Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 13B Chat on GCP Vertex AI?
GCP Vertex AI offers Llama 2 13B Chat with pay-as-you-go pricing at $0.16/1M 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 13B Chat across 11 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")llama2-13b-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("llama2-13b-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 13B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| AWS Bedrock | $0.75 | $1.00 |
| Microsoft Foundry | $0.81 | $0.94 |
| GCP Vertex AI | $0.16 | $0.48 |
| DeepInfra | $0.13 | $0.13 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.16 |
| Output tokens | $0.48 |
Capabilities
About Llama 2 13B Chat
The Llama 2 13B Chat model is a 13 billion parameter generative text model developed by Meta, optimized for conversational applications. Released on July 18, 2023, it's part of the Llama 2 family and excels in dialogue scenarios. The model leverages supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to generate coherent and contextually relevant responses. Trained on 2 trillion tokens from diverse public sources, it outperforms many open-source chat models and matches popular closed-source models in helpfulness and safety. This model is ideal for AI engineers working on chatbots, virtual assistants, and customer service automation.