Note
Got a question? Email us at scienceit@lbl.gov or join the CBorg Users Chatroom
There are two primary ways to get programmatic API access to LLM models at Berkeley Lab: through the CBorg API Proxy or through a self-managed Cloud Project (GCP or AWS). This page compares the two approaches to help you decide which is the best fit for your use case. Note: this page is specifically about API access for developers and automated workflows — if you are looking for interactive chat access, see CBorg Chat or other Tools.
CBorg API Proxy
The CBorg API Proxy is a managed service operated by the IT Division. It provides a single OpenAI-compatible API endpoint that routes requests to a variety of commercial and on-premises models.
Key Features:
- More models available — includes OpenAI, xAI, Google Gemini, and Anthropic Claude models through a single endpoint
- Managed model list — we keep the list of available models up to date as new versions are released
- Budget enforcement — we manage budget controls so you don’t accidentally exceed your spending limit
- $50/month in free usage per user — every user gets a free monthly allocation
- On-prem zero-cost models — the following models are hosted on-premises and have no per-token cost:
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Considerations:
- May go offline briefly for maintenance
- Acceptable for research purposes but not intended to support “critical infrastructure”
Cloud Project (GCP / AWS)
A Cloud Project gives you a dedicated GCP or AWS account attached to your PID, where you manage your own model access and configuration.
Key Features:
- Lower inference cost — cost of LLM inference may be around 10% lower than the “retail” cost charged by CBorg
- High availability — >99.99% uptime; cloud services will almost never go offline
- Data isolation — your prompts do not flow through the CBorg system
- Dedicated inference bandwidth — if you need to run many prompts in parallel at a high sustained rate
- $25/month in free usage per project
Considerations:
- Self-managed — you are responsible for:
- Setting up and monitoring budget alerts and cost controls
- Setting up API keys / service accounts to access models
- Deploying new models when they come out
- Generally being responsible for configuration and security
- No access to OpenAI models — Gemini, Claude, AWS Nova, Cohere and others are available, but OpenAI models are not
Summary Comparison
| Feature | CBorg API Proxy | Cloud Project |
|---|---|---|
| Available Models | OpenAI, xAI, Gemini, Claude, on-prem | Gemini, Claude, AWS Nova, Cohere, others (no OpenAI) |
| Free Usage | $50/month per user | $25/month per project |
| Model Management | Managed by IT Division | Self-managed |
| Budget Controls | Managed by IT Division | Self-managed |
| On-Prem Models | ✅ Zero cost | ❌ Not available |
| Uptime | May have brief maintenance windows | >99.99% |
| Data Isolation | Prompts routed through CBorg | Full isolation |
| Inference Cost | Standard retail pricing | ~10% lower |
| Parallel Throughput | Shared bandwidth | Dedicated bandwidth |
| Use Case | Research, prototyping, general use | Critical infrastructure, high-throughput, sensitive data |
Which should I choose?
- Choose CBorg API if you want a simple, managed experience with access to the widest range of models including OpenAI, free on-prem models, and automatic budget controls.
- Choose a Cloud Project if you need high availability for critical infrastructure, dedicated inference bandwidth, data isolation, lower per-token costs at scale, or processing of sensitive data (ECI, CUI) with approval from IT Policy.
To get started with a Cloud Project, email scienceit@lbl.gov.