Amazon BedrockPaid
AWS's fully managed service for accessing foundation models from multiple providers, with agents, guardrails, and knowledge bases built in.
AI Infrastructure comparison
Amazon Bedrock vs Together AI
Pricing, pros, cons, and ideal use cases — side by side.
Together AIPaid
A cloud platform for fast, cost-efficient inference and fine-tuning of open-weight models at production scale.
At a glance
| Amazon Bedrock | Together AI | |
|---|---|---|
| Pricing | PaidUsage-based pricing (per token, or provisioned throughput). Billed through AWS. | PaidUsage-based per-token pricing. Dedicated endpoints and fine-tuning are priced separately. |
| Category | AI Infrastructure | AI Infrastructure |
| Ideal for | Enterprises already standardized on AWSTeams needing models inside their cloud security perimeterRegulated organizations with strict compliance needs | Teams running open-weight models in productionCost-sensitive, high-volume inference workloadsEnterprises fine-tuning private model variants |
Pros & cons
Amazon Bedrock
Pros
- Multiple model providers through one managed service
- Stays inside AWS security, IAM, and compliance
- Managed RAG, agents, and guardrails included
- Familiar billing and governance for AWS shops
Cons
- Ties your AI stack to AWS
- Features can lag native provider platforms
- Pricing and quota management add complexity
Together AI
Pros
- Fast, cost-efficient open-model inference
- Wide and current model selection
- Fine-tuning and dedicated endpoints available
- Often cheaper than closed APIs at scale
Cons
- Open models shift evaluation and safety onto you
- Quality varies by model and task
- Not a managed governance platform on its own
Which should you choose?
Amazon Bedrock is built around enterprises already standardized on aws; Together AI leans more toward teams running open-weight models in production. Shortlist the one whose strengths line up with your biggest constraint.