Obiguard’s Bring Your Own LLM feature allows you to seamlessly integrate privately hosted language models into your AI infrastructure. This powerful capability enables unified management of both private and commercial LLMs through a consistent interface while leveraging Obiguard’s comprehensive suite of observability and reliability features.

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Key Benefits

  • Unified API Access: Manage private and commercial LLMs through a single, consistent interface
  • Comprehensive Monitoring: Track performance, usage, and costs alongside your commercial LLM usage
  • Simplified Access Control: Manage team-specific permissions and usage limits
  • Secure Credential Management: Protect sensitive authentication details through Obiguard’s secure vault

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Integration Options

PrerequisitesYour private LLM must implement an API specification compatible with one of Obiguard’s supported providers (e.g., OpenAI’s /chat/completions, Anthropic’s /messages, etc.).
Obiguard offers two primary methods to integrate your private LLMs:
  1. Using Virtual Keys: Store your deployment details securely in Obiguard’s vault
  2. Direct Integration: Pass deployment details in your requests without storing them

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Option 1: Using Virtual Keys

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Step 1: Add Your Deployment Details

Navigate to the Virtual Keys section in your Obiguard dashboard and create a new Virtual Key.
  1. Click “Add Key” and enable the “Local/Privately hosted provider” toggle
  2. Configure your deployment:
    • Select the matching provider API specification (typically OpenAI)
    • Enter your model’s base URL in the Custom Host field
    • Add required authentication headers and their values
  3. Click “Create” to generate your virtual key

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Step 2: Use Your Virtual Key in Requests

After creating your virtual key, you can use it in your applications:
Python
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***",  # Your Obiguard API key
  virtual_key="YOUR_PRIVATE_LLM_VIRTUAL_KEY"
)

response = client.chat.completions.create(
  model="YOUR_MODEL_NAME",  # The model name your private deployment expects
  messages=[
    {"role": "user", "content": "Explain quantum computing in simple terms"}
  ]
)

print(response.choices[0].message.content)

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Option 2: Direct Integration Without Virtual Keys

If you prefer not to store your private LLM details in Obiguard’s vault, you can pass them directly in your API requests:
Python
from obiguard import Obiguard

client = Obiguard(
  obiguard_api_key="sk-obg***",  # Your Obiguard API key
  provider="openai",  # The API spec your LLM implements
  custom_host="https://your-llm-server.com/v1/",  # Include the API version
  Authorization="Bearer YOUR_AUTH_TOKEN",  # Optional: Any auth headers needed
  forward_headers=["Authorization"]  # Headers to forward without processing
)

response = client.chat.completions.create(
  model="YOUR_MODEL_NAME",
  messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}]
)

print(response.choices[0].message.content)
The custom_host must include the API version path (e.g., /v1/). Obiguard will automatically append the endpoint path (/chat/completions, /completions, or /embeddings).

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Advanced Features

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Monitoring and Analytics

Obiguard provides comprehensive observability for your private LLM deployments, just like it does for commercial providers:
  • Log Analysis: View detailed request and response logs
  • Performance Metrics: Track latency, token usage, and error rates
  • User Attribution: Associate requests with specific users via metadata

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Troubleshooting

IssuePossible CausesSolutions
Connection ErrorsIncorrect URL, network issues, firewall rulesVerify URL format, check network connectivity, confirm firewall allows traffic
Authentication FailuresInvalid credentials, incorrect header formatCheck credentials, ensure headers are correctly formatted and forwarded
Timeout ErrorsLLM server overloaded, request too complexAdjust timeout settings, implement load balancing, simplify requests
Inconsistent ResponsesDifferent model versions, configuration differencesStandardize model versions, document expected behavior differences

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FAQs

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Next Steps

Explore these related resources to get the most out of your private LLM integration:

Universal API

Request Tracing