
How to Migrate from OpenAI to Cerebrium for Cost-Predictable AI Inference
sixhobbits
created: July 22, 2025, 8:08 a.m. | updated: July 22, 2025, 3:16 p.m.
How To Migrate From OpenAI to Cerebrium for Cost-Predictable AI InferenceIf you're building an AI application, you probably started with OpenAI's convenient APIs.
create ( messages = conversation , model = model , stream = True , stream_options = { "include_usage" : True }, temperature = 0.7 ) bot_response = "" for chunk in chat_completion : if chunk .
Add the session token to the .env file as a CEREBRIUM_API_KEY variable:OPENAI_API_KEY=your_openai_api_key_here CEREBRIUM_API_KEY=your_cerebrium_api_key_here CEREBRIUM_ENDPOINT_URL=your_cerebrium_endpoint_url_hereBuilding the OpenAI-Compatible vLLM EndpointStart by installing the Cerebrium CLI and creating a new project:pip install cerebrium cerebrium login cerebrium init openai-compatible-endpoint cd openai-compatible-endpointWe'll build the main.py file step by step to understand each component.
WHITE } Model: gpt-4o-mini | Total tokens: { stats [ 'total_tokens' ] } " ]) box = create_aligned_box ( lines , "OpenAI Response Stats" ) print ( f "{ box } " ) def display_cerebrium_stats ( stats ): if not stats : return lines = [ f " { Fore .
Optimizing a Cerebrium DeploymentThe performance gap between OpenAI and Cerebrium reveals significant optimization potential: You can improve the performance of Cerebrium deployments by changing configurations and upgrading hardware.
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