diff --git a/tests/e2e/gateway/litellm-config.yml b/tests/e2e/gateway/litellm-config.yml
new file mode 100644
index 00000000000..03ddc25f65c
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+++ b/tests/e2e/gateway/litellm-config.yml
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+#jThis default config file aims to support most popular model providers out of the box
+
+#In general, the model name used by the client will be the same as the ones from the provider (For example, you will use "anthropic.claude-3-5-sonnet-20240620-v1:0" when you're calling LiteLLM just like you would when calling Amazon Bedrock directly)
+#In the case where there are model name conflicts, a prefix will be used (For example, the Azure and the openAI model names conflict, so when you are using Azure, you will use "azure/gpt-4o-realtime-preview-2024-10-01")
+
+#Some model providers require additional user-specific configuration (such as Azure which requires you to specify your own api_base with your resource name, and your api_version). 
+#In this case, the provider is commented out, and you should uncomment it and provide your specific info
+
+#For more detailed information about each provider, refer to the docs: https://docs.litellm.ai/docs/providers
+
+#If you are not interested in a particular provider, just remove it from your config.yaml, and redeploy, and it will no longer show up in your LiteLLM deployment
+
+#If a particular provider is not working, double check your .env file, and make sure you have provided a valid api key for that provider, and then redeploy
+
+#Full details on guardrails here: https://docs.litellm.ai/docs/proxy/guardrails/bedrock
+general_settings:
+  store_prompts_in_spend_logs: true
+  master_key: "sk-1234"
+  proxy_batch_write_at: 60
+  database_connection_pool_limit: 10
+  # disable_error_logs: True
+  forward_client_headers_to_llm_api: false
+  maximum_spend_logs_retention_period: "60d"  # GSE-13389: Cleanup logs older than 60 days
+  maximum_spend_logs_cleanup_cron: "0 1 * * *"  # 01:00 UTC daily = 18:00 PDT
+  database_url: os.environ/DATABASE_URL
+  control_plane_url: os.environ/CONTROL_PLANE_URL
+  alerts: ["email"]
+
+# fallbacks: [{"gpt-4": ["anthropic.claude-3-5-sonnet-20240620-v1:0"]}] #Configure fallbacks for context window exeeded errors (In this example, we will fall back to Claude Sonnet if over 8000 tokens, which is gpt-4's limit)
+  # default_fallbacks: ["anthropic.claude-3-haiku-20240307-v1:0"] #Configure fallbacks for any error for every model (the above fallback configurations override this one)
+# environment_variables:
+#   STORE_MODEL_IN_DB: 'True'
+#   LITELLM_LOG: "DEBUG"
+litellm_settings:
+  drop_params: True
+  request_timeout: 600
+  num_retries: 3
+  json_logs: true
+  store_audit_logs: True
+  cache: true
+  cache_params:
+    type: redis
+    host: redis
+    port: 6379
+    password: os.environ/REDIS_PASSWORD
+    namespace: litellm.caching
+    ttl: 16600
+  # max_budget: 1000000000.0 # (float) sets max budget in dollars across the entire proxy across all API keys. Note, the budget does not apply to the master key. That is the only exception.
+    # namespace: "litellm.caching.caching"
+    # ttl: 15
+  # budget_duration: 1mo # (str) frequency of budget reset - You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"), months ("1mo").
+  # max_internal_user_budget: 1000000000.0 # (float) sets default budget in dollars for each internal user. (Doesn't apply to Admins. Doesn't apply to Teams. Doesn't apply to master key)
+  # internal_user_budget_duration: "1mo" # (str) frequency of budget reset - You can set duration as seconds ("30s"), minutes ("30m"), hours ("30h"), days ("30d"), months ("1mo").
+  # success_callback: ["s3_v2"]
+  # failure_callback: ["s3_v2"]
+  # service_callback: ["datadog"]
+  callbacks: ["arize_phoenix", "datadog", "smtp_email", "prometheus", "otel"]
+  require_auth_for_metrics_endpoint: false
+  cache: true
+  cache_params:
+    type: redis
+    host: redis
+    port: 6379
+    password: os.environ/REDIS_PASSWORD
+    namespace: litellm.caching
+    ttl: 16600
+  #type: redis-semantic
+  #similarity_threshold: 0.8   # similarity threshold for semantic cache
+  #redis_semantic_cache_embedding_model: text-embedding-ada-002 # only works with text-embedding-ada-002 for now... https://github.com/BerriAI/litellm/issues/4001
+
+#ttl: Optional[float]
+#default_in_memory_ttl: Optional[float]
+#default_in_redis_ttl: Optional[float]
+
+model_list:
+  - model_name: gpt-5.5
+    litellm_params:
+      model: openai/gpt-5.5
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: claude-haiku-4-5
+    litellm_params:
+      model: anthropic/claude-haiku-4-5
+      api_key: os.environ/ANTHROPIC_API_KEY
+
+  # Same underlying model via Vertex AI — distinct routing/auth path
+  # # (service-account JSON), so it gets its own model_name.
+  - model_name: gemini-2.5-flash-vertex
+    litellm_params:
+      model: vertex_ai/gemini-2.5-flash
+      vertex_project: os.environ/VERTEXAI_PROJECT
+      vertex_location: us-central1
+      vertex_credentials: os.environ/VERTEXAI_CREDENTIALS
+
+  - model_name: gemini-2.5-flash
+    litellm_params:
+      model: gemini/gemini-2.5-flash
+      api_key: os.environ/GEMINI_API_KEY
+
+  # load balancing to a different deployment, if gemini gets rate limited.
+  - model_name: gemini-2.5-flash
+    litellm_params:
+      model: gemini/gemini-2.5-flash
+      api_key: os.environ/GEMINI_API_KEY
+
+  # embedding models
+  - model_name: openai-text-embedding-3-small
+    litellm_params:
+      model: openai/text-embedding-3-small
+      api_key: os.environ/OPENAI_API_KEY
+
+  - model_name: gemini-2-embedding
+    litellm_params:
+      model: gemini/gemini-2-embedding
+      api_key: os.environ/GEMINI_API_KEY
+
+  # realtime models
+  - model_name: openai-realtime
+    litellm_params:
+      model: openai/realtime-2
+      api_key: os.environ/OPENAI_API_KEY
+    model_info:
+      mode: realtime
+
+
+mcp_servers:
+  deepwiki_mcp:
+    url: "https://mcp.deepwiki.com/mcp"
+    auth_type: none
+    description: "just a test"
+
+  atlassian:
+    url: "https://mcp.atlassian.com/v1/mcp"
+    auth_type: oauth2
+    authorization_url: https://auth.atlassian.com/authorize
+
+
+guardrails:
+  - guardrail_name: "presidio-pii"
+    litellm_params:
+      guardrail: presidio
+      mode: pre_call
+      presidio_analyzer_api_base: os.environ/PRESIDIO_ANALYZER_API_BASE
+      presidio_anonymizer_api_base: os.environ/PRESIDIO_ANONYMIZER_API_BASE
+      default_on: false
+      pii_entities_config:
+        EMAIL_ADDRESS: BLOCK
+        CREDIT_CARD: BLOCK
+        US_SSN: BLOCK
+        PHONE_NUMBER: BLOCK
+
+
diff --git a/tests/e2e/pytest.ini b/tests/e2e/pytest.ini
new file mode 100644
index 00000000000..7799f6b16a2
--- /dev/null
+++ b/tests/e2e/pytest.ini
@@ -0,0 +1,7 @@
+[pytest]
+# Config when any e2e suite under tests/e2e/ is run directly, e.g.
+#   uv run pytest tests/e2e/spend_tracking/ -v
+# The e2e marker is also registered in conftest.py for runs rooted elsewhere.
+addopts = --strict-markers --strict-config
+markers =
+    e2e: live test that requires a running proxy and real provider keys