AI visibility report
Together AI ranks #6 in LLM Inference & Serverless GPU AI search.
Outside the top three on 17 of the 25 prompts buyers actually ask.
RunPod is cited on 9 of those losses.
Free trial. Setup comes pre-filled for Together AI.
Also benchmarked
Together AI appears in 2 other verticals
Track Together AI across these prompts daily.
Start free trial#6 among 10 vendors · still absent from 94.4% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
ChatGPT
Gemini Search
Bing Copilot
PerplexityHow to read this. Together AI appears in 5.6% of tracked prompt responses and ranks #6 among 10 vendors. Presence is absolute coverage; share of voice is relative citation share; sentiment measures tone only when the brand appears.
Where Together AI is losing
Prompts where competitors are visible and Together AI is not.
These prompt-level losses are the first prompts to track and repair.
Where Together AI is winning4
Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code?
Avg # 1.0 · 1 platform
Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments?
Avg # 2.0 · 1 platform
What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments?
Avg # 2.0 · 1 platform
Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator?
Avg # 3.0 · 1 platform
Where Together AI is losing5
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times?
Competitors on 3 platforms
Track this promptWhich serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying?
Competitors on 3 platforms
Track this promptWhat are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage?
Competitors on 3 platforms
Track this promptWhich serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?
Competitors on 2 platforms
Track this promptWhat are the best LLM serving platforms for a small ML team that needs built-in request logging and usage dashboards without wiring up a separate observability stack?
Competitors on 2 platforms
Track this prompt
Track Together AI daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Together AI is a San Francisco-based AI infrastructure company founded in 2022 that operates what it calls the 'AI Native Cloud' — a full-stack platform for LLM inference, GPU compute, and model fine-tuning. The platform provides serverless and dedicated inference APIs across 200+ open-source models (Llama, DeepSeek, Qwen, Mistral, and others) with OpenAI-compatible endpoints, self-service GPU cluster provisioning using NVIDIA H100 through GB200 hardware, and a fine-tuning pipeline covering LoRA, full fine-tuning, and DPO. A distinguishing feature is Together's active in-house systems research team, which has produced widely adopted work including FlashAttention, ATLAS speculative decoding, and ThunderKittens GPU kernels — research that the company deploys directly to its production infrastructure.
Together AI is a full-stack AI infrastructure platform — branded as the 'AI Native Cloud' — that enables developers and enterprises to run, fine-tune, and scale open-source AI models in production. It combines a high-performance serverless and dedicated inference API layer, self-service NVIDIA GPU clusters (H100 through GB200), a fine-tuning and model evaluation suite, managed AI-optimized storage, and developer tooling including a code sandbox. The platform is differentiated by an active in-house systems research function that has developed FlashAttention, ATLAS speculative decoding, and ThunderKittens GPU kernels — research improvements that are deployed directly to improve inference throughput and cost efficiency for customers.
Key Facts
- Founded
- 2022
- HQ
- Menlo Park, CA, USA
- Founders
- Vipul Ved Prakash, Ce Zhang, Chris Ré +2 more
- Employees
- 150-250
- Funding
- ~$534M
- ARR
- ~$1B annualized (late 2025, per reports)
- Customers
- 450,000+ registered developers
- Valuation
- $3.3B (Feb 2025)
- Status
- Private
Target users
Key Capabilities10
- Serverless LLM inference API with OpenAI-compatible endpoints across 200+ open-source models
- Dedicated model inference on reserved, isolated hardware with guaranteed SLAs
- Batch inference API processing up to 30B tokens at 50% lower cost than real-time APIs
- Fine-tuning platform supporting LoRA, full fine-tuning, DPO, and long-context training
- Self-service GPU cluster provisioning (H100, H200, B200, GB200 NVL72) with managed Slurm
- Proprietary inference research: FlashAttention (3 & 4), ATLAS speculative decoding, ThunderKittens GPU kernels
- Managed AI-optimized storage with zero egress fees (object storage + parallel filesystem)
- Code sandbox and code interpreter for building and executing AI agent workloads
- Model evaluation tooling for quality measurement and regression tracking
- AI Factory for frontier-scale custom infrastructure deployments (1K–100K+ GPUs)
Key Use Cases8
- Production-scale LLM inference for AI-native SaaS applications
- Real-time voice AI and conversational agent serving with sub-400ms latency budgets
- Fine-tuning open-source models on proprietary data to reduce cost and improve accuracy
- Large-scale batch data processing (document classification, annotation, data transformation)
- Generative video, image, and audio model training and inference
- GPU cluster training and pre-training of foundation models
- Agentic AI systems requiring code execution and long-context reasoning
- Open-source model experimentation and prototyping via Playground and APIs
Together AI customer outcomes
6× cost reduction per turn vs. GPT-5 mini; p95 latency <400ms
Partnered with Together AI to run production inference for its multi-model voice AI stack, achieving sub-second latency for conversational agents through speculative decoding and Blackwell GPU optimization.
60% cost savings; 3× faster inference on Blackwell; 300× GPU usage growth
Migrated AI video generation workloads to Together AI's GPU clusters and kernel-optimized inference, enabling viral-scale elasticity while cutting infrastructure costs and eliminating the need for 1-2 dedicated platform engineers.
~33% cost savings; 2× latency reduction
Salesforce AI Research deployed open-source model inference on Together AI, achieving significant latency and cost improvements compared to prior infrastructure.
2× CSAT score improvement
Built an AI customer support bot on Together AI's inference platform that scaled to over 1,000 messages per minute while doubling customer satisfaction scores.
90ms model latency
Runs real-time voice AI model serving on Together AI's GPU infrastructure, achieving production-grade latency suitable for conversational applications.
2-second response time at production scale
Deployed Together AI for reliable, privacy-compliant LLM inference, achieving fast editorial AI response times while maintaining data sovereignty.
Recent Trend
How AI describes Together AI3
Direct answer: Hugging Face Inference Endpoints, Together AI, and Fireworks AI are among the easiest onboarding options for a solo developer deploying a fine-tuned open-source model.
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time?
Other providers like Together AI and Fireworks AI are also competitive depending on workload and model mix.
Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users?
Baseten and Together AI: designed for deploying ML-powered apps and workflows with orchestration-friendly APIs and model composition capabilities.
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly?
Most cited sources7
14Serverless Inference | Together AI
together.ai·Product Page
- 10
Together AI | The AI Native Cloud
together.ai·Product Page
- D6
Overview - Together AI docs
docs.together.ai·Documentation
- D5
Third-party integrations - Together AI docs
docs.together.ai·Documentation
- 3
Dedicated Model Inference | Together AI
together.ai·Product Page
- D2
Retrieval-augmented generation - Together AI docs
docs.together.ai·Documentation
Alternatives in LLM Inference & Serverless GPU6
Together AI positions itself as the 'AI Native Cloud' — a full-stack platform that combines serverless and dedicated LLM inference, GPU cluster provisioning, fine-tuning, and proprietary research-backed optimization (FlashAttention, ATLAS speculative decoding, ThunderKittens kernels) in one vertically integrated offering.
- Its key differentiator is that inference speed improvements are driven by in-house systems research rather than purely infrastructure procurement, claiming up to 2× faster inference versus alternatives.
- This sets it apart from GPU resellers (RunPod, Replicate) and from narrow inference-API specialists (Fireworks AI, Lepton AI) by offering the full lifecycle from model shaping through production serving.
- It targets AI-native companies and enterprise teams building on open-source models who need performance, cost efficiency, and the flexibility to avoid proprietary-model vendor lock-in.
Reviews
Praised
- Broad open-source model library (200+ models)
- Competitive pricing vs. closed-source providers
- OpenAI-compatible API for easy migration
- Fast inference throughput (~400 tokens/sec reported by users)
- Ease of prototyping and API key access
- Cost-efficient fine-tuning on custom data
- Responsive engineering support for enterprise customers
- Research-backed kernel optimizations (FlashAttention, ATLAS)
Criticized
- Complex and variable per-model token pricing (unpredictable bills)
- Significant developer integration effort required (not plug-and-play)
- Model deprecations without sufficient advance notice to customers
- Limited free tier for accessing models
- Occasional latency on long-context or high-load queries
- Thin public review footprint makes independent validation difficult
Formal third-party review coverage of Together AI is limited — G2 lists only 4 verified reviews as of the research date, insufficient for statistical reliability. Available user feedback highlights the breadth of the open-source model library, competitive pricing relative to closed-source providers, OpenAI API compatibility that simplifies migration, and the platform's utility for rapid LLM prototyping. Criticisms noted include unpredictable billing due to per-model variable pricing, the engineering effort required to integrate the platform, and isolated complaints about models being deprecated from serverless endpoints without sufficient advance notice. Customer case studies from named enterprise users (Salesforce, Decagon, Hedra, Cursor, Washington Post, Zomato) report strong quantitative outcomes across cost, latency, and throughput dimensions.
Pricing
Together AI uses a pay-as-you-go model with three primary pricing tiers. Serverless inference is charged per million tokens, with separate input and output rates varying by model; prices range from approximately $0.05 to $7.00 per million tokens. Batch inference is priced at 50% of real-time API rates for most models, with support for up to 30B enqueued tokens. Fine-tuning is billed per million tokens processed during training, varying by model size and method (LoRA vs. full fine-tuning, SFT vs. DPO). GPU clusters are available on a pay-as-you-go hourly basis (approximately $3.49/hr for H100, $4.19/hr for H200, $7.49/hr for B200) or as reserved capacity with commitment discounts for periods over 6 days. Dedicated model inference endpoints are billed per minute of usage. Managed storage and sandbox environments carry additional fees. Enterprise and AI Factory deployments require custom pricing via sales.
Limitations
- Together AI requires significant developer effort to integrate and maintain — it is not a plug-and-play solution.
- Variable per-token and per-model pricing across 200+ models can lead to unpredictable billing during usage spikes.
- Some users have reported frustration with models being deprecated from serverless endpoints without sufficient advance notification.
- The platform is primarily developer- and infrastructure-focused, with limited no-code tooling for non-technical users.
- The G2 review footprint is very small (4 reviews as of research date), making external review-based validation limited.
- Enterprise features such as VPC deployment, advanced access controls, and SLA terms appear to require direct sales engagement rather than being self-serve.
Frequently asked questions
Topic coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Google AI Mode | Gemini Search | Bing Copilot | Perplexity |
|---|---|---|---|---|---|
Capability1/5 cited (20%) | |||||
What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | Neither your brand nor a competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm looking for an inference platform that supports custom CUDA kernels and speculative decoding — what are my options for a performance-critical chatbot? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience1/5 cited (20%) | |||||
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the best LLM serving platforms for a small ML team that needs built-in request logging and usage dashboards without wiring up a separate observability stack? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature? | Your brand and a competitor were cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem2/5 cited (40%) | |||||
Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code? | Your brand was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which inference platforms expose an API compatible with the standard chat completions format so switching providers requires minimal code changes? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability1/5 cited (20%) | |||||
Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments? | A competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Setup & First Run1/5 cited (20%) | |||||
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | A competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | A competitor was cited | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
I'm evaluating serverless inference platforms for a small startup — which ones let you deploy a custom open-source LLM without writing any infrastructure config? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
What are the fastest serverless GPU inference platforms to go from an open-source LLM to a live production API endpoint with no GPU infrastructure to manage? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 15.2% | 29.5% | 1.6% | 0.0% | 40.0% | #4.2 | +0.32 |
| 2 | Fireworks AI | 12.0% | 17.0% | 1.6% | 6.4% | 36.8% | #3.4 | +0.46 |
| 3 | Beam | 11.2% | 17.9% | 0.0% | 0.0% | 12.0% | #4.8 | +0.29 |
| 4 | Baseten | 8.8% | 16.1% | 6.4% | 3.2% | 42.4% | #2.6 | +0.50 |
| 5 | Modal | 8.0% | 9.8% | 0.0% | 2.4% | 0.0% | #3.2 | +0.55 |
| 6 | Together AI | 5.6% | 7.1% | 2.4% | 0.8% | 44.8% | #2.1 | +0.30 |
| 7 | Cerebrium | 2.4% | 2.7% | 0.8% | 0.0% | 8.0% | #2.0 | +0.60 |
| 8 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 32.8% | — | — |
| 10 | Sference | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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