Thank you for emphasizing on security and scalability part, even though Lang chain has inbuilt Api's to connect OpenAI and embedding models it asks OpenAI keys for RAG pipelines and will differ from Azure Data and privacy policies a lot.
Here is key difference between both frameworks
Both Azure OpenAI and Lang Chain with OpenAI API have their strengths, and the choice depends on your specific priorities. Here's a comparison based on your criteria:
1. Cost :
Azure OpenAI: Pricing depends on the specific Azure services you use alongside OpenAI models. Azure offers pay-as-you-go pricing, which can be cost-effective if you already use other Azure services. Additionally, Azure provides enterprise discounts for large-scale usage. Attached OpenAI pricing details for reference -
LangChain with OpenAI API: Costs are tied to OpenAI's API pricing, which is based on the number of tokens processed. While LangChain itself is open-source and free, integrating it with OpenAI's API incurs costs. If you need additional infrastructure for LangChain, that could add to the expense. Attached OpenAI pricing details for reference.
2. Security and Compliance :
Azure OpenAI: Azure is known for its robust security and compliance features. It integrates with Azure Active Directory (AAD) for secure authentication and offers enterprise-grade compliance certifications (e.g., GDPR, HIPAA). Data are kept and processed with encryption at rest through Azure OpenAI stays within Azure's secure environment, which is a significant advantage for enterprises concerned about data privacy. There is also 30-day retention policy for customer data post which they are safely purged, Customers can also opt out of this retention period. Plus, it comes with default content filter layer which help blocks harmful content, customer can create advanced content filters and even opt out in edge cases like call center transcription scenarios. Customer can choose region specific by choosing Data zone standard or provisioned specific deployments in Azure OpenAI. Attached reference on data policy, DPA addendum, Privacy policies and abuse monitoring for reference. -
LangChain with OpenAI API: While OpenAI's API is secure, it may not offer the same level of enterprise compliance as Azure. Data sent to OpenAI's API is processed on OpenAI's servers and outside at customer side. Customer will be responsible for security and data leaks on it resources end. Security Policy | 🦜️🔗 LangChain
3. Scalability :
Azure OpenAI: Azure's cloud infrastructure is highly scalable, making it well-suited for long-term enterprise applications. It also integrates seamlessly with other Azure services, enabling you to build complex, scalable solutions. You can create multizone deployment and load balance it through APIM and load balancing. You can opt for Global standard or provision or PTU (provisioned throughput unit) which offer higher TPM and dedicated quota compared to Pay as you go models. Enterpise OpenAI deployment, Provisioned throughput Units -
LangChain with OpenAI API: LangChain is highly modular and allows you to build sophisticated applications by chaining different components. However, scalability depends on the infrastructure you set up to support LangChain and OpenAI's API for e.g you can scale the deployed webapps or containerize version of it.
Hope it addresses all your queries
Thank You.