# Baseten AI visibility in LLM Inference & Serverless GPU

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

[Website](https://www.baseten.co/)

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


## Platforms

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

Rank: 3
Total brands: 10
Measured responses: 125
Presence percent: 16
Share of voice percent: 12.54237288135593
Average position: 6.243243243243243
Docs presence percent: 8
Blog presence percent: 3.2
Brand mention percent: 46.400000000000006


## Profile

Overview: Baseten is a San Francisco-based AI inference platform founded in 2019 by Tuhin Srivastava, Amir Haghighat, Philip Howes, and Pankaj Gupta. The company's Inference Stack combines modality-specific model runtimes, multi-cloud GPU orchestration across 10+ providers, and developer tooling to enable high-performance, low-latency production deployment of open-source and proprietary AI models. Product offerings include Dedicated Deployments for custom models, pre-optimized Model APIs, Baseten Training for fine-tuning, and the open-source Truss framework. Supported modalities span LLMs, transcription, image generation, text-to-speech, and embeddings. Notable customers include Cursor, Abridge, OpenEvidence, Notion, Clay, and Writer. Backed by $585M in total funding at a $5B valuation (January 2026), Baseten reported 10x revenue growth and 100x inference volume growth year-over-year.
Product summary: Baseten is an AI inference platform offering dedicated GPU deployments, pre-optimized Model APIs, multi-node training, and compound AI orchestration. Its proprietary Inference Stack—combining custom model runtimes, multi-cloud GPU management, and developer tooling—enables companies to run open-source and custom AI models in production at high throughput, low latency, and 99.99% uptime across cloud providers.


### Key capabilities

- High-performance dedicated GPU inference for open-source and custom AI models via the Baseten Inference Stack
- Pre-optimized Model APIs with OpenAI-compatible endpoints for instant model access
- Multi-cloud capacity management across 10+ providers with 99.99% uptime and automatic cross-cloud failover
- Truss open-source framework for packaging and serving ML models from any framework
- Baseten Chains for compound/multi-model AI orchestration with per-step GPU and autoscaling control
- Baseten Training for multi-node fine-tuning with one-click promotion to inference endpoints
- Baseten Embeddings Inference (BEI) with 2x+ higher throughput and 10%+ lower latency than alternatives
- Custom performance research: speculative decoding (EAGLE-3), custom kernels, advanced KV-cache techniques
- Self-hosted and hybrid deployment options for VPC-based or on-premises workloads
- Forward-deployed engineering support for enterprise customers



### Target users

- ML and AI engineers at hypergrowth AI startups building production inference pipelines
- Platform and infrastructure teams at mid-market and enterprise companies deploying custom or fine-tuned models
- AI product teams requiring low-latency, high-throughput inference for consumer-facing applications
- Healthcare and regulated-industry engineering teams needing HIPAA-compliant AI inference
- Research and applied AI teams running compound AI, RAG, or agentic workflows
- Founders and CTOs evaluating managed inference infrastructure to avoid building in-house GPU ops



### Key use cases

- Production LLM inference for custom and fine-tuned open-source models (Llama, DeepSeek, Qwen, GPT-OSS)
- Real-time speech-to-text and speaker diarization (e.g., medical transcription, voice agents)
- AI image generation and custom ComfyUI workflow serving
- Text-to-speech and real-time audio streaming for voice AI applications
- High-throughput embeddings for RAG pipelines and semantic search
- Compound AI and agentic workflow orchestration with heterogeneous GPU allocation
- Fine-tuning and continual learning with seamless model promotion to production
- Mission-critical, HIPAA-compliant AI inference for healthcare applications

Integrations ecosystem: Baseten integrates with a broad multi-cloud GPU ecosystem spanning AWS, Google Cloud, Azure, and 7+ additional providers, with dynamic workload failover across clouds. The platform holds a Strategic Collaboration Agreement (SCA) with AWS and a deep technical partnership with Google Cloud (leveraging A4/Blackwell VMs and Dynamic Workload Scheduler). NVIDIA is both an investor and hardware/software partner, supporting TensorRT-LLM and other optimization tooling. The open-source Truss framework (MIT-licensed, 1,100+ GitHub stars) enables packaging models from any ML framework (PyTorch, Hugging Face, etc.) into production APIs. Baseten exposes an OpenAI-compatible API, lowering switching costs. The Chains SDK supports compound AI and RAG orchestration. RBAC, observability dashboards, versioning, and webhook integrations are available for enterprise workflow integration.
Pricing summary: Baseten uses consumption-based pricing with no charges for idle time. Dedicated Deployments are billed per compute minute by GPU instance type, ranging from T4 to NVIDIA B200/H100; customers configure autoscaling including scale-to-zero. Model APIs are priced per million tokens (input + output), ranging approximately $0.20–$1.50/1M tokens depending on the model. Three plan tiers exist: Basic (pay-as-you-go, free credits for new accounts), Pro (volume discounts negotiable), and Enterprise (custom pricing, self-hosted option, starting ~$5,000/month on AWS Marketplace). Training jobs are billed per-minute on on-demand GPU compute. Discounts on compute are negotiable under Pro and Enterprise plans.
Review summary: Public user sentiment, sourced primarily from ProductHunt and investor commentary, is generally positive. Practitioners highlight Baseten's reliable model serving, smooth autoscaling, intuitive onboarding, and strong engineering support as key strengths. Customers from companies such as Bland AI, Not Diamond, and Toby cite it as core AI infrastructure with quick deployment and dependable throughput. Critical feedback is limited but includes isolated reports of slow billing support response times and the complexity of cost management with variable GPU pricing. Investor and analyst commentary (Premji Invest, Conviction, BOND) consistently praises Baseten's reliability focus, product depth, and enterprise stickiness.
Competitive positioning: Baseten positions as the mission-critical inference platform for hypergrowth AI companies and enterprises requiring maximum performance, reliability, and developer experience. It differentiates on: (1) proprietary inference research including custom kernels, speculative decoding (EAGLE-3), and a purpose-built Inference Stack; (2) multi-cloud infrastructure spanning 10+ providers with 99.99% uptime and instant cross-cloud failover; (3) no vendor lock-in via open runtimes and no lock-in on customer model weights; (4) enterprise compliance (SOC 2 Type II, HIPAA); and (5) forward-deployed engineering support for enterprise customers. Against Modal Labs (its closest peer), Baseten competes on enterprise readiness and compliance. Against Together AI and Fireworks AI, it competes on custom model support and white-glove support. Against raw GPU providers like RunPod, it competes on managed developer experience and reliability SLAs.
Limitations: Baseten is an infrastructure-first platform requiring ML engineering resources to integrate; not a turnkey solution for non-technical business teams. Pricing is usage-based and can be unpredictable, varying significantly by GPU tier (T4 through B200) and traffic patterns; enterprise contracts on AWS Marketplace start ~$5,000/month. GPU availability is primarily in the US and Europe, with limited regional coverage in other geographies (expansion ongoing). The platform's depth of configurability introduces operational complexity for smaller teams. Isolated user reviews cite occasional billing support responsiveness issues. As a managed cloud service, Baseten's multi-cloud cost savings are partially offset by its management margin versus raw GPU providers.


### Source urls

- https://www.baseten.co/
- https://www.businesswire.com/news/home/20260123035833/en/Baseten-Raises-$300M-at-a-$5B-Valuation-to-Power-a-Multi-Model-Future
- https://fortune.com/2025/09/05/exclusive-baseten-ai-inference-unicorn-raises-150-million-at-2-15-billion-valuation/
- https://siliconangle.com/2026/01/20/ai-inference-startup-baseten-hits-5b-valuation-300m-round-backed-nvidia/
- https://www.baseten.co/resources/customers/
- https://sacra.com/c/baseten/
- https://research.contrary.com/company/baseten
- https://www.baseten.co/pricing/
- https://artificialanalysis.ai/providers/baseten
- https://cloud.google.com/blog/products/ai-machine-learning/how-baseten-achieves-better-cost-performance-for-ai-inference
- https://github.com/basetenlabs
- https://www.producthunt.com/products/baseten/reviews
- https://www.premjiinvest.com/blog/inference-pricing-is-a-race-to-the-bottom-why-baseten-is-different

Research approved: 2026-05-06T17:53:53.754+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Patreon | Deployed OpenAI Whisper on Baseten for auto-generated closed captions for creator content, eliminating the need for custom GPU infrastructure management. | 440 engineer-hours saved annually; $600K cost savings; 70% reduction in GPU costs |
| Sully.ai | Transitioned its inference stack to open-source models on Baseten, addressing latency, cost, and quality challenges for clinical AI documentation and returning over 30M clinical minutes to healthcare. | 90% inference cost savings; 65% lower median latency |
| OpenEvidence | Used Baseten Embeddings Inference to power near-instant medical information retrieval for physicians, achieving ultra-low latency critical for clinical use cases. | 3x speed improvement; 160ms embedding latency |
| Zed Industries | Served AI code completions through Baseten's Inference Stack, improving response speed for the Zed code editor's AI features. | 2x faster code completions |
| Gamma | Leveraged Baseten for AI image generation powering presentation creation features, achieving a major improvement in generation throughput. | 5x faster image generation |
| Abridge | Used Baseten's inference infrastructure to scale real-time medical conversation transcription and clinical note generation safely across health systems. | More than 1 million clinical notes generated weekly for tens of thousands of clinicians |



### Reviews breakdown





### Review themes



#### Praised

- Fast and reliable model serving in production
- Smooth autoscaling with low ops overhead
- Easy path from model to live API
- Strong forward-deployed engineering support
- Intuitive onboarding and clear developer tooling
- Multi-cloud reliability and failover
- Consistent throughput under high load
- Cost-effective vs. building in-house GPU infrastructure



#### Criticized

- Unpredictable billing due to variable GPU pricing
- Requires ML engineering resources; not turnkey for non-technical teams
- Slow billing support responsiveness reported by some users
- Enterprise pricing can be high (~$5K+/month)
- Limited GPU region availability outside US and Europe




### Company facts

Founded year: 2019
Hq: San Francisco, CA, USA


#### Founders

- Tuhin Srivastava
- Amir Haghighat
- Philip Howes
- Pankaj Gupta

Employees range: Not available
Total funding: ~$585M
Valuation: $5B
Arr: Not available
Customer count: Not available
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 | 9 | 36 |
| perplexity | 4 | 16 |
| google-ai | 0 | 0 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | 2 | 1 |
| Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | 3 | 1 |
| 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? | 1 | 1 |
| Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | 2 | 1.5 |
| 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? | 1 | 2 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | 5 |
| 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 have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | 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 | 2 |
| Developer Experience | 5 | 4 |
| Integrations & Ecosystem | 5 | 3 |
| Performance & Reliability | 5 | 1 |
| Setup & First Run | 5 | 2 |



## 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: 1
Perplexity: 2
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: Not available
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: 7
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 |
| --- | --- |
| 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: 1
Chatgpt-search: Not available
Perplexity: 1
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: 4
Chatgpt-search: 2
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: Not available
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: Not available
Chatgpt-search: 3
Perplexity: Not available
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: 1
Chatgpt-search: 1
Perplexity: 1
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: 1
Chatgpt-search: 4
Perplexity: Not available
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: Not available
Chatgpt-search: 4
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: 2
Chatgpt-search: Not available
Perplexity: 1
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: 2
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: 1
Chatgpt-search: 31
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: Not available
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: Not available
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: Not available
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: 1
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://docs.baseten.co/deployment/deployments | Deployments - Baseten | docs.baseten.co | Not available | commercial | documentation | 8 | 8 |
| https://www.baseten.co/blog/how-leading-platforms-ensure-observability-for-llm-inference/ | How leading platforms ensure observability for LLM inference | baseten.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/0607136518567120a50c7363c84031a20c68f0bf.png | commercial | blog_post | 8 | 8 |
| https://www.baseten.co/blog/using-asynchronous-inference-in-production/ | Using asynchronous inference in production | baseten.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/0607136518567120a50c7363c84031a20c68f0bf.png | commercial | blog_post | 7 | 7 |
| https://docs.baseten.co/observability/metrics | Metrics | docs.baseten.co | Not available | commercial | documentation | 6 | 6 |
| https://docs.baseten.co/engines/performance-concepts/cloud-storage-deployment | Deploy from cloud storage - Baseten overview | docs.baseten.co | Not available | commercial | documentation | 6 | 6 |
| https://www.baseten.co/ | Inference Platform: Deploy AI models in production \| Baseten | baseten.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/0607136518567120a50c7363c84031a20c68f0bf.png | commercial | product_page | 6 | 6 |
| https://www.baseten.co/pricing/ | Cloud Pricing | baseten.co | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/0607136518567120a50c7363c84031a20c68f0bf.png | commercial | product_page | 6 | 6 |
| https://docs.baseten.co/inference/async | Async inference - Baseten | docs.baseten.co | Not available | commercial | documentation | 5 | 5 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | google-ai | Baseten * How it works: Uses an open-source packaging standard called _Truss_ . It streamlines deploying custom models or quantized weights with minimal boilerplate configuration files, managing auto-scaling and cold starts for you. |
| Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | google-ai | Baseten: Offers managed infrastructure for custom model packaging and deployment. |
| Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | google-ai | Baseten * Why it stands out: Baseten provides some of the deepest out-of-the-box observability for production LLMs among serverless/managed GPU providers. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| bing-copilot-search | Modal | ...st iteration on prompt templates and sampling parameters without redeploying, the best developer experiences today come from Modal, RunPod, and Fireworks AI. Modal is the most “Pythonic” and code-first, RunPod excels at container flexibility with quick... |
| google-ai-mode | Together AI | Together AI * The Experience: Together AI provides a robust web dashboard featuring a fully interactive Playground alongside standard OpenAI-compatible serverless APIs. |
| chatgpt-search | Modal | For your use case, I'd put Modal, RunPod Serverless, Replicate, and Baseten at the top of the shortlist, but for slightly different reasons. |
| chatgpt-search | RunPod | For your use case, I'd put Modal, RunPod Serverless, Replicate, and Baseten at the top of the shortlist, but for slightly different reasons. |
| chatgpt-search | Replicate | For your use case, I'd put Modal, RunPod Serverless, Replicate, and Baseten at the top of the shortlist, but for slightly different reasons. |
| perplexity | RunPod | For quick iteration on prompts and sampling settings, the best fit in the sources I could verify is Runpod Serverless with its vLLM worker. |
| google-ai | Modal | Modal * Why it excels for iteration: Modal allows you to define infrastructure and serverless functions directly in Python code using simple decorators (e.g., `@app.function(gpu="L40S")` ). Because your application logic lives as standard code ra... |
| 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. |



## Trend

Visibility delta: -4
Avg position delta: 1.3130106851037082
Citation count delta: -6