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AI Tool Comparison
Amazon Bedrock vs Cerebras Inference
A side-by-side breakdown to help you pick the right tool for your workflow.
Amazon Bedrock
Get API access to foundation models from multiple providers, plus fine-tuning and agent tools, without managing infrastructure. New Priority and Flex service levels added alongside On-Demand and Provisioned Throughput.
Models
paid
Cerebras Inference
Run Llama 70B at 1,800 tokens per second — 20x faster than GPU alternatives. The only inference provider where speed itself is the competitive moat.
Models
freemium
Bottom Line
Cerebras Inference edges ahead on rating (4.7 vs 4.4), but the right pick still comes down to which workflow you're running.
Choose Amazon Bedrock if…
Enterprise AI
Choose Cerebras Inference if…
Coding
| Attribute | Amazon Bedrock | Cerebras Inference |
|---|---|---|
| Category | Models | Models |
| Pricing | paid | freemium |
| Pricing Detail | Pay-as-you-go per token / Provisioned Throughput custom | Free tier available / Pay-per-token |
| Rating |
Key Features
Amazon Bedrock
- Multi-model access
- VPC isolation
- IAM + CloudWatch integration
- AWS Agents with RAG
- Data encryption
- Private model deployment
Cerebras Inference
- 1,800+ tokens/second on Llama 3.1 70B — fastest available
- Wafer-scale chip architecture eliminates inter-chip communication overhead
- Supports Llama 3.1, 3.3, DeepSeek R1, and Qwen models
- OpenAI-compatible API with streaming support
- Free tier for prototyping with no credit card required
- Real-time performance suitable for voice and interactive applications
Pros
Amazon Bedrock
- •Enterprise compliance and security requirements met out of the box
- •AWS integration eliminates the need for cross-cloud data movement
- •Single API across Claude, Llama, and Titan simplifies model comparison
Cerebras Inference
- •Fastest inference in the industry by a wide margin
- •Free tier is genuinely useful, not just a trial
- •OpenAI-compatible — drops into existing code immediately
Cons
Amazon Bedrock
- Per-token pricing higher than direct API access for high-volume workloads
- Requires AWS expertise to configure correctly
Cerebras Inference
- Model selection is limited to a curated set, not the full open-source catalog
- Purpose-built hardware means no custom model fine-tuning support
- Very high throughput can mask context window limitations
