The first architecture decision in every generative AI project on AWS is the same: call a managed model through Amazon Bedrock, or own the model and the endpoint on Amazon SageMaker AI? Teams ask us this more than any other question, and the honest answer is that it depends on four things: volume, control, data constraints, and who you have on staff. This guide lays them out and ends with the decision matrix we use.
What each one actually is
Amazon Bedrock is a managed API in front of foundation models from Amazon (Nova), Anthropic, Meta, Mistral, Cohere, and others. You pay per token (or per hour for provisioned throughput), and AWS operates the model. Around the API sit managed building blocks: Knowledge Bases for RAG, Agents, Guardrails, model evaluation, and a fine-tuning / custom-model path for a subset of models. See the Bedrock user guide.
Amazon SageMaker AI is infrastructure for models you own. You pick a model (from JumpStart, Hugging Face, or your own training), a serving container, and an instance type, and you pay per instance-hour. You control weights, the serving stack, batching, quantization, and everything in between. Fine-tuning is unrestricted: any open-weight model, any method, any dataset.
The two are not exclusive, and most mature architectures we see use both.
1. Volume and the cost curve
Per-token pricing is linear; instance pricing is a step function. At low and medium volume, Bedrock is cheaper because you pay for nothing while idle. At some point the per-token bill crosses the cost of running a GPU endpoint around the clock, and owning the endpoint wins. Where that crossover sits depends on the model and the instance, so run the numbers:
- Tokens per day x Bedrock price per token for your chosen model.
- Versus: instances needed to serve peak x hourly rate x 24, using a load test for tokens-per-second per instance. Inferentia2 and quantized models move this line substantially.
Two patterns distort the picture. Bursty traffic favours Bedrock (or asynchronous SageMaker endpoints that scale to zero). Steady, high-volume traffic with a small model favours SageMaker AI; a 7B or 8B open-weight model on a single GPU serving a narrow task is routinely an order of magnitude cheaper per token than a frontier model API, if the quality holds.
2. Control and fine-tuning
If the product depends on model behaviour you need to shape deeply (domain language, strict output formats, a proprietary task), fine-tuning is on the table, and the question is how much of it you need.
- Bedrock offers fine-tuning and continued pre-training for a subset of its models, with managed infrastructure and simple pricing. Good when a supported model fits and you want the result served as an API.
- SageMaker AI offers everything: LoRA / QLoRA, full fine-tuning, RLHF, DPO, any open-weight model, multi-node on HyperPod. Necessary when you need a model Bedrock does not host, a method Bedrock does not offer, or weights you can export and run elsewhere.
Control also means the serving stack: custom tokenizers, speculative decoding, structured-output grammars, exotic batching. On SageMaker AI you can do all of it; on Bedrock you use what is exposed.
3. Data residency, compliance, and ownership
Both services keep your prompts and data inside your AWS account boundary and do not use them to train shared models. The differences are at the edges:
- Region availability. A specific Bedrock model may not be available in your required region; a SageMaker AI endpoint runs wherever the instance type exists.
- Weights ownership. If a regulator, customer contract, or exit plan requires that you can hand over or re-host the model, you need open weights on SageMaker AI.
- Network isolation. SageMaker AI endpoints can run VPC-only with no internet path. Bedrock is reachable over VPC endpoints, which satisfies most controls but is a different architecture.
- Explainability and evaluation. Owning the model makes deep evaluation (logits, attention, ablations) possible; Bedrock's evaluation tooling is good but works at the API surface.
4. Team and operational load
A Bedrock integration is an afternoon for an application developer. A production SageMaker AI LLM endpoint needs someone who understands GPUs, container memory, autoscaling, and what to do when p99 latency doubles on a Tuesday. If you do not have that person, Bedrock's operational simplicity is worth a real premium, at least until the bill says otherwise.
Hybrid patterns that work
- Bedrock for the frontier, SageMaker AI for the workhorse. Route complex, low-volume requests to a top-tier Bedrock model and high-volume, narrow requests (classification, extraction, routing) to a small fine-tuned model on SageMaker AI.
- Bedrock Knowledge Bases for retrieval, SageMaker AI for generation, or the reverse: a custom retrieval stack on OpenSearch feeding a Bedrock model. Mix by component.
- Prototype on Bedrock, graduate to SageMaker AI. Validate the product with the simplest integration; move the expensive path once traffic and requirements are known.
- SageMaker AI for training, Bedrock Custom Model Import for serving. Fine-tune an open-weight model on SageMaker AI, then import the weights into Bedrock to get per-token pricing without running endpoints, where the model family is supported.
Decision matrix
| Your situation | Lean toward |
|---|---|
| Early product, unknown traffic, small team | Bedrock |
| Steady traffic, narrow task, cost pressure | SageMaker AI with a small fine-tuned model |
| Need a model Bedrock does not host | SageMaker AI |
| Need deep fine-tuning (RLHF, full FT, custom method) | SageMaker AI (HyperPod for large runs) |
| Contract or regulator requires owned, exportable weights | SageMaker AI |
| Need managed RAG, agents, and guardrails fast | Bedrock |
| Mixed workload: some hard, mostly easy requests | Hybrid with routing |
| No ML engineering on staff | Bedrock, budget to revisit at scale |
Our recommendation process
We run a short GenAI readiness assessment for exactly this decision: use cases, expected traffic, data constraints, and team skills go in; a recommendation with a cost model at three traffic levels comes out. Sometimes the answer is "use Bedrock and stop thinking about it", and we say so. Details on our generative AI consulting page, or contact us to get started.