AWS Bedrock vs Google Vertex AI: which foundation model platform wins?
Use AWS Bedrock if you want managed inference with no infrastructure overhead and prefer Anthropic/Meta models. Use Google Vertex AI if you need Gemini integration, custom fine-tuning, or existing GCP infrastructure.
VERDICT
Side-by-side comparison
| Dimension | AWS Bedrock | Google Vertex AI | Winner |
|---|---|---|---|
| Model selection | Anthropic Claude, Meta Llama 3, Cohere, Mistral, Titan | Google Gemini, Claude, Llama via Model Garden, custom models | Tie |
| Inference pricing (1M tokens input) | $0.30–$3.00 (Claude 3 Sonnet to Opus) | $0.075–$2.00 (Gemini Flash to Pro, standard pricing) | Google Vertex AI |
| Time-to-first-token (Claude 3) | 200–300ms (managed endpoints) | 250–350ms (managed API) | AWS Bedrock |
| Custom model fine-tuning | Limited (Titan Text only, on-demand) | Full support (any model, built-in tools) | Google Vertex AI |
| Setup complexity | Create endpoint, call API (5 min) | Create endpoint or API key (5–10 min, more complex with Workbench) | AWS Bedrock |
| Integration with data stack | AWS services (S3, DynamoDB, SageMaker) | GCP ecosystem (BigQuery, Dataflow, Vertex ML) | Tie |
| Async/batch processing | Batch inference via CLI/API, serverless | Batch prediction with Vertex Batch Prediction | Tie |
| Model switching (no redeployment) | Yes: same API endpoint, swap model_id parameter | Yes: switch model in Vertex API, no endpoint re-creation | Tie |
| Regional availability | US-East, US-West, EU (limited) | 30+ regions globally (broader coverage) | Google Vertex AI |
| Open-source model support | Llama 3 (70B, 8B), Mistral, smaller models limited | Llama 3, Gemma, PaLM via Model Garden | Google Vertex AI |
Performance benchmarks
Throughput (Claude 3 Sonnet, 1K input + 1K output, batch of 10 requests)
Bedrock's regional distribution shows slightly better throughput for US-East; Vertex AI's global regions have variable latency. Both support batching; Bedrock Batch API achieves 40–50 req/sec for non-real-time workflows.
Cost per 1M input tokens (Claude 3 Sonnet)
Vertex AI is 4x cheaper for Gemini models; cost parity for Claude (same model, same vendor). Bedrock has no commitment discounts; Vertex AI offers reserved capacity at 30–50% discount.
Time-to-first-token (Claude 3 Opus, 1K context)
Bedrock averages 50–100ms faster for US-East; Vertex AI wins globally with more regional endpoints. Both under 400ms for production chat; difference negligible for most applications.
Fine-tuning cost per model (10K training tokens)
Vertex AI's fine-tuning is 3–5x cheaper and supports more models; Bedrock's fine-tuning is limited to Titan. For production model customization, Vertex AI has clear cost advantage.
When to use each
- ✓ You need Anthropic Claude (Sonnet, Opus, Haiku) as primary model with no GCP infrastructure: Bedrock is Claude's native inference platform with lowest latency and exclusive Batch API at scale.
- ✓ Evaluating 5+ foundation models (Claude, Llama, Mistral, Cohere) without committing to one: Bedrock's single API endpoint lets you swap models by parameter, no redeployment.
- ✓ Building chatbots or RAG systems for AWS shops: seamless integration with S3 for retrieval, Lambda for orchestration, and DynamoDB for memory, all within same platform.
- ✓ You need guaranteed latency <300ms for US-East users on Claude: Bedrock's regional managed endpoints are co-located with CloudFront, delivering predictable performance.
- ✓ Serverless-first architecture where you don't want to manage infrastructure: Bedrock scales to zero, pay-per-token, no endpoint provisioning or idle costs.
- ✓ Gemini integration is a hard requirement: Vertex AI is Google's managed platform for Gemini 2.5 Pro, Flash, and Pro Vision with full feature parity and lowest latency.
- ✓ Fine-tuning custom models at scale: Vertex AI's MLOps pipelines support supervised fine-tuning, PEFT, and LoRA for any open-source model (Llama, Mistral, Qwen) at 3–5x lower cost than alternatives.
- ✓ Existing GCP data stack (BigQuery, Dataflow, Pub/Sub): Vertex AI's native integration with BigQuery lets you build ML workflows without data movement; LLM predictions run directly on warehouse data.
- ✓ Global applications requiring 30+ regional endpoints: Vertex AI's worldwide region coverage is 2x broader than Bedrock; latency is more consistent across Asia-Pacific, Europe, and Americas.
- ✓ Unified ML platform including vision, embeddings, and structured predictions: Vertex AI consolidates LLM endpoints, batch prediction, model monitoring, and custom training in one console with unified IAM.
Common misconceptions
AWS Bedrock
AWS Bedrock is just a wrapper around Anthropic Claude: you're locked into one model.
Bedrock supports 7+ model families (Claude, Llama, Mistral, Cohere, Titan). You switch models in the API parameter without redeploying. Not a single-vendor lock-in.
Bedrock is cheaper than Vertex AI for all models.
Bedrock pricing is competitive on Claude (identical pricing to direct API). Vertex AI's Gemini Flash is 4x cheaper than Claude Sonnet. Cost winner depends on which model you need.
Bedrock Batch API is production-ready for real-time use cases.
Batch API is async-only (5–60 min latency). For real-time inference, use on-demand endpoints. Batch is for offline workloads like data labeling or report generation.
Google Vertex AI
Vertex AI fine-tuning is simple: just run a command and it works.
Fine-tuning requires dataset preparation, JSONL formatting, tuning hyperparameters, and understanding Vertex Workbench or gcloud CLI. Not a one-click feature for beginners.
Vertex AI is cheaper across the board because it's Google.
Vertex AI's Gemini Flash is cheap; Claude pricing on Vertex is identical to Bedrock (no discount for GCP). Vertex only wins on Gemini and open-source models.
Vertex AI's 30+ regions means lower latency everywhere.
More regions doesn't guarantee lower latency if your data isn't routed correctly. Some regions have higher cold-start times. Bedrock's smaller region count is deliberately optimized for lowest latency in each region.
Code examples
Task: Send a chat prompt to Claude 3 Sonnet and receive a streamed response.
import boto3
import os
import json
from botocore.exceptions import ClientError
client = boto3.client('bedrock-runtime', region_name='us-east-1')
# Model ID identifies Claude 3 Sonnet on Bedrock
model_id = 'anthropic.claude-3-sonnet-20240229-v1:0'
messages = [
{'role': 'user', 'content': 'Explain quantum computing in one sentence.'}
]
# Bedrock uses same Anthropic Messages API format
response = client.converse_stream(
modelId=model_id,
messages=messages,
system='You are a helpful assistant.'
)
for event in response['stream']:
if 'contentBlockDelta' in event:
print(event['contentBlockDelta']['delta']['text'], end='', flush=True)
print()Bedrock wraps Anthropic's native Messages API via boto3; you use the same message format as direct Anthropic SDK, but authentication is AWS IAM and routing is region-based managed endpoints.
import vertexai
from vertexai.generative_models import GenerativeModel, Part
import os
# Initialize Vertex AI with GCP project
vertexai.init(project=os.environ['GCP_PROJECT'], location='us-central1')
# Gemini model on Vertex AI
model = GenerativeModel('gemini-2.5-pro')
# Vertex AI uses native Gemini SDK
response = model.generate_content(
contents='Explain quantum computing in one sentence.',
stream=True,
generation_config={'max_output_tokens': 256}
)
for chunk in response:
print(chunk.text, end='', flush=True)
print()Vertex AI uses Google's native GenerativeModel SDK with Gemini; authentication is GCP credentials (ADC), and you stream via the Gemini Python library, not a generic API wrapper.
Migration path
- Switching from AWS Bedrock to Google Vertex AI:
- Install: `pip install google-cloud-aiplatform` (Bedrock uses `boto3`).
- Auth: Replace AWS credential chain with `vertexai.init(project=..., location=...)` using GCP ADC or service account.
- Model IDs: Change `anthropic.claude-3-sonnet-20240229-v1:0` (Bedrock format) to `gemini-2.5-pro` (Vertex AI format, or use Claude via Model Garden).
- API calls: Replace `client.converse_stream()` with `model.generate_content(stream=True)`: signature differs but both support streaming.
- System prompts: Bedrock uses `system=` parameter; Vertex AI uses `system_instruction=` in GenerativeModel constructor or inline in messages.
- Cost monitoring: Bedrock costs are in CloudWatch; Vertex AI costs are in Cloud Billing dashboard: different dashboards, different metric names. Switching is a 1–2 day refactor for moderate-sized applications; larger migrations should run parallel endpoints for A/B testing latency and cost.
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