# Together AI AI visibility in LLM Inference & Serverless GPU

Canonical: https://devtune.ai/verticals/llm-inference-serverless-gpu/together-ai

[Website](https://www.together.ai/)

Latest AI responses: 2026-11-02
Prompts: 25
Runs: 5


## Platforms

- bing-copilot-search
- google-ai-mode
- chatgpt-search
- perplexity
- google-ai

Rank: 4
Total brands: 10
Measured responses: 125
Presence percent: 12.8
Share of voice percent: 10.847457627118644
Average position: 5.375
Docs presence percent: 4.8
Blog presence percent: 1.6
Brand mention percent: 40


## Profile

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.
Product summary: 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 capabilities

- 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)



### Target users

- AI-native startup founders and ML engineers building production LLM applications
- Enterprise engineering teams seeking open-source model inference with SLA guarantees
- AI/ML researchers needing scalable GPU compute for training and experimentation
- Teams fine-tuning open-source models on proprietary data to reduce cost or improve accuracy
- Generative media companies building video, image, and audio AI products at scale
- Platform and infrastructure teams migrating away from closed-source AI providers



### Key use cases

- 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

Integrations ecosystem: Together AI offers an OpenAI-compatible REST API and Python/TypeScript SDKs, enabling drop-in migration for teams already using OpenAI endpoints. The platform is available on AWS Marketplace. It hosts 200+ open-source models from providers including Meta (Llama), DeepSeek, Qwen (Alibaba), Mistral, Google, MiniMax, and OpenAI open-weight releases. Infrastructure partners include NVIDIA (GPU hardware), Weka (high-performance parallel storage), Vast.ai, and Tigris (object storage). Model ecosystem partners include Hugging Face (model compatibility), Cartesia (voice AI via Sonic integration), Black Forest Labs, MiniMax, Liquid AI, and Runware. Fine-tuning integrations include support for LoRA and full fine-tuning workflows with DPO. The platform also integrates with MongoDB and offers a code sandbox (via CodeSandbox acquisition) for AI agent development environments.
Pricing summary: 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.
Review summary: 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.
Competitive positioning: 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.
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.


### Source urls

- https://www.together.ai/
- https://www.together.ai/about-us
- https://www.together.ai/customers
- https://www.together.ai/customers/decagon
- https://www.together.ai/customers/hedra
- https://www.together.ai/pricing
- https://www.together.ai/blog/together-ai-announcing-305m-series-b
- https://docs.together.ai/intro
- https://www.prnewswire.com/news-releases/together-ai-raises-305m-series-b-to-scale-ai-acceleration-cloud-for-open-source-and-enterprise-ai-302380967.html
- https://sacra.com/c/together-ai/
- https://taptwicedigital.com/stats/together-ai
- https://www.datacenterdynamics.com/en/news/together-ai-seeks-1bn-in-funding-report/
- https://www.together.ai/blog/batch-inference-api-updates-2025
- https://www.g2.com/sellers/together-ai
- https://aws.amazon.com/marketplace/pp/prodview-3zxbbifggyplc

Research approved: 2026-05-06T17:53:54.043+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Decagon | 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. | 6× cost reduction per turn vs. GPT-5 mini; p95 latency <400ms |
| Hedra | 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. | 60% cost savings; 3× faster inference on Blackwell; 300× GPU usage growth |
| Salesforce | Salesforce AI Research deployed open-source model inference on Together AI, achieving significant latency and cost improvements compared to prior infrastructure. | ~33% cost savings; 2× latency reduction |
| Zomato | 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. | 2× CSAT score improvement |
| Cartesia | Runs real-time voice AI model serving on Together AI's GPU infrastructure, achieving production-grade latency suitable for conversational applications. | 90ms model latency |
| The Washington Post | Deployed Together AI for reliable, privacy-compliant LLM inference, achieving fast editorial AI response times while maintaining data sovereignty. | 2-second response time at production scale |



### Reviews breakdown





### Review themes



#### 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




### Company facts

Founded year: 2022
Hq: Menlo Park, CA, USA


#### Founders

- Vipul Ved Prakash
- Ce Zhang
- Chris Ré
- Tri Dao
- Percy Liang

Employees range: 150-250
Total funding: ~$534M
Valuation: $3.3B (Feb 2025)
Arr: ~$1B annualized (late 2025, per reports)
Customer count: 450,000+ registered developers
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| Modal | 33 | 125 | 26.400000000000002 | 8.275862068965518 |
| RunPod | 27 | 125 | 21.6 | 5.349206349206349 |
| Baseten | 20 | 125 | 16 | 6.243243243243243 |
| Together AI | 16 | 125 | 12.8 | 5.375 |
| Fireworks AI | 12 | 125 | 9.6 | 4.84 |
| Cerebrium | 10 | 125 | 8 | 5.2105263157894735 |
| Replicate | 10 | 125 | 8 | 12.761904761904763 |
| Beam | 6 | 125 | 4.8 | 11.181818181818182 |
| Lepton AI | 0 | 125 | 0 | Not available |
| Sference | 0 | 125 | 0 | Not available |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| bing-copilot-search | 0 | 0 |
| google-ai-mode | 7 | 28.000000000000004 |
| chatgpt-search | 6 | 24 |
| perplexity | 3 | 12 |
| google-ai | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | 1 | 1 |
| What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product? | 3 | 3 |
| What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature? | 1 | 4 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | 5 |
| Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | 4 |
| Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | 4 |
| Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | 3 |
| 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? | 3 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 3 |
| Developer Experience | 5 | 3 |
| Integrations & Ecosystem | 5 | 1 |
| Performance & Reliability | 5 | 2 |
| Setup & First Run | 5 | 4 |



## Prompt results

- Prompt text: Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 6
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Fireworks AI | 3 |
| Together AI | 6 |
| Modal | 10 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Baseten | 2 |



##### Google-ai




- Prompt text: Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 3
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Together AI | 3 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 7 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Fireworks AI | 1 |
| RunPod | 4 |



##### Google-ai




- Prompt text: Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 10
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 6 |
| Baseten | 7 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Cerebrium | 5 |
| Fireworks AI | 8 |
| Together AI | 10 |
| Replicate | 14 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 5 |



##### Google-ai




- Prompt text: Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Baseten | 1 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| RunPod | 3 |



##### Google-ai




- Prompt text: Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Perplexity





##### Google-ai




- Prompt text: Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Cerebrium | 7 |



##### Chatgpt-search





##### Perplexity





##### Google-ai




- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 3
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Fireworks AI | 2 |
| Together AI | 3 |
| Baseten | 4 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Baseten | 2 |
| Replicate | 3 |
| Modal | 4 |
| RunPod | 6 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Replicate | 3 |



##### Google-ai




- Prompt text: What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: 4
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 4 |
| Fireworks AI | 6 |



##### Google-ai




- Prompt text: What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fireworks AI | 7 |



##### Perplexity





##### Google-ai




- Prompt text: What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 1
Chatgpt-search: 1
Perplexity: 7
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Together AI | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Together AI | 1 |
| Baseten | 3 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 7 |



##### Google-ai




- Prompt text: Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 8
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Replicate | 4 |
| Together AI | 8 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Modal | 7 |
| Replicate | 9 |
| RunPod | 11 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Baseten | 1 |



##### Google-ai




- Prompt text: What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 8
Perplexity: 2
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 3 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Modal | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 3 |
| Baseten | 4 |
| Fireworks AI | 5 |
| Together AI | 8 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 2 |
| Fireworks AI | 4 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Modal | 2 |
| RunPod | 4 |
| Cerebrium | 6 |


- Prompt text: Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 2
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Together AI | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 2 |
| Replicate | 3 |
| Baseten | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Modal | 1 |


- Prompt text: Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Beam | 1 |
| Modal | 2 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Cerebrium | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 2 |
| Beam | 3 |
| Cerebrium | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 3 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 7 |


- Prompt text: Which inference platforms expose an API compatible with the standard chat completions format so switching providers requires minimal code changes?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Perplexity





##### Google-ai




- Prompt text: What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Baseten | 2 |
| Cerebrium | 3 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| Baseten | 1 |



##### Google-ai




- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 7
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 1 |



##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Modal | 11 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Baseten | 2 |
| Replicate | 6 |
| Together AI | 7 |
| Fireworks AI | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 4 |
| Modal | 7 |



##### Google-ai




- Prompt text: Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| RunPod | 2 |
| Replicate | 4 |
| Modal | 5 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Replicate | 2 |
| Modal | 15 |
| Baseten | 31 |
| Beam | 33 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |



##### Google-ai

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 2 |


- Prompt text: What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 5
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Fireworks AI | 1 |
| Together AI | 5 |



##### Chatgpt-search





##### Perplexity





##### Google-ai




- Prompt text: Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 2
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| RunPod | 1 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Together AI | 2 |
| Fireworks AI | 3 |
| Modal | 4 |



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Cerebrium | 3 |
| Fireworks AI | 5 |



##### Google-ai




- Prompt text: Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: 1
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Together AI | 1 |



##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 3 |
| Modal | 5 |



##### Google-ai




- Prompt text: What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 3 |



##### Google-ai-mode





##### Chatgpt-search





##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Replicate | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| RunPod | 4 |


- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Modal | 3 |



##### Perplexity





##### Google-ai




- Prompt text: I'm looking for an inference platform that supports custom CUDA kernels and speculative decoding — what are my options for a performance-critical chatbot?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Perplexity





##### Google-ai




- Prompt text: 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?


#### Brand position by platform

Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: Not available
Perplexity: Not available
Google-ai: Not available



#### Platform rows



##### Bing-copilot-search





##### Google-ai-mode

| Display name | Position |
| --- | --- |
| Cerebrium | 1 |
| Beam | 2 |



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Beam | 1 |
| Cerebrium | 2 |
| RunPod | 9 |



##### Perplexity

| Display name | Position |
| --- | --- |
| Modal | 1 |
| RunPod | 3 |
| Beam | 6 |
| Cerebrium | 7 |



##### Google-ai







## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://www.together.ai/serverless-inference | Serverless Inference \| Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | product_page | 17 | 17 |
| https://www.together.ai/dedicated-model-inference | Dedicated Model Inference \| Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | product_page | 14 | 14 |
| https://docs.together.ai/docs/fine-tuning/deployment | Deploy a fine-tuned model - Together AI docs | docs.together.ai | Not available | commercial | documentation | 7 | 7 |
| https://www.together.ai/blog/autoscaling-endpoints-for-llm-inference | Autoscaling endpoints for LLM inference - Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | blog_post | 7 | 7 |
| https://www.together.ai/pricing | Pricing \| Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | product_page | 6 | 6 |
| https://docs.together.ai/docs/dedicated-endpoints/custom-models | Upload a fine-tuned model - Together AI docs | docs.together.ai | Not available | commercial | documentation | 5 | 5 |
| https://www.together.ai/blog/flash-decoding-for-long-context-inference | Flash-Decoding for long-context inference | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | blog_post | 4 | 4 |
| https://www.together.ai/models | Build with leading AI models \| Together AI | together.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/7d2200c86998b4f449b5c5c47446b0578fc47a49.png | commercial | product_page | 4 | 4 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | google-ai | Together AI (Best End-to-End Pipeline) If you haven't fine-tuned your model yet, or if you already have your weights/adapters ready, Together AI offers one of the smoothest unified workflows for solo developers. |
| What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases? | google-ai | Together AI * Why it fits: Similar to Fireworks, Together AI offers a broad catalog of open-source models with strong support for long-context variants. |
| Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | google-ai | Together AI (Dedicated Endpoints): Widely used for enterprise fine-tuning and inference. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| bing-copilot-search | Beam | Leading options include Beam, Modal, RunPod, Baseten, Replicate, Northflank, Koyeb, and Spheron. |
| bing-copilot-search | Modal | Leading options include Beam, Modal, RunPod, Baseten, Replicate, Northflank, Koyeb, and Spheron. |
| google-ai-mode | Modal | ...enerally scale across two operational tiers: \| Platform Feature / Metric \| Tier 1: Advanced Native Batch + Real-Time (e.g., Modal) \| Tier 2: Concurrency/Queue-Driven Batch + Real-Time (e.g., Beam, Cerebrium) \| \|---------------------------\|---------... |
| chatgpt-search | Modal | The strongest options are Modal, Runpod, and Beam, with some additional platforms worth considering depending on whether you want managed model APIs or control over your own inference containers. |
| chatgpt-search | RunPod | The strongest options are Modal, Runpod, and Beam, with some additional platforms worth considering depending on whether you want managed model APIs or control over your own inference containers. |
| chatgpt-search | Beam | The strongest options are Modal, Runpod, and Beam, with some additional platforms worth considering depending on whether you want managed model APIs or control over your own inference containers. |
| perplexity | RunPod | Based on the available results, Runpod Serverless and Modal support both offline batch inference and real-time API serving: - Runpod Serverless offers managed batch jobs for bulk, offline requests alongside the same queue-based endpoint’s re... |
| perplexity | Modal | Based on the available results, Runpod Serverless and Modal support both offline batch inference and real-time API serving: - Runpod Serverless offers managed batch jobs for bulk, offline requests alongside the same queue-based endpoint’s re... |
| google-ai | Modal | Modal * Overview: Modal is a code-first serverless platform built natively on a multi-cloud GPU pool. |
| bing-copilot-search | Modal | Leading options include Modal, RunPod, Beam, Replicate, and enterprise clouds like AWS SageMaker and Google Cloud’s llm‑d stack. ## 🔑 Key Platforms for Multimodal Inference \| Platform \| GPU Support... |
| google-ai-mode | Modal | Modal * Overview: Modal is a code-first, highly optimized serverless platform built specifically for heavy AI and ML workloads. |
| chatgpt-search | Modal | ## Platforms worth evaluating 1. Modal Best for custom VLM deployment - Deploy your own models and serving stacks using vLLM, SGLang, or custom code. |



## Trend

Visibility delta: 0
Avg position delta: -5.967105263157896
Citation count delta: -6