# Amazon SageMaker Inference vs LMDeploy

**Leader by vioscaleAI score:** Amazon SageMaker Inference

| Attribute | Amazon SageMaker Inference | LMDeploy |
|---|---|---|
| **vioscaleAI score** | 67.6 (75% (high)) | 55.9 (64% (medium)) |
| activity.commits_last_30d | - | 66 |
| adoption.dependent_repos | - | 2 |
| adoption.github_stars | - | 8,104 |
| content.faq | `[{"answer":"AWS SageMaker Inference is a managed service for deploying trained machine learning models into production, supporting multiple ML frameworks and providing integration with AWS MLOps tools like model registries, feature stores, and CI/CD pipelines. It is indexed under Model Serving.","source":"https://aws.amazon.com/ec2/pricing/?nc2=h_pr_ec2","question":"What is Amazon SageMaker Inference?","confidence":0.6},{"answer":"Amazon SageMaker Inference offers a free tier, so you can start without paying. Pricing changes often, so verify at source before relying on it.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"Is Amazon SageMaker Inference free?","confidence":0.6},{"answer":"Amazon SageMaker Inference supports the web and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://aws.amazon.com/ec2/pricing/?nc2=h_pr_ec2","question":"What platforms does Amazon SageMaker Inference support?","confidence":0.6},{"answer":"We have only confirmed a cloud / SaaS deployment for Amazon SageMaker Inference, so it appears to be vendor-hosted. If a self-hosted option exists we have not found it documented publicly.","source":"https://aws.amazon.com/ec2/pricing/?nc2=h_pr_ec2","question":"Can Amazon SageMaker Inference be self-hosted?","confidence":0.6},{"answer":"We have confirmed 15 integrations for Amazon SageMaker Inference, including TensorFlow, PyTorch, ONNX, XGBoost, SageMaker Pipelines, SageMaker Projects, SageMaker Feature Store and SageMaker Model Registry, plus 7 more. This is what we could verify from public sources, so the vendor may support others we have not indexed.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"What does Amazon SageMaker Inference integrate with?","confidence":0.6},{"answer":"We have independently confirmed HIPAA and GDPR for Amazon SageMaker Inference. Certifications we do not list are ones we have not been able to verify from public sources, which is not the same as Amazon SageMaker Inference not holding them. Always confirm compliance directly before you rely on it.","source":"https://aws.amazon.com/compliance/?nc2=h_dsc_aa_cmp","question":"What security certifications does Amazon SageMaker Inference have?","confidence":0.7},{"answer":"Amazon SageMaker Inference is available worldwide. Its primary market is the United States.","source":"https://aws.amazon.com/bedrock/pricing/?nc2=h_pr_br","question":"Where is Amazon SageMaker Inference available?","confidence":0.5}]` | `[{"answer":"A software framework that enables developers to compress, deploy, and serve large language models with quantization optimization, multiple inference engines, and compatibility across various model architectures. It is indexed under Model Serving.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"What is LMDeploy?","confidence":0.6},{"answer":"LMDeploy is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"Is LMDeploy free to use?","confidence":0.6},{"answer":"LMDeploy supports Windows and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"What platforms does LMDeploy support?","confidence":0.6},{"answer":"Yes. LMDeploy can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"Can LMDeploy be self-hosted?","confidence":0.6},{"answer":"Yes. LMDeploy is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.","source":"https://github.com/internlm/lmdeploy","question":"Is LMDeploy open source?","confidence":0.95}]` |
| deployment.options | `{"cloud":true}` | `{"self_hosted":true}` |
| description.long | A managed inference service that deploys machine learning models with support for various frameworks and deployment patterns, ranging from low-latency millisecond responses to high-throughput scenarios, with integration into MLOps workflows. | LMDeploy is a comprehensive toolkit enabling efficient compression, deployment, and serving of large language models across multiple machines and GPUs with advanced optimization techniques. |
| features.capabilities | `{"gpu_acceleration":true,"multi_model_serving":true,"multi_framework_support":true,"autoscaling_scale_to_zero":true}` | `{"gpu_acceleration":true,"continuous_batching":true,"multi_model_serving":true,"multi_gpu_multi_node":true,"quantization_support":true}` |
| integrations.count | 13 | 2 |
| integrations.list | `[{"name":"TensorFlow"},{"name":"PyTorch"},{"name":"ONNX"},{"name":"XGBoost"},{"name":"SageMaker Pipelines"},{"name":"SageMaker Projects"},{"name":"SageMaker Feature Store"},{"name":"SageMaker Model Registry"},{"name":"SageMaker Clarify"},{"name":"Amazon Bedrock"},{"name":"Amazon S3"},{"name":"AWS CloudWatch"},{"name":"AWS CloudTrail"},{"name":"S3","native":true,"direction":"inbound"},{"name":"SharePoint","native":true,"direction":"inbound"},{"name":"Confluence","native":true,"direction":"inbound"}]` | `[{"name":"llm-compressor"},{"name":"OpenCompass"}]` |
| language.primary | - | Python |
| license.spdx | - | Apache-2.0 |
| market.availability | `{"primaryMarkets":["US"],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` | - |
| platform.support | `{"cli":true,"web":true}` | `{"cli":true,"windows":true}` |
| pricing | `{"type":"usage","summary":"Pay-as-you-go usage pricing. New AWS customers receive up to $200 in Free Tier credits; free tier available for 6 months.","currency":"USD","freeTier":true,"sourceUrl":"https://aws.amazon.com/s3/pricing/?nc2=h_pr_s3","retrievedAt":"2026-09-29T15:03:46.730Z"}` | `{"type":"open_source","freeTier":true,"sourceUrl":"https://lmdeploy.readthedocs.io/en/latest/","retrievedAt":"2026-08-20T12:17:08.568Z"}` |
| pricing.free_tier | yes | yes |
| pricing.model | freemium | open_source |
| pricing.price_level | low | free |
| pricing.transparent | no | - |
| release.cadence_days | - | 21 |
| release.history | - | `[{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.18.0","date":"2026-09-28T07:04:58Z","type":"stable","version":"v0.18.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.17.0","date":"2026-09-01T07:13:39Z","type":"stable","version":"v0.17.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.16.0","date":"2026-08-19T04:43:53Z","type":"stable","version":"v0.16.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.15.0","date":"2026-07-31T13:00:46Z","type":"stable","version":"v0.15.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.14.0","date":"2026-06-24T04:36:56Z","type":"stable","version":"v0.14.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/0.14.0a2","date":"2026-06-16T04:16:04Z","type":"prerelease","version":"0.14.0a2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/0.14.0a1","date":"2026-06-01T08:46:02Z","type":"prerelease","version":"0.14.0a1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.13.0","date":"2026-05-12T03:46:57Z","type":"stable","version":"v0.13.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3","date":"2026-04-08T03:37:26Z","type":"stable","version":"v0.12.3"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.2","date":"2026-03-18T03:13:55Z","type":"stable","version":"v0.12.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.1","date":"2026-02-13T09:02:12Z","type":"stable","version":"v0.12.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.0","date":"2026-02-04T06:28:25Z","type":"stable","version":"v0.12.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.11.1","date":"2025-12-24T13:27:06Z","type":"stable","version":"v0.11.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.11.0","date":"2025-12-04T06:20:38Z","type":"stable","version":"v0.11.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.2","date":"2025-10-28T11:32:42Z","type":"stable","version":"v0.10.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.1","date":"2025-09-26T02:45:30Z","type":"stable","version":"v0.10.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.0","date":"2025-09-09T05:05:09Z","type":"stable","version":"v0.10.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.2.post1","date":"2025-08-19T09:44:41Z","type":"stable","version":"v0.9.2.post1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.2","date":"2025-07-26T10:00:25Z","type":"stable","version":"v0.9.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.1","date":"2025-07-04T10:05:31Z","type":"stable","version":"v0.9.1"}]` |
| security.fedramp | yes | - |
| security.gdpr | yes | - |
| security.hipaa | yes | - |
| security.iso27001 | yes | - |
| security.pci | yes | - |
| security.soc2 | yes | - |
| security.trust_center | https://aws.amazon.com/security/?nc1=f_cc | - |
| security.vulnerabilities | - | `{"count":10,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=lmdeploy&per_page=100","last_12m":8,"max_severity":"CRITICAL"}` |

## Capabilities (Model Serving)

| Capability | Amazon SageMaker Inference | LMDeploy |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Continuous batching | - | ✓ |
| Dynamic batching | - | - |
| Multi framework support | ✓ | - |
| GPU acceleration | ✓ | ✓ |
| Multi GPU multi node | - | ✓ |
| Quantization support | - | ✓ |
| Openai compatible API | - | - |
| Autoscaling scale to zero | ✓ | - |
| Multi model serving | ✓ | ✓ |
| Canary ab rollout | - | - |
| Kubernetes native | - | - |
| Open source | - | - |

*Source: vioscaleAI. Generated 2026-10-16T20:10:29.259Z. "-" = undocumented, not absent.*