AI visibility report for Together AI
Vertical: LLM Inference & Serverless GPU
AI search visibility benchmark across 3 platforms in LLM Inference & Serverless GPU.
Also benchmarked
Together AI appears in 2 other verticals
Presence Rate
Top-3 citations across 75 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
ChatGPT
PerplexityOverview
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
| Platform | Inference API | Fine-tuning API/workflow | Typical OSS models | Notes | | --- | --- | --- | --- | --- | | Together AI | Yes | Yes | Llama, Qwen, Mistral, DeepSeek, Gemma | Probably the cle...
What platforms offer fine-tuning APIs alongside inference for the same open-source models?
...s | Less infra control | | Fireworks AI | High-performance LLM serving | Yes | Production LLM APIs | More LLM-focused | | Together AI | Open-model inference | Yes | Open-source LLM products | Less generic serverless compute | | Google Cloud Run \+ GPU |...
Which serverless GPU platforms have proven track records with high-traffic AI applications?
...er OSS models | | Groq | ~$0.05–0.79 | Fastest inference latency | Slightly pricier than DeepInfra but extremely fast | | Together AI | ~$0.05–0.90 | Huge model catalog + fine-tuning | Good developer ecosystem | | Fireworks AI | ~$0.07–0.90 | Reliability...
Which LLM inference providers offer the cheapest pricing per million tokens for open-source models?
Most cited sources5
3Products: Inference, Fine-Tuning, Training, and GPU Clusters | Together AI
together.ai·Product Page
- 3
Fine-Tuning | Together AI
together.ai·Product Page
- D2
Introduction - Together.ai Docs
docs.together.ai·Docs
- D1
Introduction - Together.ai Docs
docs.together.ai·Docs
- D1
OpenAI compatibility - Together AI docs
docs.together.ai·Docs
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 Coverage
Prompt-Level Results
| Prompt | Gemini Search | ChatGPT | Perplexity |
|---|---|---|---|
Capabilities1/5 cited (20%) | |||
Which GPU clouds support multi-modal model inference including vision, audio, and image generation? | |||
What platforms offer fine-tuning APIs alongside inference for the same open-source models? | |||
What inference platforms provide LoRA adapter swapping at request time? | |||
Which inference providers support custom model deployment beyond just popular open-source weights? | |||
Which serverless AI providers offer EU data residency and sovereign infrastructure for regulated workloads? | |||
Cost & Pricing0/5 cited (0%) | |||
What's the most cost-effective way to run a high-volume RAG pipeline against an open-weights model? | |||
What serverless GPU platforms charge per-second so I'm not paying for idle time? | |||
Which inference platforms offer batch or async pricing tiers with significant discounts for non-realtime workloads? | |||
Which GPU cloud providers offer spot or preemptible pricing for AI workloads? | |||
Which LLM inference providers offer the cheapest pricing per million tokens for open-source models? | |||
Performance0/5 cited (0%) | |||
Which LLM inference providers have the lowest cold start times for serverless GPU workloads? | |||
Which serverless AI platforms can handle bursty traffic to long-running model endpoints? | |||
What inference platforms deliver the highest tokens-per-second for Llama 70B and similar large models? | |||
What are the best inference platforms for low-latency real-time agent workflows? | |||
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays? | |||
Production Readiness0/5 cited (0%) | |||
Which LLM inference platforms have the most reliable uptime and SLAs for production workloads? | |||
Which GPU compute providers support running models inside a customer's VPC for compliance? | |||
Which serverless GPU platforms have proven track records with high-traffic AI applications? | |||
What inference platforms include built-in observability, logging, and alerting for production model deployments? | |||
What inference providers offer dedicated capacity or reserved GPU instances for predictable performance? | |||
Setup & First Run1/5 cited (20%) | |||
What's the fastest way to deploy an open-source LLM behind an API endpoint without managing GPUs? | |||
Which serverless GPU platforms let me run a Hugging Face model with a single CLI command? | |||
I need a hosted inference API for Llama or Mistral that I can hit with an OpenAI-compatible client — what are my options? | |||
What's the easiest way to run my own fine-tuned model in production without provisioning GPUs? | |||
Which inference platforms have the lowest learning curve for a frontend developer who just wants an API key? | |||
Strengths1
I need a hosted inference API for Llama or Mistral that I can hit with an OpenAI-compatible client — what are my options?
Avg # 4.0 · 1 platform
Gaps5
Which serverless GPU platforms have proven track records with high-traffic AI applications?
Competitors on 2 platforms
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays?
Competitors on 2 platforms
Which GPU clouds support multi-modal model inference including vision, audio, and image generation?
Competitors on 1 platform
Which serverless GPU platforms let me run a Hugging Face model with a single CLI command?
Competitors on 1 platform
Which serverless AI platforms can handle bursty traffic to long-running model endpoints?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 13.3% | 42.9% | 1.3% | 0.0% | 13.3% | #7.5 | +0.06 |
| 2 | Modal Labs | 6.7% | 20.0% | 0.0% | 4.0% | 6.7% | #5.0 | +0.25 |
| 3 | Cerebrium | 4.0% | 11.4% | 0.0% | 0.0% | 2.7% | #4.3 | +0.02 |
| 4 | Together AI | 4.0% | 17.1% | 2.7% | 0.0% | 4.0% | #6.3 | +0.23 |
| 5 | Beam | 1.3% | 2.9% | 0.0% | 0.0% | 1.3% | #1.0 | +0.00 |
| 6 | Fireworks AI | 1.3% | 2.9% | 1.3% | 0.0% | 1.3% | #3.0 | +0.70 |
| 7 | Sference | 1.3% | 2.9% | 0.0% | 0.0% | 0.0% | #5.0 | +0.00 |
| 8 | Baseten | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 10 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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