc.cluster.local:8000
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model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
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# ------------------------------------------------------------------------
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# Cloud-native providers (nested provider-specific config)
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# ------------------------------------------------------------------------
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# Azure OpenAI — two auth modes:
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# 1. azure-apikey: explicit API key (via env var).
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# 2. azure-managed-identity: inherits credentials via DefaultAzureCredential
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# when `value` is empty. Covers managed identity
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# on Azure VMs / AKS workload identity / env vars
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# (AZURE_CLIENT_ID etc.) / Azure CLI (dev).
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# (Service-principal client_id/client_secret/tenant_id fields exist in the
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# runtime code but aren't exposed in the current schema — use env-based
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# DefaultAzureCredential instead.)
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azure:
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keys:
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- name: "azure-apikey"
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value: "env.AZURE_API_KEY"
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weight: 1
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models: ["gpt-4o", "gpt-4o-mini", "text-embedding-3-small"]
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azure_key_config:
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endpoint: "env.AZURE_ENDPOINT" # e.g. https://my-resource.openai.azure.com
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api_version: "2024-10-21"
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deployments:
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# Logical model name -> Azure deployment name
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gpt-4o: "gpt-4o-prod"
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gpt-4o-mini: "gpt-4o-mini-prod"
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text-embedding-3-small: "embeddings-prod"
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- name: "azure-managed-identity"
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# Pure identity inheritance: empty `value` triggers DefaultAzureCredential.
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# Works out-of-the-box on AKS with workload identity, Azure VMs with
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# system/user-assigned managed identity, or local dev via `az login`.
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value: ""
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weight: 1
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models: ["gpt-4o"]
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azure_key_config:
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endpoint: "env.AZURE_ENDPOINT"
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api_version: "2024-10-21"
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deployments:
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gpt-4o: "gpt-4o-prod"
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# Google Vertex AI — two auth modes:
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# 1. vertex-sa-key: explicit service-account key JSON via env var.
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# 2. vertex-workload-id: inherits credentials from the environment
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# (GKE Workload Identity, GCE metadata server,
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# or GOOGLE_APPLICATION_CREDENTIALS path). Omit
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# `auth_credentials` and the Google SDK calls
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# google.FindDefaultCredentials automatically.
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vertex:
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keys:
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- name: "vertex-sa-key"
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value: ""
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weight: 1
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models: ["*"]
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vertex_key_config:
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project_id: "env.VERTEX_PROJECT_ID"
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region: "us-central1"
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auth_credentials: "env.VERTEX_AUTH_CREDENTIALS"
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# project_number: "env.VERTEX_PROJECT_NUMBER" # optional
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- name: "vertex-workload-id"
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# Pure ADC inheritance: works on GKE with Workload Identity, GCE
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# VMs, Cloud Run, or local dev via `gcloud auth application-default login`.
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value: ""
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weight: 1
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||||
models: ["*"]
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vertex_key_config:
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project_id: "env.VERTEX_PROJECT_ID"
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region: "us-central1"
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||||
# auth_credentials intentionally omitted -> ADC lookup
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||||
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||||
# AWS Bedrock — three auth modes:
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# 1. bedrock-static: explicit AWS access/secret keys + S3 batch bucket
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# 2. bedrock-irsa: inherits pod/EKS credentials (IRSA, EC2 instance
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||||
# profile, env vars, ~/.aws/credentials) — set only
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||||
# `region`; the AWS SDK default credential chain
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||||
# resolves the rest.
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||||
# 3. bedrock-assumerole: STS AssumeRole chained on top of the default
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||||
# chain — inherits *source* creds from the pod,
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||||
# then assumes a cross-account role.
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bedrock:
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keys:
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- name: "bedrock-static"
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||||
value: ""
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||||
weight: 1
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||||
models: ["*"]
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||||
bedrock_key_config:
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||||
region: "us-east-1"
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||||
access_key: "env.AWS_ACCESS_KEY_ID"
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||||
secret_key: "env.AWS_SECRET_ACCESS_KEY"
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||||
deployments:
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||||
# Logical model -> Bedrock inference profile
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||||
anthropic.claude-3-5-sonnet: "us.anthropic.claude-3-5-sonnet-20240620-v1:0"
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||||
batch_s3_config:
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||||
buckets:
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||||
- bucket_name: "my-bedrock-batch-bucket"
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||||
prefix: "batch/"
|
||||
is_default: true
|
||||
- name: "bedrock-irsa"
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||||
# Pure credential inheritance: works out-of-the-box on EKS with IRSA,
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||||
# on EC2 with an instance profile, or with AWS_* env vars present.
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||||
value: ""
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||||
weight: 1
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||||
models: ["*"]
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||||
bedrock_key_config:
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||||
region: "us-east-1"
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||||
# access_key / secret_key intentionally omitted -> SDK default chain
|
||||
- name: "bedrock-assumerole"
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||||
value: ""
|
||||
weight: 1
|
||||
models: ["*"]
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||||
bedrock_key_config:
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||||
region: "us-west-2"
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||||
# No static creds -> source identity comes from pod's default chain.
|
||||
role_arn: "env.AWS_ROLE_ARN"
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||||
external_id: "env.AWS_EXTERNAL_ID"
|
||||
session_name: "bifrost-session"
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||||
|
||||
# ==========================================================================
|
||||
# GOVERNANCE — budgets, rate limits, and virtual keys
|
||||
# ==========================================================================
|
||||
|
||||
governance:
|
||||
# --------------------------------------------------------------------
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||||
# Budgets — spending caps per period
|
||||
# --------------------------------------------------------------------
|
||||
budgets:
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||||
- id: "budget-dev"
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||||
max_limit: 50 # $50
|
||||
reset_duration: "1M" # monthly
|
||||
- id: "budget-production"
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||||
max_limit: 500 # $500
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||||
reset_duration: "1M"
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||||
- id: "budget-testing"
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||||
max_limit: 10 # $10
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||||
reset_duration: "1d" # daily
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||||
- id: "budget-team-platform"
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||||
max_limit: 2000 # $2000 — larger team/platform budget
|
||||
reset_duration: "1M"
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Rate limits — token + request caps per period
|
||||
# --------------------------------------------------------------------
|
||||
rateLimits:
|
||||
- id: "rate-limit-standard"
|
||||
token_max_limit: 100000
|
||||
token_reset_duration: "1h"
|
||||
request_max_limit: 1000
|
||||
request_reset_duration: "1h"
|
||||
- id: "rate-limit-high"
|
||||
token_max_limit: 500000
|
||||
token_reset_duration: "1h"
|
||||
request_max_limit: 5000
|
||||
request_reset_duration: "1h"
|
||||
- id: "rate-limit-testing"
|
||||
token_max_limit: 10000
|
||||
token_reset_duration: "1h"
|
||||
request_max_limit: 100
|
||||
request_reset_duration: "1h"
|
||||
- id: "rate-limit-burst"
|
||||
token_max_limit: 50000
|
||||
token_reset_duration: "1m" # Short-window burst cap
|
||||
request_max_limit: 500
|
||||
request_reset_duration: "1m"
|
||||
|
||||
# --------------------------------------------------------------------
|
||||
# Virtual keys — access tokens scoped to providers/models/keys
|
||||
# --------------------------------------------------------------------
|
||||
# provider_configs[].keys scopes the VK to specific provider keys by name.
|
||||
# Omit provider_configs to grant access to every provider.
|
||||
# Omit keys inside a provider_config to allow all keys for that provider.
|
||||
|
||||
virtualKeys:
|
||||
# 1. Dev key — access to every provider, no restrictions.
|
||||
- id: "vk-all-providers-dev"
|
||||
name: "Dev: all providers"
|
||||
is_active: true
|
||||
budget_id: "budget-dev"
|
||||
rate_limit_id: "rate-limit-standard"
|
||||
# No provider_configs -> all providers accessible
|
||||
|
||||
# 2. OpenAI only — restricted to 2 keys and 2 models.
|
||||
- id: "vk-openai-scoped"
|
||||
name: "OpenAI only (scoped)"
|
||||
is_active: true
|
||||
budget_id: "budget-production"
|
||||
rate_limit_id: "rate-limit-high"
|
||||
provider_configs:
|
||||
- provider: "openai"
|
||||
weight: 1
|
||||
allowed_models: ["gpt-4o", "gpt-4o-mini"]
|
||||
keys:
|
||||
- name: "openai-primary"
|
||||
- name: "openai-secondary"
|
||||
|
||||
# 3. Multi-provider — weighted routing across OpenAI/Anthropic/Groq.
|
||||
# OpenAI gets 50% (weight 2), the others 25% each (weight 1).
|
||||
- id: "vk-multi-provider"
|
||||
name: "Multi-provider weighted"
|
||||
is_active: true
|
||||
budget_id: "budget-production"
|
||||
rate_limit_id: "rate-limit-high"
|
||||
provider_configs:
|
||||
- provider: "openai"
|
||||
weight: 2
|
||||
allowed_models: ["*"]
|
||||
# Omitting keys -> all openai keys allowed
|
||||
- provider: "anthropic"
|
||||
weight: 1
|
||||
allowed_models: ["*"]
|
||||
keys:
|
||||
- name: "anthropic-primary"
|
||||
- provider: "groq"
|
||||
weight: 1
|
||||
allowed_models: ["*"]
|
||||
|
||||
# 4. Cloud providers — Azure + Vertex + Bedrock only.
|
||||
# Shows that VK scoping is identical regardless of nested key_config.
|
||||
- id: "vk-cloud-providers"
|
||||
name: "Cloud providers (Azure/Vertex/Bedrock)"
|
||||
is_active: true
|
||||
budget_id: "budget-team-platform"
|
||||
rate_limit_id: "rate-limit-high"
|
||||
provider_configs:
|
||||
- provider: "azure"
|
||||
weight: 1
|
||||
keys:
|
||||
- name: "azure-apikey"
|
||||
- name: "azure-managed-identity"
|
||||
- provider: "vertex"
|
||||
weight: 1
|
||||
keys:
|
||||
- name: "vertex-sa-key"
|
||||
- name: "vertex-workload-id"
|
||||
- provider: "bedrock"
|
||||
weight: 1
|
||||
keys:
|
||||
- name: "bedrock-static"
|
||||
- name: "bedrock-irsa"
|
||||
- name: "bedrock-assumerole"
|
||||
|
||||
# 5. Self-hosted — Ollama + vLLM + SGL only.
|
||||
# Low budget because self-hosted inference is ~free.
|
||||
- id: "vk-self-hosted"
|
||||
name: "Self-hosted (Ollama/vLLM/SGL)"
|
||||
is_active: true
|
||||
budget_id: "budget-dev"
|
||||
rate_limit_id: "rate-limit-high"
|
||||
provider_configs:
|
||||
- provider: "ollama"
|
||||
weight: 1
|
||||
- provider: "vllm"
|
||||
weight: 1
|
||||
- provider: "sgl"
|
||||
weight: 1
|
||||
|
||||
# 6. Testing — tight budget, tight rate limit, single model, single key.
|
||||
- id: "vk-testing-limited"
|
||||
name: "Testing (gpt-4o-mini only)"
|
||||
is_active: true
|
||||
budget_id: "budget-testing"
|
||||
rate_limit_id: "rate-limit-testing"
|
||||
provider_configs:
|
||||
- provider: "openai"
|
||||
weight: 1
|
||||
allowed_models: ["gpt-4o-mini"]
|
||||
keys:
|
||||
- name: "openai-secondary"
|
||||
|
||||
# 7. Batch API — restricted to keys flagged use_for_batch_api: true.
|
||||
# Pairs with the openai-batch key above. Burst rate-limit for batch flushes.
|
||||
- id: "vk-batch-api"
|
||||
name: "Batch API workloads"
|
||||
is_active: true
|
||||
budget_id: "budget-production"
|
||||
rate_limit_id: "rate-limit-burst"
|
||||
provider_configs:
|
||||
- provider: "openai"
|
||||
weight: 1
|
||||
allowed_models: ["*"]
|
||||
keys:
|
||||
- name: "openai-batch"
|
||||
|
||||
# Plugins configuration
|
||||
plugins:
|
||||
telemetry:
|
||||
enabled: false
|
||||
logging:
|
||||
enabled: true
|
||||
config: {}
|
||||
governance:
|
||||
enabled: true
|
||||
config:
|
||||
is_vk_mandatory: false # Set to true to require virtual key on all requests
|
||||
|
||||
# Resource limits
|
||||
resources:
|
||||
limits:
|
||||
cpu: 1000m
|
||||
memory: 1Gi
|
||||
requests:
|
||||
cpu: 250m
|
||||
memory: 256Mi
|
||||
|
||||
# Probes
|
||||
livenessProbe:
|
||||
httpGet:
|
||||
path: /health
|
||||
port: http
|
||||
initialDelaySeconds: 30
|
||||
periodSeconds: 30
|
||||
timeoutSeconds: 5
|
||||
failureThreshold: 3
|
||||
|
||||
readinessProbe:
|
||||
httpGet:
|
||||
path: /health
|
||||
port: http
|
||||
initialDelaySeconds: 10
|
||||
periodSeconds: 10
|
||||
timeoutSeconds: 5
|
||||
failureThreshold: 3
|
||||
|
||||
autoscaling:
|
||||
enabled: false
|
||||
Reference in New Issue
Block a user