AI visibility report for Together AI
Vertical: AI Code Sandboxes & Agent Runtimes
AI search visibility benchmark across 5 platforms in AI Code Sandboxes & Agent Runtimes.
Also benchmarked
Together AI appears in 2 other verticals
Presence Rate
Top-3 citations across 125 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
Grok
Perplexity
Gemini Search
ChatGPTOverview
Together AI is a full-stack AI platform, self-branded as the 'AI Native Cloud,' providing open-source model inference, GPU compute, code sandboxes, fine-tuning, and model evaluation services. Founded in 2022 and headquartered in Menlo Park, California, the company has raised approximately $534M at a $3.3B valuation as of February 2025. Its platform spans serverless and dedicated inference APIs, self-service GPU clusters (H100 through GB300), Together Sandbox (secure VM-based code execution environments for AI agents, derived from its CodeSandbox acquisition), managed storage, and fine-tuning pipelines optimized through proprietary research — including FlashAttention, ATLAS speculative decoding, and the Together Kernel Collection. The platform serves over 450,000 developers and enterprises including Cursor, Salesforce, and The Washington Post, claiming up to 2x faster inference and 60% lower costs versus closed-source providers.
Together AI is a research-backed, full-stack AI infrastructure platform combining serverless and dedicated LLM inference, GPU cluster compute, secure code sandbox environments, fine-tuning, and model evaluations — enabling AI-native developers and enterprises to build, train, and run production AI applications on a single integrated cloud.
Key Facts
- Founded
- 2022
- HQ
- Menlo Park, California, USA
- Founders
- Vipul Ved Prakash, Ce Zhang, Chris Ré +2 more
- Employees
- 150-250
- Funding
- ~$534M
- ARR
- ~$300M (Sept 2025 est., Sacra); ~$1B ann
- Customers
- 450,000+ developers
- Valuation
- $3.3B (Feb 2025); ~$7.5B reported target
- Status
- Private
Target users
Key Capabilities10
- Serverless inference API for 200+ open-source models with OpenAI-compatible endpoints
- Dedicated model and container inference on single-tenant GPU hardware
- Batch inference API for async, large-scale token processing at reduced cost
- Together Sandbox: secure, fast VM-based code execution environments (2.7s cold start P95, 500ms snapshot resume)
- Self-service GPU clusters (H100, H200, B200, GB200, GB300) with on-demand and reserved pricing
- Fine-tuning platform supporting LoRA and full fine-tuning with SFT and DPO
- Model evaluations API with LLM-judge-based automated scoring
- Managed Storage with parallel filesystems and zero egress fees
- Proprietary inference research: FlashAttention, ATLAS speculative decoding, Together Kernel Collection
- AI Factory for frontier-scale custom infrastructure deployments
Key Use Cases8
- Running open-source LLMs via API without managing GPU infrastructure
- Building real-time, low-latency AI coding assistants and agentic applications
- Executing LLM-generated code securely in sandboxed environments for AI agents
- Fine-tuning foundation models on proprietary datasets for domain-specific tasks
- Training and pre-training custom models on reserved GPU clusters
- Processing large-scale batch inference workloads cost-effectively
- Building and deploying voice AI and multimodal generative media applications
- Rapidly prototyping AI apps with integrated model inference and code execution
Together AI customer outcomes
6x cost reduction per turn vs. GPT-5 mini
Decagon engineered sub-second voice AI using Together AI inference and GPU clusters, achieving dramatic cost reduction versus GPT-5 mini for its AI customer service platform.
60% cost savings
Hedra scales viral AI video generation on Together AI's Dedicated Container Inference and Accelerated Compute, handling traffic surges without performance degradation.
2x latency reduction and ~33% cost savings
Salesforce AI Research achieved significant latency and cost improvements using Together AI dedicated inference for production AI workloads.
11x faster inference vs. prior provider
Vercept achieved a 5x performance breakthrough and 11x faster inference versus OpenAI after switching to Together AI when standard inference frameworks failed to meet their requirements.
10x faster product launch; 98% lower preview cold starts
HeroUI Chat launched 10x faster by using Together Code Sandbox as its core infrastructure for running AI-generated project previews, eliminating the need to build custom VM infrastructure.
72-GPU GB200 NVL72 cluster; weights-to-test-endpoint in days
Cursor partnered with Together AI to deploy production inference on NVIDIA Blackwell (GB200 NVL72) for real-time in-editor AI coding assistance, establishing a weights-to-production pipeline within days.
Recent Trend
How AI describes Together AI3
Together AI Code Sandbox : Snapshotting, forking, real-time terminals; tied to their ecosystem.
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment?
Novita * Together AI Sandbox : MicroVMs on CodeSandbox tech; convenient if using their inference.
I'm evaluating sandboxed agent runtimes for a small team building an AI data analyst tool — what should I look at to avoid the overhead of self-hosting?
E2B, Modal, and Together AI's offerings stand out as the easiest to integrate for managed, sandboxed code execution in LLM apps. These provide API/SDK access to isolated runtimes (often microVMs or secure containers) without you managing servers, Doc...
I'm adding a code interpreter to my LLM app and need a sandboxed runtime — which services are easiest to integrate without managing my own infrastructure?
Most cited sources4
14Introducing Together Code Sandbox & Together Code Interpreter: SOTA code execution for AI
together.ai·Blog Post
- 5
Together Code Interpreter: execute LLM-generated code seamlessly with a simple API call
together.ai·Blog Post
- 3
Together AI | The AI Native Cloud
together.ai·Blog Post
- 1
How LegionEdge Built a Real-Time AI Prototyping Platform with Together Code Sandbox
together.ai·Home
Alternatives in AI Code Sandboxes & Agent Runtimes6
Together AI positions itself as the 'AI Native Cloud' — a full-stack alternative to both hyperscaler AI APIs and pure-play inference providers.
- Its differentiation rests on three pillars: (1) proprietary systems research translated directly into production performance gains (FlashAttention, ATLAS speculative decoding, ThunderKittens kernels); (2) open-source model neutrality, offering 200+ models without proprietary lock-in; and (3) vertical integration from raw GPU clusters through sandbox code execution, enabling AI-native teams to train, fine-tune, deploy, and execute code on a single platform.
- On the sandbox/agent runtime axis specifically, Together AI entered via its acquisition of CodeSandbox and tightly couples code execution sandboxes with its inference and GPU infrastructure — a combination that standalone sandbox providers (E2B, Daytona, Runloop) cannot offer natively.
Reviews
Praised
- Fast inference speeds (~400 tokens/second in production)
- Wide selection of open-source models (200+)
- OpenAI-compatible API for easy migration
- Generous free credits ($100 at signup, up to $50,000 for startups)
- Cost-effective vs. closed-source providers
- Strong inference performance backed by original research
- Fast and simple API key onboarding
Criticized
- Not suitable for non-technical or non-developer users
- Documentation thin or incomplete in some areas
- Unexpected billing if testing is not carefully managed
- Limited free tier for production use
- Technical expertise required to get value
- Sandbox capabilities newer and less mature than standalone sandbox providers
Developer sentiment toward Together AI is broadly positive, particularly for inference speed, open-source model breadth, and cost competitiveness versus proprietary alternatives. Users praise the OpenAI-compatible API for easy migration and the generous startup credit program. Critical feedback centers on the platform's steep learning curve for non-developers, documentation gaps in advanced areas, potential for unexpected billing without careful monitoring, and limited free-tier access for production workloads. The G2 listing surfaces reviews noting inference speeds of approximately 400 tokens/second in production — significantly faster than GPT-4 Turbo — as a standout strength.
Pricing
Serverless inference is token-based and varies by model: e.g., Llama 3.3 70B at $0.88/$0.88 per 1M input/output tokens; DeepSeek-R1-0528 at $3.00/$7.00; gpt-oss-120B at $0.15/$0.60. Batch inference available at approximately 50% discount on most serverless models. Dedicated model inference: $3.99/hr (1x H100), $5.49/hr (1x H200), $9.95/hr (1x B200). GPU cluster on-demand pricing: H100 at $3.49/hr, H200 at $4.19/hr, B200 at $7.49/hr per GPU; reserved pricing discounts up to ~27% for 4-6 month commitments. Together Sandbox Code Interpreter: $0.03/session (60 min); VM compute at $0.0446/vCPU/hr and $0.0149/GiB RAM/hr. Managed Storage: $0.16/GiB/month. Fine-tuning: LoRA from $0.48/1M tokens (models up to 16B). A free-tier credit ($100 at signup, up to $50,000 via Startup Accelerator) is available with no credit card required.
Limitations
- Together AI's sandbox and code-execution capabilities are newer and still maturing following the CodeSandbox acquisition; dedicated sandbox-first competitors such as E2B have longer track records in that specific segment.
- The platform is heavily developer/API-centric and explicitly not suited for non-technical users — documentation has been noted as thin in some areas by G2 reviewers.
- Serverless inference is subject to shared infrastructure rate limits.
- GPU cluster availability at frontier scale (GB200, GB300) requires contacting sales.
- Billing can escalate unexpectedly for users unfamiliar with token-based pricing.
- Infrastructure is primarily US-centric (Maryland, Memphis data centers), with limited international presence (Sweden added September 2025).
- The platform does not offer proprietary foundational model training or managed MLOps pipelines beyond fine-tuning.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Google AI Mode | Gemini Search | Grok | ChatGPT | Perplexity |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
Which sandboxed execution platforms let AI agents run arbitrary shell commands safely without kernel-level escape risks or shared-tenant interference? | |||||
What are the best isolated runtime options for AI agents that need persistent filesystem state across multiple execution steps in a single session? | |||||
Which agent runtime platforms support spawning concurrent sandbox instances so multiple AI agents can run code in parallel for a multi-agent workflow? | |||||
I need a code execution environment that supports GPU workloads for AI-generated training scripts — which sandboxed platforms handle that use case? | |||||
Looking for a sandboxed code interpreter that can handle long-running jobs — 10 to 30 minutes — without hitting timeout limits. What are my options? | |||||
Developer Experience1/5 cited (20%) | |||||
Which agent compute platforms have the most active developer communities and solid docs for teams just getting into agentic AI workflows? | |||||
I want a sandboxed runtime where my team can define reusable execution templates — which platforms make that workflow easy without deep infra knowledge? | |||||
Which code sandbox services have good observability built in so I can actually debug what my AI agent is running inside the environment? | |||||
What do platform engineers typically use to manage ephemeral execution environments for AI agents — and which options have the least operational burden? | |||||
Which AI sandbox platforms offer the best developer experience for iterating on agent tools locally before deploying to production? | |||||
Integrations & Ecosystem0/5 cited (0%) | |||||
What are the best code execution sandbox options that support pre-installing custom dependencies from a private package registry before agent runs? | |||||
Which sandboxed agent runtimes integrate well with popular LLM orchestration frameworks so I don't have to build a custom execution bridge? | |||||
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run? | |||||
Which agent compute platforms avoid heavy lock-in and work across major cloud providers so I can keep data residency in my existing infrastructure? | |||||
I need an AI agent sandbox that allows secure outbound connections to a relational database during execution — which platforms support that? | |||||
Performance & Reliability0/5 cited (0%) | |||||
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day? | |||||
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter? | |||||
Which microVM sandbox services have the lowest cold-start latency for AI agent code execution at scale — sub-500ms range? | |||||
My AI agent generates and executes code in a tight loop — which sandbox platforms sustain high-frequency execution without degrading over time? | |||||
Which isolated execution environments scale elastically under bursty AI agent traffic without me having to pre-provision capacity? | |||||
Setup & First Run2/5 cited (40%) | |||||
Looking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly? | |||||
I'm evaluating sandboxed agent runtimes for a small team building an AI data analyst tool — what should I look at to avoid the overhead of self-hosting? | |||||
What's the fastest sandbox runtime to spin up for an AI agent backend — which platforms let you get isolated code execution running in under 5 minutes? | |||||
Which microVM-based sandbox platforms have the smoothest onboarding for a solo developer shipping an AI coding assistant MVP? | |||||
I'm adding a code interpreter to my LLM app and need a sandboxed runtime — which services are easiest to integrate without managing my own infrastructure? | |||||
Strengths1
I'm adding a code interpreter to my LLM app and need a sandboxed runtime — which services are easiest to integrate without managing my own infrastructure?
Avg # 1.0 · 1 platform
Gaps5
Looking for an ephemeral code execution environment I can provision per user session — which services have a simple SDK or API to get started quickly?
Competitors on 4 platforms
What sandboxed agent runtime platforms are best suited for production workloads executing user-submitted code thousands of times per day?
Competitors on 4 platforms
Which code sandbox platforms are considered production-ready for enterprise AI applications where uptime and SLA guarantees actually matter?
Competitors on 4 platforms
Which sandboxed agent runtimes integrate well with popular LLM orchestration frameworks so I don't have to build a custom execution bridge?
Competitors on 4 platforms
What sandboxed execution environments have good support for streaming output back to the calling application in real time during an agent's code run?
Competitors on 4 platforms
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Northflank | 66.4% | 42.4% | 3.2% | 66.4% | 58.4% | #19.4 | +0.37 |
| 2 | Modal | 49.6% | 25.5% | 6.4% | 8.0% | 48.0% | #18.1 | +0.41 |
| 3 | E2B | 25.6% | 13.2% | 10.4% | 8.0% | 25.6% | #26.1 | +0.40 |
| 4 | Daytona | 15.2% | 7.3% | 7.2% | 3.2% | 15.2% | #18.9 | +0.46 |
| 5 | Cloudflare | 12.0% | 4.0% | 2.4% | 6.4% | 11.2% | #27.0 | +0.42 |
| 6 | Fly.io | 6.4% | 2.5% | 3.2% | 0.8% | 6.4% | #17.6 | +0.41 |
| 7 | CodeSandbox | 4.8% | 2.0% | 2.4% | 0.0% | 4.8% | #24.7 | +0.38 |
| 8 | Together AI | 4.0% | 0.9% | 0.0% | 2.4% | 4.0% | #7.3 | +0.42 |
| 9 | Runloop | 4.0% | 2.2% | 2.4% | 0.0% | 4.0% | #62.7 | +0.40 |
| 10 | Morph Labs | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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