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Tools/Together Inference vs Ray Serve
Together Inference

Together Inference

infrastructure
vs
Ray Serve

Ray Serve

infrastructure

Together Inference vs Ray Serve — Comparison

Overview
What each tool does and who it's for

Together Inference

Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.

⚡️ FlashAttention-4: up to 1.3× faster than cuDNN on NVIDIA Blackwell → Introducing Together AI's new look → 🔎 ATLAS: runtime-learning accelerators delivering up to 4x faster LLM inference → ⚡ Together GPU Clusters: self-service NVIDIA GPUs, now generally available → 📦 Batch Inference API: Process billions of tokens at 50% lower cost for most models → 🪛 Fine-Tuning Platform Upgrades: Larger Models, Longer Contexts → The full stack platform for production AI, powered by cutting-edge systems research. We design a full-stack AI platform powered by cutting edge system research — helping teams ship faster, scale reliably and achieve superior unit economics. Open and responsible development Everything works best when we help the open-source community work better together. Our wonder, curiosity, and hope drive us to find ways to make everyone’s lives better. We are optimizers, making the most with what we have and not taking more than what we need. We build everything with the purpose of benefiting society. Featured partners that help us scale Meet our leaders, researchers and engineers building the systems behind Together AI. Senior Director of People Ops SVP of Engineering Infrastructure VP OF Technical Program Management

Ray Serve

Based on the social mentions provided, Ray Serve appears to be well-regarded as part of the broader Ray ecosystem for distributed AI and ML workloads. Users appreciate its integration with popular tools like SGLang and vLLM for both online and batch inference scenarios, with new CLI improvements making large model development more accessible. The active community engagement through frequent meetups, office hours, and educational content suggests strong adoption and support, particularly for LLM inference at scale. The mentions focus heavily on technical capabilities and real-world production use cases, indicating Ray Serve is viewed as a serious solution for enterprise-scale AI deployment rather than just an experimental tool.

Key Metrics
—
Avg Rating
—
0
Mentions (30d)
1
—
GitHub Stars
41,936
—
GitHub Forks
7,402
—
npm Downloads/wk
—
—
PyPI Downloads/mo
—
Community Sentiment
How developers feel about each tool based on mentions and reviews

Together Inference

0% positive100% neutral0% negative

Ray Serve

0% positive100% neutral0% negative
Pricing

Together Inference

subscription + tieredFree tier

Pricing found: $0.30, $0.06, $1.20, $0.50, $2.80

Ray Serve

tiered

Pricing found: $100

Features

Only in Ray Serve (1)

Ray Serve:...
Developer Ecosystem
—
GitHub Repos
—
—
GitHub Followers
—
—
npm Packages
20
—
HuggingFace Models
3
—
SO Reputation
—
Product Screenshots

Together Inference

Together Inference screenshot 1Together Inference screenshot 2

Ray Serve

No screenshots

Company Intel
information technology & services
Industry
information technology & services
380
Employees
9
$533.5M
Funding
—
Series B
Stage
—
Supported Languages & Categories

Together Inference

AI/MLDevOpsDeveloper Tools

Ray Serve

AI/MLDevOpsSecurityAnalyticsDeveloper Tools
View Together Inference Profile View Ray Serve Profile