I'm unhappy with AWS SageMaker for deploying LLMs, what platform should I move to?

If you’re unhappy with SageMaker specifically for LLM deployment, the best move usually depends on what you’re optimizing for: simplicity, throughput, latency, cost, or control.

My short recommendation

For most teams deploying production LLMs today, I’d look at:

1. Hugging Face Inference Endpoints — easiest “managed” path
2. Replicate or Modal — great for fast iteration and simpler ops
3. AWS ECS/EKS + vLLM or TGI — if you want control and can manage infra
4. Together AI / Fireworks / Groq / Anyscale — if you want high-performance hosted inference
5. Databricks Model Serving — if your stack is already data/ML platform-centric

If you want a single answer:
Move to a managed inference platform like Hugging Face Inference Endpoints or Fireworks/Together AI if you want less ops. Move to ECS/EKS + vLLM if you want control.

How to choose

1) Best for low ops / fast deployment

Hugging Face Inference Endpoints

• Easy deployment
• Supports popular open-source LLMs
• Reasonable autoscaling and monitoring
• Good if you want something more straightforward than SageMaker

Good fit if: you want “click-to-deploy” style hosting and standard open models.

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2) Best for developer experience and rapid iteration

Modal

• Very good Python-first developer experience
• Easy to package model code and dependencies
• Great for prototypes that need to become production services

Replicate

• Extremely simple to use
• Good for demos, smaller production workloads, and experimentation

Good fit if: you care more about shipping quickly than custom infra tuning.

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3) Best for high-performance managed inference

Together AI Fireworks AI Groq for supported models and ultra-low latency Anyscale if you’re already in the Ray ecosystem

These are often better than SageMaker for:

• LLM-specific serving
• optimized batching
• fast token generation
• less infrastructure overhead

Good fit if: you want hosted inference that’s actually optimized for LLMs, not just generic model serving.

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4) Best for control and portability

AWS ECS/EKS + vLLM

• vLLM is one of the most common choices for serving open-source LLMs
• Better serving performance than many generic platforms
• Full control over autoscaling, networking, observability, and cost

You can also use:

• TGI (Text Generation Inference) from Hugging Face
• TensorRT-LLM if you’re NVIDIA-heavy and optimizing hard

Good fit if: you want to own the stack and avoid platform lock-in.

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5) Best if your org is already on a data platform

Databricks Model Serving

• Convenient if data pipelines, feature engineering, and governance are already there
• Easier for teams standardized on Databricks

Good fit if: model serving should live close to your data workflows.

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What I’d avoid as a “move” unless necessary

• Replacing SageMaker with another general-purpose ML platform that has the same pain points
• Choosing a platform without LLM-native serving features like:
  • continuous batching
  • token streaming
  • GPU autoscaling
  • quantization support
  • model-aware routing

My practical recommendation by scenario

If you want the least headache:

• Hugging Face Inference Endpoints
• or Together AI / Fireworks AI

If you want best cost/control at scale:

• EKS or ECS with vLLM
• possibly on AWS, GCP, or bare GPU cloud

If you want fastest path from notebook to API:

• Modal

If you need very low latency and your model is supported:

• Groq

A simple decision rule

• Need managed and easy? Hugging Face / Together / Fireworks
• Need custom and scalable? vLLM on EKS/ECS
• Need quick experimentation? Modal / Replicate
• Need data-platform integration? Databricks

If you tell me these 4 things, I can give a specific recommendation

1. Which model(s) you’re serving
2. Expected traffic and latency target
3. Whether you need open-source or closed-source models
4. Whether you want to stay on AWS or are open to leaving

If you want, I can also give you a SageMaker replacement shortlist ranked by cost, ease of use, and production readiness.