AI visibility report for Together AI
Vertical: AI/ML Infrastructure & LLM Tools
AI search visibility benchmark across 5 platforms in AI/ML Infrastructure & LLM Tools.
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
Gemini Search
Perplexity
Grok
ChatGPT
Google AI ModeOverview
Together AI is a Menlo Park, California–based AI infrastructure company founded in 2022 that operates what it calls the 'AI Native Cloud' — a full-stack platform for running, fine-tuning, and training open-source AI models. The platform provides serverless and dedicated inference APIs across 200+ models, GPU cluster rentals spanning H100 through NVIDIA Blackwell GB300, fine-tuning tools supporting LoRA and DPO, a code sandbox, managed storage, and proprietary systems research including FlashAttention and ATLAS speculative decoding. Together AI claims approximately 2× faster inference than comparable platforms through its custom kernel work. The company serves over 450,000 developers and named enterprise customers including Cursor, Salesforce, Decagon, Hedra, and The Washington Post. It raised a $305M Series B at a $3.3B valuation in February 2025, backed by General Catalyst, NVIDIA, Kleiner Perkins, and Salesforce Ventures.
Together AI provides a full-stack 'AI Native Cloud' purpose-built for AI model deployment and development, combining serverless and dedicated LLM inference across 200+ open-source models, NVIDIA GPU cluster compute from H100 through Blackwell GB300, fine-tuning, evaluations, a code sandbox, and managed storage — all underpinned by proprietary systems research in inference optimization (FlashAttention, ATLAS, ThunderKittens) and an OpenAI-compatible API surface.
Key Facts
- Founded
- 2022
- HQ
- Menlo Park, CA, USA
- Founders
- Vipul Ved Prakash, Ce Zhang, Chris Ré +2 more
- Employees
- ~197
- Funding
- ~$537M
- ARR
- ~$300M (Sept 2025, est. Sacra)
- Customers
- 450,000+ developers
- Valuation
- $3.3B (Feb 2025 Series B)
- Status
- Private
Target users
Key Capabilities10
- Serverless inference API covering 200+ open-source models across chat, vision, image, audio, video, embeddings, and reranking modalities
- Dedicated model inference on single-tenant GPU hardware with guaranteed performance and autoscaling
- Batch inference API for async large-scale workloads at up to 50% lower cost than serverless
- GPU cluster provisioning (H100, H200, B200, GB200 NVL72, GB300 NVL72) with on-demand and reserved pricing
- Fine-tuning platform supporting LoRA and full fine-tuning via SFT and DPO, with no infrastructure management required
- Proprietary inference research deployed to production (FlashAttention-3/4, ATLAS runtime-learning speculative decoding, ThunderKittens GPU kernels)
- Code Sandbox for secure, scalable code execution integrated with LLM calls
- OpenAI-compatible API for seamless migration from existing integrations
- Model Evaluations API with LLM-judge scoring and automated reports
- AI Factory offering custom frontier-scale infrastructure deployments (GB200/GB300 clusters, custom power capacity)
Key Use Cases8
- Production LLM inference for AI-native SaaS applications requiring low latency and high throughput
- Fine-tuning open-source models on proprietary datasets for domain-specific accuracy
- High-throughput batch processing of text, image, and multimodal content at scale
- GPU cluster rental for LLM pre-training, RL post-training, and research workloads
- Real-time voice AI and sub-second latency agentic application serving
- Deploying custom generative media models (video, image, audio) in production
- Building RAG pipelines using integrated embedding models, vector store connectors, and reranking
- Rapid prototyping across 200+ open-source models via a unified OpenAI-compatible API
Together AI customer outcomes
72 GB200 GPUs in production; days from weights to test endpoint
Cursor partnered with Together AI to deploy real-time coding inference on NVIDIA Blackwell (GB200 NVL72), establishing a repeatable quantization pipeline from new model weights to a production-like test endpoint.
6x cost reduction per turn vs. GPT-5 mini
Decagon used Together AI's inference and fine-tuning infrastructure to power sub-second voice AI, achieving a significant cost reduction compared to competing closed-source API providers.
60% cost savings
Hedra scales viral AI video generation using Together AI's Dedicated Container Inference and GPU clusters, absorbing traffic surges without performance degradation.
2x latency reduction; ~33% cost savings
Salesforce AI Research migrated inference workloads to Together AI, achieving measurably faster response times and lower infrastructure spend compared to prior providers.
11x faster inference
Vercept achieved a major inference performance breakthrough with Together AI after standard inference frameworks failed to meet latency and throughput requirements.
2x CSAT score; scaled to 1,000+ messages per minute
Zomato built an AI customer support bot on Together AI's inference platform that doubled customer satisfaction and scaled to handle high request volumes.
Recent Trend
How AI describes Together AI3
| Vector | Managed Platforms (Anyscale, Together AI, Groq) | Self-Hosted GPUs (AWS, GCP, Run:ai) | | --- | --- | --- | | Speed to Market | Minutes | Weeks to months | | Cost Structure | Pay-per-token | Fixed hourly instance rates | | Ope...
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps?
| | Dedicated bare metal / Pods | GMI Cloud , Together AI , RunPod | Custom open-source model configurations and data privacy.
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at?
...AI infrastructure platforms and \"LLM Gateways\" let you swap between commercial API providers (like OpenAI, Anthropic, and Google ) and open-source models (hosted via Hugging Face, Together AI, vLLM, or Ollama) with zero application code changes.
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region?
Most cited sources
No cited source mix is available for this brand yet.
Alternatives in AI/ML Infrastructure & LLM Tools6
Together AI positions itself as the 'AI Native Cloud' — a research-grounded, full-stack alternative to both legacy cloud providers and narrow inference API services.
- It differentiates through proprietary inference research (FlashAttention, ATLAS speculative decoding, ThunderKittens kernels), claiming approximately 2× faster inference than comparable platforms, an OpenAI-compatible API surface covering 200+ open-source models, and an integrated stack spanning serverless inference, dedicated GPU clusters, fine-tuning, and sandbox environments.
- Compared to Fireworks AI and Replicate, Together AI offers a broader compute and training surface; versus hyperscalers, it provides a model-native, open-source-first developer experience at more competitive per-token economics.
Reviews
Praised
- Fast inference speed (~400 tokens/sec reported in production)
- OpenAI API compatibility for seamless migration
- Large and diverse open-source model selection (200+)
- Competitive pricing vs. closed-source API providers
- Strong documentation, cookbooks, and code examples
- Eliminates GPU cluster management overhead for teams
- Research-backed performance improvements (FlashAttention, ATLAS)
Criticized
- Technical expertise required for full platform utilization
- Very sparse verified reviews on major third-party platforms
- Occasional documentation gaps noted by some users
- Payment and access friction reported by a subset of users
- No direct support for closed-source frontier models (GPT-4o, Claude, etc.)
Third-party verified review data for Together AI's cloud platform is sparse on major review platforms as of research date. Developer community analysis and third-party assessments highlight strong inference throughput (~400 tokens/sec in production), ease of OpenAI API compatibility enabling rapid migration, broad open-source model selection, and competitive pricing relative to closed-source providers. Identified criticisms across community sources include a learning curve for full platform utilization, technical expertise requirements, occasional documentation gaps, and some payment or access friction. Enterprise customers (Salesforce, Cursor, Hedra, Decagon, Zomato) publicly report strong outcomes around cost reduction, latency improvement, and scaling reliability.
Pricing
Together AI uses pay-per-use pricing across all product lines. Serverless inference is billed per million tokens: small models start at approximately $0.03–$0.10/1M input tokens; large reasoning models such as DeepSeek R1 cost up to $3.00/1M input and $7.00/1M output tokens. Batch inference is priced at up to 50% below serverless rates. Fine-tuning starts at $0.48/1M tokens for LoRA SFT on models up to 16B parameters, scaling to specialized model pricing (e.g., $10/1M for DeepSeek-class models). GPU clusters are available on-demand (H100 at $3.49/hr, H200 at $4.19/hr, B200 at $7.49/hr) with reserved discounts for commitments of one week or longer (e.g., H100 from $2.55/hr at 4–6 months). Dedicated inference instances start at $3.99/hr (H100). Sandbox compute is billed at $0.0446/vCPU/hr and $0.0149/GiB RAM/hr. Enterprise-tier, AI Factory, and GB200/GB300 cluster pricing require direct sales contact.
Limitations
- Together AI's platform is primarily optimized for open-source models; teams requiring proprietary closed-source frontier models (GPT-4o, Claude) are not directly served.
- Third-party review data is sparse, with very few verified reviews on major platforms, making systematic user sentiment analysis difficult.
- Some community feedback identifies a learning curve for full platform utilization and occasional documentation gaps.
- Sustained large-scale GPU cluster usage represents significant spend, with enterprise-tier and AI Factory pricing requiring direct sales engagement.
- The platform's published compliance and SLA documentation is limited in publicly available detail.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Gemini Search | Perplexity | Grok | ChatGPT | Google AI Mode |
|---|---|---|---|---|---|
Capability0/5 cited (0%) | |||||
I'm evaluating managed LLM inference platforms versus self-hosted GPU instances for a high-traffic workload — what are the key trade-offs and what should I look at? | |||||
Which serverless GPU platforms support model fine-tuning jobs, not just inference — what are the practical compute limits to know about? | |||||
What ML platforms handle dataset versioning alongside model versioning so you can reliably reproduce a training run from six months ago? | |||||
Which AI observability tools are best at detecting prompt injection attempts and guardrail violations in production LLM apps? | |||||
Which LLM orchestration frameworks handle long-running multi-agent workflows reliably — including surviving infrastructure restarts when a task takes hours? | |||||
Developer Experience0/5 cited (0%) | |||||
Which LLM observability platforms handle prompt versioning well — can you roll back to a previous prompt version and compare outputs side by side? | |||||
What ML experiment tracking tools handle multi-user collaboration well — so multiple data scientists can work on the same project without stepping on each other's runs? | |||||
Which AI infrastructure platforms support running the same orchestration logic locally against a mock LLM before deploying to production? | |||||
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure? | |||||
Looking for an LLM evaluation platform a solo engineer can get running in a day without deep ML expertise — what are my options? | |||||
Integrations & Ecosystem0/5 cited (0%) | |||||
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production? | |||||
Which AI/ML platforms have the best compliance story for SOC 2 and data residency — ensuring training data and model outputs stay in a specific region? | |||||
Which LLM observability platforms support exporting trace data to BigQuery or Snowflake for custom analysis? | |||||
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs? | |||||
What AI infrastructure platforms handle multi-model setups well — letting you switch between LLM providers and open-source models without rewriting application code? | |||||
Performance & Reliability0/5 cited (0%) | |||||
Which managed LLM inference platforms handle cold starts well — is there a way to keep a model warm without paying for idle GPU time? | |||||
Which LLM proxy gateway tools add observability without significant latency overhead — worth it for latency-sensitive production apps? | |||||
What LLM gateway or routing tools support automatic fallback when a primary model provider goes down in production? | |||||
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates? | |||||
What LLM infrastructure platforms give the best cost-to-latency balance for a high-throughput app doing 10,000 requests per hour? | |||||
Setup & First Run0/5 cited (0%) | |||||
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code? | |||||
What tools let you set up a RAG pipeline evaluation framework to measure retrieval quality and answer accuracy before going to production? | |||||
Which LLM orchestration frameworks are best for onboarding a software engineering team with no ML background — what's realistic for the first week? | |||||
What platforms can affordably serve a fine-tuned 7B parameter model with low latency for a production app without requiring a dedicated ML team? | |||||
What are the best ML experiment tracking tools for a team currently logging metrics to spreadsheets — which ones get you value fast with minimal setup? | |||||
Strengths
No clear strengths identified yet.
Gaps5
What tools support automatically running LLM evals on every pull request as part of a CI/CD pipeline before deploying prompt changes to production?
Competitors on 2 platforms
What are the best tools for debugging a multi-step AI agent pipeline — specifically tracing which tool call or LLM response caused a failure?
Competitors on 2 platforms
What monitoring tools should you set up for a production LLM pipeline to catch quality regressions like answer relevance drift or rising hallucination rates?
Competitors on 2 platforms
Which ML experiment tracking platforms integrate best with PyTorch training loops — minimal code changes to start logging runs?
Competitors on 2 platforms
What's the easiest LLM gateway to set up that adds caching, rate limiting, and cost tracking across multiple model providers without custom code?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | Braintrust | 14.4% | 39.8% | 0.8% | 0.0% | 13.6% | #8.2 | +0.23 |
| 2 | LangChain | 9.6% | 19.4% | 3.2% | 0.0% | 8.8% | #11.1 | +0.19 |
| 3 | Weights & Biases | 4.8% | 8.7% | 0.8% | 0.0% | 4.0% | #6.6 | +0.15 |
| 4 | Langfuse | 4.8% | 11.7% | 0.0% | 1.6% | 4.8% | #9.9 | +0.56 |
| 5 | Modal Labs | 4.0% | 8.7% | 1.6% | 3.2% | 4.0% | #8.0 | +0.00 |
| 6 | MLflow | 3.2% | 4.9% | 0.0% | 0.0% | 3.2% | #6.0 | +0.00 |
| 7 | Anyscale | 1.6% | 2.9% | 1.6% | 0.8% | 1.6% | #17.7 | +0.00 |
| 8 | BerriAI (LiteLLM) | 1.6% | 2.9% | 1.6% | 0.0% | 1.6% | #17.7 | +0.00 |
| 9 | Comet ML | 0.8% | 1.0% | 0.0% | 0.0% | 0.8% | #10.0 | +0.80 |
| 10 | Fireworks AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 11 | Helicone | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 12 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 13 | Together AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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