⚡Stack Radar

Agent Sandboxes · Reviewed 24 Oct 2026

Together AI

Full-stack cloud to build, train, and deploy open-source AI models.

free tier · together.ai ↗

Paid against free › Agent Sandboxes › Together AI

Together AI at a glance
998.2KEstimated total trafficModeled monthly visits6.2KRanking keywordsPublic search sample97/100Technical health15 pages checked14.7KReferring domainsLive backlink summary

Overview

01

Summary

The useful answer before the supporting detail.

Together AI is a full-stack AI cloud platform for building, fine-tuning, and deploying open-source AI models, from inference to GPU clusters S5. Teams use it to run models efficiently in production, leveraging dedicated infrastructure and managed services S5. Its practical value is accelerating AI development with optimized performance and transparent pricing S23.

02

Product identity and positioning

Name, category, positioning, audience, and first-party claims.

  • Identity: Together AI, a full-stack AI cloud platform S5.
  • Core purpose: Enables building, fine-tuning, and deploying open-source AI models on one platform S5.
  • Intended users: Developers and teams building production AI applications S1.
  • Product scope: A platform including serverless inference, dedicated endpoints, GPU clusters, and sandboxes S5.
  • Positioning: The full-stack platform for production AI, powered by systems research S1.
  • Claimed distinction: Optimized for performance and cost-efficiency, with faster inference S5.
03

Use cases and audiences

Who the product serves and the jobs it is designed to handle.

AudiencePractical use case
AI developersBuild and deploy open-source models with serverless or dedicated endpoints S5
Voice agent teamsDeploy real-time voice agents with low latency S46
ResearchersRun experiments and benchmarks against models and GPUs in sandboxes S25
EnterprisesDeploy GenAI in any environment with full control S98

Features

09

Features and capabilities

Core functions and evidence-backed product capabilities.

CapabilityWhat it does
Serverless inferenceRun open-source models on demand via API with per-token pricing S5 S23
Dedicated GPU endpointsDeploy models on dedicated infrastructure with guaranteed performance S5 S23
Fine-tuningFine-tune open-source models for production using techniques like preference optimization S5
GPU clustersAccess GPU infrastructure for generative media workloads and large-scale training S5
Code SandboxCreate fast, secure dev environments for AI apps with instant VMs and preview hosting S25
Batch APIProcess large workloads asynchronously at lower cost S5
Voice APIDeploy voice models from multiple providers through a single API S46
10

Integrations and ecosystem

Connected products, exports, APIs, and workflow compatibility.

Integration or interfaceTypeWhat it enables
OpenAI-compatible APIAPITest and migrate with existing OpenAI code S98
Python SDKSDKProgrammatically manage sandboxes and models S25
Voice providers (MiniMax, Deepgram, etc.)IntegrationAccess multiple voice models via a single API S46
AWS MarketplaceIntegrationPurchase Together AI via AWS S98
Files APIAPIUpload batch files and manage jobs S108

Pricing

11

Pricing and plans

Published plans, free access, trials, credits, and pricing limitations.

Plan or offerVerified price and billingIncludedLimits
Code Sandbox$0.0446/vCPU-hour + $0.0149/GiB-hour, usage-based S23Not establishedVMs from 2 to 64 vCPUs, 1 to 128 GB RAM S23
Code Interpreter$0.03 per 60-minute session S23Not establishedNot established
Serverless inferencePer 1M tokens, varies by model size S23Not establishedNot established
Dedicated endpointsCustom pricing, contact sales S23Not establishedNot established
GPU clustersCustom pricing, contact sales S23Not establishedNot established
Free credits$25 free credits for new users S75Not establishedNot established

Competition

12

Competitive landscape

Domains competing for the same organic search demand.

Sampled competitors

CompetitorFirst-page appearancesSampled queriesAverage positionQueries observed
reddit.com222.5together ai, llama coder
lablab.ai111together ai
llamacoder.together.ai111llama coder
newcomer.co112together ai
about.fb.com113llama coder
together.fund114together ai
youtube.com114llama coder
instagram.com115together ai
linkedin.com115llama coder
sacra.com116together ai

Website intelligence — Collected evidence

Customers, testimonials, and first-party review claims

No supporting public-page evidence was collected for this category.

Independent review sources discovered

No links to supported independent review platforms were discovered on the collected pages.

14

AI visibility

Measured ChatGPT and Google AI mentions with cited sources.

AI-assistant referral traffic

  • Estimated current generative-AI referral share: 2.7%
MonthSource
—All AI assistants
—All AI assistants
—All AI assistants

Website intelligence — Collected evidence

Site-stated AI capabilities

Page typeEvidenceSource
otherBuild what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.https://www.together.ai/
otherFull-stack AI platform, powered by cutting-edge research.https://www.together.ai/
otherThe Together AI Platformhttps://www.together.ai/
otherGPU infrastructure purpose-built for generative media workloads. Deploy video, audio, and image models with performance acceleration powered by Together Research.https://www.together.ai/
otherUse fast, secure code sandboxes at scale to set up full-scale development environments for AI apps and agents.https://www.together.ai/
otherHigh-performance managed storage for AI-native workloads. Object storage and parallel filesystems optimized for AI, with zero egress fees.https://www.together.ai/
otherFoundational systems research for production AI.https://www.together.ai/
featuresCursorhttps://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresDecagonhttps://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresHedra‍https://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresThe AI Native Cloud is more than a positioning statement. It is a full-stack AI cloud that is purpose-built for AI-natives by researchers and engineers who have delivered foundational AI work such as FlashAttention and ThunderKittens. The same people who published that research are the ones running the production systems our customers, such as Cursor and Decagon, depend on. That proximity is hard to replicate. When a technique comes out of our research program, we can quickly move from research to production and ship these techniques for our customers' immediate benefit.https://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresToday at the first AI Native Conf, we are announcing seven research and product releases across three areas: Kernels, reinforcement learning, and algorithmic inference optimization. Each one represents a massive advancement from our research-to-production pipeline for customers to use.https://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresAn extension of ThunderKittens, together.compile generates an optimized kernel stack at startup with a single function call — no changes to model code required. When applied to Hedra's Omnia video model, together.compile accelerated generation of 200 frames by 25%.https://www.together.ai/blog/ai-native-conf-research-and-product-announcements
featuresThe production platform for open-weight AI inferencehttps://www.together.ai/blog/the-production-platform-for-open-weight-ai-inference
featuresTalk to our teamhttps://www.together.ai/blog/the-production-platform-for-open-weight-ai-inference
featuresWe're also announcing a closed beta for custom training, including full-weight and LoRA reinforcement learning and supervised fine-tuning, with checkpoints you can deploy straight to production. Request access to the custom training beta.https://www.together.ai/blog/the-production-platform-for-open-weight-ai-inference
featuresOpen-weight models now match closed models on quality, run at a fraction of the cost, and are fully customizable for your task. That combination has made them the foundation for teams building serious AI products and agents. But the strategic motivation for adopting open-weight models is still the ability to exercise control. Unlike their closed alternatives, open-weight models give teams control over performance, quality, and functionality. Many commercial AI applications have adopted a majority open-weight endpoint strategy so they can maintain complete control over their user experience. Many enterprises use this control to incorporate their valuable proprietary IP into models without risking exposure to third parties.https://www.together.ai/blog/the-production-platform-for-open-weight-ai-inference
featuresWhile open-weight model users want maximum control over performance, functionality, and quality, few teams want to test their ability to correctly align a matrix of decisions across quantization levels, parallelism schemes, engine parameters, and draft model architectures—to name just a few—on a weekly basis. Meanwhile, frontier techniques for achieving the best quality and performance are advancing just as quickly. In a rapidly evolving AI landscape, no one has the time or desire to stop and read an almanac’s worth of trivia about inference internals or a mountain of research papers—not even the teams running the world’s most successful models and agents.https://www.together.ai/blog/the-production-platform-for-open-weight-ai-inference
featuresBuild voice agents that sound natural. Combine the best STT, LLM, and TTS models on co-located infrastructure for ultra-low latency and production-scale reliability.https://www.together.ai/solutions/voice
featuresWhy Together AI for Voice Agentshttps://www.together.ai/solutions/voice

These are first-party product claims, not independent measurements of AI-search visibility.

15

Marketing opportunities

Actions calculated directly from the collected evidence.

OpportunityRecommended actionEvidence
Competitor gapReview the sampled queries where reddit.com appears.2 first-page appearances across together ai, llama coder
Technical priorityMissing page titlehttps://www.together.ai/blog/glm-5-3-vs-glm-5-3-flash-on-deepswe-cost-coding-and-routing

Website intelligence — Collected evidence

Website evidence gaps

  • customers: no usable public page was collected; verify whether the site publishes this evidence.
  • reviews: no usable public page was collected; verify whether the site publishes this evidence.
  • changes: no usable public page was collected; verify whether the site publishes this evidence.
  • identity: no usable public page was collected; verify whether the site publishes this evidence.
  • technical: no usable public page was collected; verify whether the site publishes this evidence.

Trust and proof

16

Customers, proof, and reviews

Published proof, customer signals, and review evidence.

Independent reviews

No citation-grounded independent review links were found.

Customer proof

No named customers, testimonials, or customer case studies were found.

Independent mentions

  • Build AI development environments — # Build AI development environments ... Use fast, secure code sandboxes at scale to set up full-scale development environments for AI. ... Code Sandbox lets you run experiments and benchmarks against real models and GPUs in a controlled environment — designed for fast iteration before production deployment. ... Create VMs instantly Leverage industry-leading 2.7 second cold-starts (P95), 500ms snapshot resumes (P95) and VM cloning in under 1 second. ... Go beyond Docker images, leveraging Dev Con
  • Code sandbox - Together AI docs — Together code sandbox runs your code in isolated runtime environments. You define an environment as a snapshot built from a Docker image or Dockerfile, then create sandboxes, running instances of a snapshot where you execute commands and work with files. Use it to run untrusted or model-generated code, execute tool calls from agents, or provide rollout environments for reinforcement learning. The `together-sandbox` package ships as a Python SDK, a TypeScript SDK, and a CLI. This page covers the
  • Together Code Sandbox: the most robust infrastructure for building AI coding products at scale — a robust infrastructure, Together Code Sandbox provides programmatic access to the leading virtual sandboxing product, allowing developers to spin up sandboxes in milliseconds and use a wide range of CPU compute (up to
  • Introducing Together Code Sandbox & Together Code Interpreter: SOTA code execution for AI — Fast and scalable snapshotting: Tried-and-true memory snapshotting technology that starts VMs from a snapshot in 500 ms ... ) and clones them in under 1 second. - Suite of dev tools: Control sandbox dev environments with tools for terminal access, tasks
  • Build what's next — - Sandbox Use fast, secure code sandboxes at scale to set up full-scale development environments for AI apps and agents. Learn more - ... = client.hosts.getUrl(3000) await sdk.sandboxes.hibernate(sandbox.id) // Snapshot saved — resume anytime in <1s
  • Together Products — Accelerated Compute Scale from self-serve instant ... with Together Kernel Collection. Learn more read docs Sandbox Use fast, secure code sandboxes at scale to set up full-scale development environments for AI apps and agents. Learn more read docs
  • Code interpreter — Together code interpreter (TCI) enables you to execute Python code in a sandboxed environment. The code interpreter currently only supports Python. Together plans to expand the language options in the future.
  • Pricing — ## Sandbox Code Sandbox Customize a deployment of VM sandboxes for large development environments. Compute costs / Price/Hour / Per vCPU / $0.0446 / / --- / --- / / Per GiB RAM / $0.0149 / ... Duration? / Price/Session / Session (60 minutes) / $0.03 /

External links were grounded in one live web search. Search snippets are leads, not independently scraped page content; rejected links remain available in Review evidence.

17

Company, security, and trust

Ownership, policies, security signals, and public trust evidence.

SignalFindingSource
Company and ownershipFounder and CEO, HeroUIsource
Contact and supportContact us to discuss your enterprise deployment needs. Together APIs are fully compatible with OpenAI, making testing and migration easier.source
Legal and privacyCompliance with major industry standards such as SOC 2, GDPR, and HIPAAsource
Security and complianceEnd-to-end encryption for all data, both in transit and at restsource
Uptime and status pageAutoscale dynamically to thousands of concurrent calls across 25+ global regions. Dedicated GPU endpoints with a 99.9% uptime SLA keep traffic spikes running on pre-warmed capacity, every time.source
Domain registered2017-12-16—
Domain expires2028-02-13—

Every trust signal above is backed by a collected page or a registration record.

18

Documentation and product updates

Help resources, product guidance, and visible changes over time.

Resource or updateTypeWhat is availableDate or freshness
Contact and supportSupportPublic contact and support channel. (source)Not dated
Contact and supportSupportPublic contact and support channel. (source)Not dated
Key research and product announcements at the AI Native ConfProduct updateProduct update is publicly available. (source)Not dated
Together Fine-Tuning Platform, Now With Preference Optimization and Continued TrainingProduct updateProduct update is publicly available. (source)Not dated

Not found in the collected public sources: public roadmap, changelog or release notes, status page, separate help center or knowledge base.

Technical

19

Technical and delivery signals

Delivery model, platforms, API, data ownership, reliability claims and release activity.

SignalFindingSource
Delivery model (cloud, self-hosted, desktop, browser extension)Cloud platform with deployment options including VPC and on-premise S98
Platforms and appsWeb-based platform S23
API and extensibility (API, SDK, webhooks, MCP, plugins)APIs and SDKs are available S98
Data ownership and exportNot published on the collected pages.—
Performance and reliability claimsClaims 2.7s cold starts, 500ms snapshot resumes, VM cloning under 1 second S25
Release activityNot published on the collected pages.—
20

Measured infrastructure

Domain, certificate, and hosting signals, measured directly.

SignalFinding
Domain registered2017-12-16 — NameCheap, Inc.
Domain expires2028-02-13
TLS certificateGoogle Trust Services, valid, expires 2026-11-28
Security headersCSP
Headers not servedHSTS, X-Content-Type-Options, X-Frame-Options, Referrer-Policy
Server headercloudflare
Mail handled byaspmx.l.google.com, alt1.aspmx.l.google.com
Nameserversedward.ns.cloudflare.com, gail.ns.cloudflare.com

Not measurable: wayback: rate limited by archive.org, ladder stopped after 1 probe(s). A gap here means the lookup was unavailable, not that the finding is negative.

21

Technical site audit

A bounded multi-page crawl with page-level issues and URLs.

Scout — Bounded technical crawl

  • Pages crawled: 15 of 15 maximum
  • Technical health: 97/100
  • Critical issues: 1
  • Warnings: 5
  • Notices: 5

Issues

SeverityIssueValueAffected URL
warningTitle length should be reviewed88https://www.together.ai/blog/together-link-frontier-quality-open-models-in-the-harness-you-already-use
noticeImages are missing alt text2https://www.together.ai/blog/together-link-frontier-quality-open-models-in-the-harness-you-already-use
noticeMeta description length should be reviewed224https://www.together.ai/blog/the-open-source-ai-stack
noticeImages are missing alt text2https://www.together.ai/blog/the-open-source-ai-stack
criticalMissing page title—https://www.together.ai/blog/glm-5-3-vs-glm-5-3-flash-on-deepswe-cost-coding-and-routing
noticeImages are missing alt text2https://www.together.ai/blog/glm-5-3-vs-glm-5-3-flash-on-deepswe-cost-coding-and-routing
noticeMeta description length should be reviewed248https://www.together.ai/provisioned-throughput
warningDuplicate title in audit sample2https://www.together.ai/
warningDuplicate title in audit sample2https://www.together.ai/
warningDuplicate meta description in audit sample2https://www.together.ai/
warningDuplicate meta description in audit sample2https://www.together.ai/

Crawled pages

StatusTitleH1sWordsResponseURL
200Together AI \The AI Native Cloud1333766 ms
200Together Link: open models in the harness you already use. Start with one command today.1197573 mshttps://www.together.ai/blog/together-link-frontier-quality-open-models-in-the-harness-you-already-use
200The Open Source AI Stack1460647 mshttps://www.together.ai/blog/the-open-source-ai-stack
200Missing title1411361 mshttps://www.together.ai/blog/glm-5-3-vs-glm-5-3-flash-on-deepswe-cost-coding-and-routing
200Together AI \The AI Native Cloud1333781 ms
200Serverless Inference \Together AI1167841 ms
200Batch Inference \Together AI1145236 ms
200Provisioned Throughput \Together AI1216918 ms
200Dedicated Model Inference \Together AI1198649 ms
200Dedicated Container Inference \Together AI1140020 ms
200Together Link \Open models in your harness \Together AI11584
200Build with leading AI models \Together AI1165747 ms
200NVIDIA GPU Clusters: H100, H200, B200, GB200 \Together AI1178140 ms
200Frontier AI Factory \Together AI1133220 ms
200Sandbox \Together AI1124635 ms

This is a bounded Scout crawl. Core Web Vitals and Lighthouse scores are not claimed unless a dedicated performance audit is connected.

22

Search Console and Analytics

First-party search and behavioral data connection state.

Modeled acquisition channels

ChannelEstimated share
direct51.9%
search37.0%
search organic24.6%
search paid12.4%
social2.9%
social organic2.9%
social paid0.0%
referrals4.7%
paid referrals0.3%
mail0.6%
gen ai2.7%
affiliate0.0%
other0.0%

First-party connections

IntegrationStatusData available after connection
Google Search ConsoleNot connectedClicks, impressions, CTR, average position, queries, pages, countries, and devices
Google AnalyticsNot connectedOrganic sessions, engagement, landing pages, conversions, geography, and devices

No first-party Search Console or Analytics numbers are shown until the site owner authorizes the corresponding Google account. Scout traffic model values above are modeled estimates, not private analytics totals.

Limits and method

23

Limitations and open questions

Known constraints and missing evidence from the collected pages.

Collected evidence does not mention specific alternative products. Gaps include lack of published security certifications, uptime statistics, and detailed product documentation pages. Open questions remain around enterprise contract terms and SLA details.

24

Sources, method, and limitations

Evidence inventory, collection method, provider use, and constraints.

This report is based on 15 collected pages from together.ai, including product, pricing, and blog pages, using static and rendered collection. Sitemap provided 500 URLs, and external proof search was enabled. Limitations: pricing tables from older blog posts may reflect historical pricing, and customer testimonials were not independently verified beyond their presence on the site.

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