Sinapsis AI vs AWS Bedrock Compared

AWS Bedrock is Amazon's managed AI service. It offers access to foundation models from Anthropic, Meta, Mistral, and others, with enterprise integrations across the AWS ecosystem. For teams already deep in AWS, the convenience is real.

But convenience comes with a price, and it's not just the invoice.

What AWS Bedrock Does Well

Bedrock leverages the full AWS ecosystem. IAM for access control, CloudWatch for monitoring, S3 for data storage, Lambda for serverless inference. If your entire stack is AWS, Bedrock slots in naturally.

It also offers model customization (fine-tuning), Knowledge Bases (managed RAG), and Agents, all within a managed, enterprise-grade environment with SLAs and compliance certifications.

For enterprises that need "AI with an SLA from a vendor our procurement team already approved," Bedrock delivers.

The Lock-in Problem

Bedrock's strength is also its weakness: everything lives inside AWS.

Vendor lock-in. Bedrock APIs are AWS-specific. Moving to another cloud or self-hosted means rewriting integration code, changing auth flows, and migrating data pipelines.
Cost opacity. Bedrock pricing involves per-token fees, provisioned throughput pricing, Knowledge Base charges, and the usual AWS maze of data transfer costs. Understanding your actual cost per workflow step requires significant effort.
Limited observability. CloudWatch gives you AWS-level metrics like invocations, latency, and errors. It doesn't give you user behavior analytics, conversion funnels, or AI-powered optimization recommendations.
No multi-cloud. If you use Bedrock and later need Azure or GCP models, you're building a second integration from scratch.
AWS pricing applies. Data transfer between regions, S3 storage for RAG documents, and Lambda invocations for preprocessing all add up. Costs compound in ways that are hard to predict.

The Sinapsis AI Difference

| Capability | AWS Bedrock | Sinapsis AI | |-----------|-------------|-----------------| | Foundation model access | Yes (via AWS marketplace) | Yes (any provider + self-hosted) | | Managed RAG | Yes (Knowledge Bases) | Yes (hybrid cosine + BM25) | | AI Agents | Yes | Yes (with workflow orchestration) | | Visual workflow builder | Partial (Step Functions) | Yes (drag-and-drop) | | Per-step cost tracking | Complex (CloudWatch + Cost Explorer) | Yes (real-time, built-in) | | User behavior analytics | No (needs external tools) | Yes (heatmaps, funnels, replays) | | AI optimization recommendations | No | Yes | | Multi-cloud / multi-provider | No (AWS only) | Yes | | Self-hosted | No (AWS managed) | Yes (any infrastructure) | | Vendor lock-in | High | None | | Open source | No | Yes | | A/B testing | No (build it yourself) | Yes | | Team workspaces | Via IAM (complex) | Yes (RBAC, simple) |

The Cost Conversation

Bedrock's pricing looks simple until you add everything up:

Per-token model fees (vary by model and region)
Provisioned Throughput (if you want predictable pricing)
Knowledge Base charges (embedding + storage + retrieval)
Data transfer (between services, regions, internet)
Supporting services (Lambda, S3, CloudWatch, Step Functions)

Sinapsis AI gives you a single platform with transparent pricing. Self-hosted, you control the infrastructure costs directly. No hidden data transfer fees, no cross-service charges.

When to Use Each

Use AWS Bedrock when:

Your entire stack is AWS and switching isn't realistic
You need enterprise SLAs from AWS specifically
Your procurement team requires an AWS marketplace purchase
You don't need user behavior analytics
Lock-in is an acceptable trade-off for convenience

Use Sinapsis AI when:

You want to avoid vendor lock-in
You need multi-provider or multi-cloud flexibility
You want transparent, predictable cost tracking
You need user behavior analytics alongside AI metrics
You want AI-powered optimization recommendations
You prefer open source with self-hosted deployment
Compliance requirements favor data sovereignty

The Bottom Line

AWS Bedrock is AI-as-a-service inside the AWS walled garden. Sinapsis AI is AI operations anywhere you want to run it. One locks you in with convenience. The other gives you freedom with capability.

Your AI infrastructure should be as portable as your ambition.

What AWS Bedrock Does Well

It also offers model customization (fine-tuning), Knowledge Bases (managed RAG), and Agents, all within a managed, enterprise-grade environment with SLAs and compliance certifications.

For enterprises that need "AI with an SLA from a vendor our procurement team already approved," Bedrock delivers.

The Lock-in Problem

Bedrock's strength is also its weakness: everything lives inside AWS.

Vendor lock-in. Bedrock APIs are AWS-specific. Moving to another cloud or self-hosted means rewriting integration code, changing auth flows, and migrating data pipelines.

Cost opacity. Bedrock pricing involves per-token fees, provisioned throughput pricing, Knowledge Base charges, and the usual AWS maze of data transfer costs. Understanding your actual cost per workflow step requires significant effort.

Limited observability. CloudWatch gives you AWS-level metrics like invocations, latency, and errors. It doesn't give you user behavior analytics, conversion funnels, or AI-powered optimization recommendations.

No multi-cloud. If you use Bedrock and later need Azure or GCP models, you're building a second integration from scratch.

AWS pricing applies. Data transfer between regions, S3 storage for RAG documents, and Lambda invocations for preprocessing all add up. Costs compound in ways that are hard to predict.

The Sinapsis AI Difference

The Cost Conversation

Bedrock's pricing looks simple until you add everything up:

Per-token model fees (vary by model and region)

Provisioned Throughput (if you want predictable pricing)

Knowledge Base charges (embedding + storage + retrieval)

Data transfer (between services, regions, internet)

Supporting services (Lambda, S3, CloudWatch, Step Functions)

Sinapsis AI gives you a single platform with transparent pricing. Self-hosted, you control the infrastructure costs directly. No hidden data transfer fees, no cross-service charges.

When to Use Each

Use AWS Bedrock when:

Your entire stack is AWS and switching isn't realistic

You need enterprise SLAs from AWS specifically

Your procurement team requires an AWS marketplace purchase

You don't need user behavior analytics

Lock-in is an acceptable trade-off for convenience

Use Sinapsis AI when:

You want to avoid vendor lock-in

You need multi-provider or multi-cloud flexibility

You want transparent, predictable cost tracking

You need user behavior analytics alongside AI metrics

You want AI-powered optimization recommendations

You prefer open source with self-hosted deployment

Compliance requirements favor data sovereignty