AWS Bedrock API
AWS Bedrock API
Fully managed service offering foundation models from AI21, Anthropic, Cohere, Meta, and Stability.
Quick Facts
| Field | Value |
|---|---|
| Provider | Amazon AWS |
| Category | AI & Machine Learning APIs |
| Website | https://aws.amazon.com/bedrock |
| Authentication | AWS Sig V4 |
| Pricing Model | usage-based |
| Free Tier | Limited free credits for new users |
| Rate Limit | 60 req/min (varies by model) |
Overview
AWS Bedrock API is a ai & machine learning apis provided by Amazon AWS. Fully managed service offering foundation models from AI21, Anthropic, Cohere, Meta, and Stability. This API is designed to help developers integrate ai-ml capabilities into their applications with minimal setup and maximum reliability.
The API supports AWS Sig V4 authentication, ensuring secure access to all endpoints. With a usage-based pricing model, Amazon AWS offers flexible options for projects of any size, from prototypes to enterprise deployments.
Amazon AWS maintains comprehensive documentation, SDKs for popular programming languages, and active community support. The API is built on REST principles, returning JSON responses with standard HTTP status codes, making it straightforward to integrate into existing workflows.
Authentication
This API uses AWS Sig V4 authentication. Sign each request with AWS Signature Version 4 using your access key ID and secret access key.
Always store your credentials securely using environment variables or a secrets manager. Never commit API keys to version control or expose them in client-side code.
Code Samples
Python
import requests
import os
API_KEY = os.environ.get("API_KEY", "YOUR_API_KEY")
BASE_URL = "https://aws.amazon.com/api"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
response = requests.get(f"{BASE_URL}/v1/resources", headers=headers)
print(response.status_code)
print(response.json())
JavaScript
const API_KEY = process.env.API_KEY || 'YOUR_API_KEY';
const BASE_URL = 'https://aws.amazon.com/api';
const response = await fetch(`${BASE_URL}/v1/resources`, {
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
}
});
const data = await response.json();
console.log(data);
cURL
curl -H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
https://aws.amazon.com/api/v1/resources
Go
package main
import (
"net/http"
"fmt"
"io"
"os"
)
func main() {
apiKey := os.Getenv("API_KEY")
if apiKey == "" {
apiKey = "YOUR_API_KEY"
}
req, _ := http.NewRequest("GET", "https://aws.amazon.com/api/v1/resources", nil)
req.Header.Set("Authorization", "Bearer " + apiKey)
req.Header.Set("Content-Type", "application/json")
resp, _ := http.DefaultClient.Do(req)
defer resp.Body.Close()
body, _ := io.ReadAll(resp.Body)
fmt.Println(string(body))
}
Pricing
The pricing model is usage-based. Pay only for what you use, with pricing based on API calls, tokens, or compute time. Visit the provider’s pricing page at https://aws.amazon.com/bedrock for current rates and detailed pricing information.
Use Cases
- Integration: Connect Amazon AWS services to your application for seamless ai-ml functionality.
- Automation: Automate ai-ml workflows and reduce manual operations.
- Scaling: Handle growing ai-ml demands with Amazon AWS’s robust infrastructure.