Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 7B Chat on GCP Vertex AI?
GCP Vertex AI offers Llama 2 7B Chat with pay-as-you-go pricing at $0.08/1M input tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Llama 2 7B Chat across 10 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")llama2-7b-chatRequest example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("llama2-7b-chat")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Llama 2 7B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| Baseten API | — | — |
| Fireworks AI | $0.20 | $0.20 |
| Microsoft Foundry | $0.52 | $0.67 |
| GCP Vertex AI | $0.08 | $0.24 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.08 |
| Output tokens | $0.24 |
Capabilities
About Llama 2 7B Chat
The Llama 2 7B Chat model is a fine-tuned variant of Meta's Llama 2 series, optimized for conversational AI applications. Built on an auto-regressive transformer architecture, it boasts 7 billion parameters and has been trained on a diverse dataset of 2 trillion tokens. The model underwent supervised fine-tuning and reinforcement learning with human feedback to enhance its performance in dialogue scenarios. It demonstrates competitive capabilities in terms of helpfulness and safety compared to both open-source and closed-source alternatives like ChatGPT and PaLM.