Sglang vs Temporal AI

Side-by-side comparison of features, pricing, and ratings

Analysis reviewed Live tool data as of 2026-10-09
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At a glance

DimensionSglangTemporal AI
PricingFree (open-source, self-host)Freemium (self-host free, cloud usage-based billing per Billable Actions)
Primary Use CaseHigh-performance LLM serving and inferenceDurable execution for AI agents and workflows
DeploymentSelf-host only (pip/Docker, single node to cluster)Self-host or Temporal Cloud
Key FeatureDisaggregated prefill/decode, speculative decodingAutomatic state capture, retries, human-in-the-loop
Hardware SupportNVIDIA, AMD, CPU, TPU, Ascend, XPUAny (cloud or on-prem via Docker/K8s)
Latest Newsv0.4.0 release with vision language model support (2026-03-15)Improved cost transparency with usage-based billing (2026-06-25)

For teams building reliable AI agents that need crash-proof execution and human-in-the-loop, Temporal AI is the clear choice. For developers deploying LLMs with maximum throughput and supporting many hardware backends, SGLang is unmatched. These tools are complementary: SGLang serves the model, Temporal orchestrates the workflow around it.

Try SglangTry Temporal AI
Sglang
Sglang

SGLang is the open-source serving engine for LLMs, multimodal and diffusion models, tuned for high throughput on NVIDIA, AMD, TPU, NPU and CPU hardware.

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Temporal AI
Temporal AI

Temporal is the durable execution platform that keeps AI agents and long-running workflows alive through crashes, retries, and abandoned

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Pricing
Free
Freemium
Plans
$0
$150 credits for 90 days
Starting at $50 per million actions
Greater of $500/mo or 10% of usage
Custom
Popularity
26 views
7.5k views
Skill Level
Intermediate
Advanced
API Available
Platforms
APICLI
WebAPI
Categories
🖥️ GPU Cloud & Model Inference
🕸️ Agent Frameworks & Orchestration⚙️ Developer Infrastructure
Features
Open-source inference serving for LLMs, multimodal and diffusion models
Day-0 support for DeepSeek-V4.1 and Kimi K3 (2.8T parameters, 1M context)
Disaggregated prefill/decode serving pipeline
Speculative decoding to cut generation latency
Zero-overhead scheduler for reduced host-side overhead
Optimized GPU kernels including FlashInfer MoE and MLA backends
Runs on NVIDIA GPUs, AMD GPUs, CPU servers, TPU, Ascend NPUs, XPU
Supports DeepSeek, Qwen, GPT-OSS, Llama, Mistral, GLM models
Diffusion model serving: FLUX 3, Qwen-Image 2.1, Ming-Image 0.1
OpenAI-compatible API endpoints for drop-in client compatibility
Beam search returning the n best sequences per request
Unified radix tree prefix caching for hybrid models
Multi-node and multi-GPU distributed inference
Distributed chunked prefill (DCP) for long-context workloads
Chunked pipeline parallelism and tensor/expert/context parallelism
Durable execution captures Workflow state at every step with no checkpointing or recovery code
Native SDKs for Go, Java, Python, TypeScript, .NET, PHP, Ruby, and Rust
Activities retry automatically with backoff, four timeout classes, and heartbeating
Signals, Queries, and Updates read and mutate running Workflows mid-flight
Workflow Streams for real-time interactivity with running executions
Durable AI agents via OpenAI Agents SDK and Google ADK running LLM and tool calls as Activities
Serverless Workers host durable AI agents on Amazon Bedrock AgentCore
Serverless Workers on AWS Lambda (public preview) and GCP Cloud Run (pre-release)
Standalone Activities provide a lighter job-queue pattern with Python examples
Humans-in-the-loop orchestration without wrapper Workflows
Saga pattern via compensating transactions that read like try/catch
Durable Timers sleep for months; cron Schedules support backfill and Continue-As-New
Native Task Queue priority and fair distribution without a custom queueing layer
Worker Versioning pins Workflows to a version; GitHub Actions automates it in CI
Replay tests validate against real workflow histories; Time-skipping tests fast-forward timers
Integrations
OpenAI Agents SDK
Google ADK
AWS Lambda
Google Cloud Run
Amazon Bedrock AgentCore
Kubernetes
GitHub Actions

What real users say: Sglang vs Temporal AI

Not marketing copy and not our opinion — a structured sweep of public discussion (reviews, forums, communities and video comments), showing what people praise and what they complain about for each tool.

Sglang

35 mentions across 2 sources · 75% positive (averaged across 2 sources)

Hacker News, Lemmy

What users praise

  • • Top-tier inference engine alongside vLLM and llama.cpp.
  • • Broad hardware support: NVIDIA, AMD, CPU, TPU, Ascend.
  • • Advanced optimizations like disaggregated prefill/decode and speculative decoding.
  • • OpenAI-compatible API makes integration straightforward.

What frustrates them

  • • Steeper learning curve than Ollama for beginners.
  • • Smaller community than vLLM, fewer tutorials and plugins.
  • • Documentation can be sparse for advanced features or edge-cases.
  • • Occasional instability with very new or proprietary models.

Researched Jul 3, 2026

Temporal AI

No verifiable community signal. We scanned public discussion on Oct 7, 2026 and found posts matching the name “Temporal AI”, but could not establish that they are about this product rather than something else sharing its name. Rather than publish a score built on the wrong subject, we publish none.

Who should pick which

  • AI Agent Developer
    Pick: Temporal AI

    Temporal provides durable execution, retries, and human-in-the-loop, essential for reliable AI agents. Integrates with OpenAI Agents SDK and Google ADK.

  • ML Inference Engineer
    Pick: Sglang

    SGLang offers state-of-the-art inference performance with disaggregated prefill/decode, speculative decoding, and broad hardware support (NVIDIA, AMD, TPU).

  • Startup Building LLM Product
    Pick: Sglang

    Free and open-source, SGLang allows self-hosting with high throughput and low latency, keeping infrastructure costs low.

  • Enterprise Needing Workflow Orchestration
    Pick: Temporal AI

    Temporal's durable execution and visibility suit mission-critical processes like financial transactions with Saga patterns and human approval steps.

  • Researcher Comparing Model Performance
    Pick: Sglang

    SGLang supports a wide range of open models and provides easy benchmarking with its efficient scheduler and parallelisms.

Frequently Asked Questions

Sglang vs Temporal AI: which should you choose?

For teams building reliable AI agents that need crash-proof execution and human-in-the-loop, Temporal AI is the clear choice. For developers deploying LLMs with maximum throughput and supporting many hardware backends, SGLang is unmatched. These tools are complementary: SGLang serves the model, Temporal orchestrates the workflow around it.

Can Temporal AI be used to orchestrate LLM calls served by SGLang?

Yes, Temporal can call any API, including SGLang's OpenAI-compatible endpoint, as part of a workflow.

Which tool is cheaper for a small startup?

SGLang is free and self-hosted, so startup costs are just infrastructure. Temporal Cloud has usage-based billing, but self-hosted Temporal is also free.

Does SGLang support vision-language models?

Yes, since v0.4.0 (released 2026-03-15), SGLang includes vision language model support.

Does Temporal AI require a specific programming library?

No, Temporal provides SDKs for Python, Go, TypeScript, Ruby, C#, Java, PHP, and Rust (public preview).

Can I run SGLang on AMD GPUs?

Yes, SGLang supports AMD GPUs in addition to NVIDIA, CPU, TPU, Ascend, and XPU.

Does Temporal AI have a human-in-the-loop feature?

Yes, via signals and pause/resume, allowing manual approval or intervention in workflows.

Is there a managed cloud option for Temporal?

Yes, Temporal Cloud offers managed service with usage-based billing, as highlighted in their 2026-06-25 news.

Does SGLang provide a REST API?

Yes, SGLang exposes an OpenAI-compatible API, making it easy to integrate with existing tools.

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Explore each tool further

Sglang
View Sglang reviewSglang alternatives
Temporal AI
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Last reviewed: July 3, 2026