Amazon SageMaker
Fully managed machine learning service to build, train, and deploy ML models at scale
Triton Inference Server
Unified AI model serving across clouds, edge, and GPUs
Triton Inference Server delivers high‑performance, multi‑framework model serving for cloud, data‑center, and edge environments, supporting GPUs, CPUs, and AWS Inferentia with dynamic batching, ensembles, and extensive metrics.
Overview
Highlights
Supports multiple deep learning and machine learning frameworks
Dynamic batching, sequence handling, and model ensembling
Extensible backend API with Python‑based custom backends
HTTP/REST and gRPC inference protocols compatible with KServe
Pros
- Broad framework compatibility reduces model conversion effort
- Optimized performance on NVIDIA GPUs, CPUs, and Inferentia
- Rich metrics and tooling for monitoring and profiling
- Strong integration with NVIDIA AI Enterprise ecosystem
Considerations
- Best performance achieved on NVIDIA hardware; CPU fallback may be slower
- Advanced features require detailed configuration and tuning
- Primary deployment model relies on Docker containers
- Enterprise support may require NVIDIA AI Enterprise subscription
Managed products teams compare with
When teams consider Triton Inference Server, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Organizations deploying heterogeneous AI models at scale
- Applications needing low‑latency real‑time inference
- Developers building complex model pipelines and ensembles
- Edge deployments that can leverage GPU or CPU inference
Not ideal when
- Small projects with a single model and minimal scaling needs
- Environments lacking NVIDIA GPUs and without CPU fallback
- Users seeking a lightweight pure‑Python inference server
- Teams requiring a fully managed SaaS inference solution
How teams use it
Real‑time video analytics
Process live video streams with sub‑second latency using GPU‑accelerated models.
Batch recommendation scoring
Run large‑scale recommendation models in dynamic batches to maximize throughput.
Multi‑modal inference pipeline
Combine BERT text analysis with vision models via ensembling for richer predictions.
Edge robotics control
Deploy low‑latency inference on ARM CPUs or Jetson devices for autonomous navigation.
Tech snapshot
Python56%
Shell21%
C++19%
CMake1%
Java1%
Roff1%
Frequently asked questions
Which hardware platforms does Triton support?
Triton runs on NVIDIA GPUs, x86 and ARM CPUs, and AWS Inferentia accelerators.
Can I add my own custom model backend?
Yes, the Backend API lets you implement custom backends, including Python‑based ones.
Is CPU‑only deployment possible?
Yes, Triton can be launched on CPU‑only systems; performance may differ from GPU runs.
How can I monitor inference performance?
Triton provides metrics for GPU utilization, server throughput, latency, and more via Prometheus endpoints.
What deployment methods are recommended?
Docker containers are the primary method, with support for Kubernetes, Helm, and direct binary builds.
Project at a glance
Active- Stars
- 10,255
- Watchers
- 10,255
- Forks
- 1,703
LicenseBSD-3-Clause
Repo age7 years old
Last commit20 hours ago
Primary languagePython
Last synced 3 hours ago