AWS LLM Deployment Paths: Bedrock, SageMaker, or Self-Managed

"How do we deploy an LLM on AWS?" has three answers, and the wrong framing treats them as features to compare rather than contracts to choose between. Bedrock sells tokens through a managed API; SageMaker sells managed endpoints you configure; self-managed EC2 or EKS sells capacity you operate. The comparison coverage converges on which contract fits which volume and control profile — this page assembles that routing so your workloads land on the right path the first time.

Three Paths, Three Management Contracts

The three AWS paths sell different management contracts: Bedrock sells tokens through a managed API with provider-operated everything, SageMaker sells managed endpoints you configure around models you bring or choose, and self-managed EC2/EKS sells raw capacity where your team operates the serving stack — and every cost, control, and operations difference follows from which contract the workload needs.

Official AWS community guidance ranks the management burden exactly this way — Bedrock simplest, SageMaker next, EC2 the most work — and the ranking is the contract made visible. The control column is its mirror: only the self-managed path hands you the weights, the engine choice, and the tuning depth, which is why compliance-driven deployments (your boundary, your engine) and performance-driven ones (kernel-level work) end up there despite the operations. AWS's own documentation provides the Bedrock-versus-SageMaker selection guidance for the managed pair; the third path is the one you choose on purpose.

Routing Workloads by Volume and Control

Route by two axes: volume and control — comparison coverage converges on Bedrock below roughly five thousand requests per day (or ~10M tokens monthly) where simplicity dominates, SageMaker endpoints through the mid-band up to ~100K requests daily, and self-managed above roughly fifty million tokens monthly where the operations investment pays — with control needs (weights, engine, tuning) overriding volume when compliance or performance demands it.

The thresholds are published calibration, not constants: the request-day bands come from practitioner comparison, the token bands from cost analysis at particular prices, and both move with your negotiated rates and configuration. Treat them as the routing rule's calibration marks — recompute the crossover for your own pricing, and let the control override settle the borderline cases where volume alone would guess wrong.

Verify Each Path Before Production

Each path's verification differs: Bedrock needs model-output and latency evaluation under your traffic (the managed surface hides internals), SageMaker needs endpoint scaling and cost-at-concurrency validation, and self-managed needs the full serving verification — throughput, latency, update safety — plus the quota check the site's quota page covers, because capacity ceilings bind self-managed paths hardest.

The verification effort scales with how much the path exposes you to operate — the managed paths verify the behavior you consume, the self-managed path verifies the stack you run. For the self-managed route, the site's benchmark-methodology page defines the manifest discipline for throughput and latency testing, and its quota page covers the ceiling that binds exactly when your self-managed deployment succeeds enough to scale.

FAQ

Bedrock or SageMaker — which is the default?

Bedrock for consumption: it is the simplest path, cheapest at low volume, and right whenever managed models meet your needs. SageMaker becomes the default when you bring your own models, need endpoint-level control, or pass the volume band where its economics overtake — the confusion dissolves once you ask who owns the model.

When does self-managed on AWS actually win?

At sustained high volume (the ~50M-tokens-monthly band per published calibration) or when control is non-negotiable — your weights, your engine, your tuning — because only then do the operations you take on repay what the managed paths would have done for you.

Can we run multiple paths at once?

Yes, and most AWS estates converge there: Bedrock for experiments and burst, self-managed or SageMaker for the steady core, with a routing layer assigning workloads by volume and data class — the paths are contracts, not commitments to a single one.