# Modal AI visibility in LLM Inference & Serverless GPU

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

[Website](https://modal.com/)

Updated: 2026-09-29T01:48:56.712604+00:00
Prompts: 25
Runs: 5


## Platforms

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

Rank: 2
Total brands: 10
Measured responses: 125
Presence percent: 19.2
Share of voice percent: 25.157232704402517
Average position: 3.7
Docs presence percent: 4
Blog presence percent: 4
Brand mention percent: 0


## Profile

Overview: Modal Labs is a New York-based AI infrastructure company founded in 2021 by Erik Bernhardsson and Akshat Bubna. The platform provides serverless GPU compute for machine learning workloads, enabling Python developers to deploy inference endpoints, run large-scale batch jobs, fine-tune open-source models, and execute secure AI-agent sandboxes using simple function decorators—without YAML, Kubernetes, or manual infrastructure management. Modal operates a multi-cloud GPU capacity pool spanning NVIDIA B200, H200, H100, and A100 hardware, with a proprietary container runtime delivering sub-second cold starts. Pricing is fully consumption-based, billed per second of actual compute usage, with automatic scale-to-zero when idle. Customers include Lovable, Ramp, Substack, Harvey AI, Mistral, Suno, Cognition, and Allen AI. Modal raised an $87M Series B in September 2025 at a $1.1B valuation led by Lux Capital, reaching unicorn status.
Product summary: Modal is a serverless AI infrastructure platform that turns any Python function into an autoscaling cloud workload with GPU acceleration. Developers decorate Python functions with @app.function(), specify container environments and hardware in code, and invoke workloads via .remote()—Modal handles container builds, scheduling, autoscaling, and logging automatically. Core products include Modal Inference (low-latency LLM and model serving), Modal Training (single- and multi-node GPU fine-tuning), Modal Sandboxes (secure ephemeral environments for AI-generated code execution), Modal Batch (massively parallel batch processing), and Modal Notebooks (collaborative GPU-backed notebooks). The underlying platform includes a custom file system, container runtime, scheduler, and image builder engineered for AI workloads.


### Key capabilities

- Sub-second container cold starts (custom container runtime, claimed 100x faster than Docker)
- Serverless GPU compute with elastic autoscaling to zero
- Access to NVIDIA B200, H200, H100, A100, L40S, A10, L4, T4 GPUs across multi-cloud capacity pool
- Python-native SDK with decorator-based function definition—no YAML or config files
- Secure ephemeral Sandboxes for executing LLM-generated or untrusted code
- Multi-node RDMA-connected GPU clusters for distributed training
- Memory snapshots to reduce LLM cold start times by up to 10x
- Built-in distributed storage: Volumes, Dicts, Queues, and cloud bucket mounts
- Per-second consumption billing with scale-to-zero cost model
- SOC 2 compliance and HIPAA compatibility with gVisor-based container isolation



### Target users

- ML engineers deploying and scaling AI models in production
- AI researchers running fine-tuning and training experiments
- Python developers building GPU-intensive backend applications
- AI product startups needing elastic GPU compute without DevOps overhead
- Data science teams running large-scale batch and data processing jobs
- Coding agent and AI-agent platform teams requiring secure sandboxed execution



### Key use cases

- LLM inference endpoint deployment and autoscaling
- Open-source model fine-tuning and RL training pipelines
- Batch processing and parallel data pipelines at scale
- Secure AI agent code sandboxing (e.g., coding agents, MCP servers)
- Audio transcription and speech processing at scale (e.g., Whisper)
- Image and video generation inference
- Computational biology and scientific HPC workloads
- CI/CD pipelines with GPU-accelerated testing

Integrations ecosystem: Modal integrates with major ML inference frameworks including vLLM, SGLang, and TensorRT-LLM for OpenAI-compatible LLM serving. It supports Hugging Face Hub for model weight downloads and Weights & Biases for experiment logging. Storage integrations include cloud bucket mounts for AWS S3, GCP, and Azure. Observability integrations include Datadog and OpenTelemetry providers. Identity and access management integrations cover Okta SSO and custom SAML. Modal is transactable via AWS and GCP cloud marketplaces for committed spend. The platform supports LangGraph for AI agent workflows, FastMCP for MCP server deployments, and Unsloth for efficient LLM fine-tuning. The Python SDK is compatible with any pip-installable ML library, allowing teams to bring their own toolchain.
Pricing summary: Modal uses a purely consumption-based pricing model with no idle costs—charges accrue only during active compute time, billed per second. GPU rates range from $0.000164/sec for NVIDIA T4 to $0.001736/sec for NVIDIA B200. CPU is $0.0000131/core/sec and memory is $0.00000222/GiB/sec. Three plan tiers exist: Starter ($0/month platform fee, includes $30/month in free compute credits, up to 3 seats, 10 GPU concurrency); Team ($250/month, includes $100/month free credits, unlimited seats, 50 GPU concurrency, custom domains, static IP, deployment rollbacks); Enterprise (custom pricing, higher GPU concurrency, HIPAA, Okta SSO, audit logs, embedded ML engineering services). Startup credit grants of up to $25K and academic grants of up to $10K are available. AWS and GCP marketplace transactability allows use of committed cloud spend.
Review summary: Developer sentiment for Modal is strongly positive across social media and community forums, with ML engineers from companies including Tesla, Hugging Face, Harvey, and the Linux Foundation publicly praising the platform's developer experience, fast cold starts, Python-native workflow, and documentation quality. Common praise themes include the Vercel-like simplicity of deploying GPU workloads and the generous free tier. Criticisms are limited but center on less fine-grained infrastructure customization compared to traditional cloud providers and the platform's Python-centric nature. Formal review coverage is sparse: Capterra lists a single review (4.0/5), and no verified G2 or Gartner Peer Insights listing was found as of research date.
Competitive positioning: Modal positions itself as developer-first, Python-native serverless GPU infrastructure, differentiated by sub-second cold starts, zero-YAML configuration, and per-second consumption-based pricing. Unlike raw GPU rental providers such as RunPod, Modal abstracts infrastructure complexity while preserving full ML flexibility. Unlike managed LLM API providers such as Fireworks AI or Together AI, Modal supports the full ML lifecycle—inference, fine-tuning, batch processing, and secure code sandboxes—within a single unified platform. Its closest architectural competitors are Baseten and Beam, though Modal's custom-built container runtime (claimed 100x faster than Docker) and multi-cloud capacity pool are cited as differentiators. Modal targets ML engineers who want Vercel-like developer experience for AI workloads without vendor lock-in on models.
Limitations: Modal is primarily Python-centric, limiting native adoption by polyglot development teams (JavaScript/Go SDKs exist for calling Modal but not for defining Functions). Infrastructure customization is less granular than traditional cloud providers, which can frustrate teams with highly specific networking or hardware requirements. The platform is not a plug-and-play managed inference API—users must write and maintain their own serving code, making it less suitable for non-technical teams. Per-GPU-hour effective costs can be higher than raw GPU rental providers like RunPod for sustained, high-utilization workloads where serverless economics offer less advantage. Vendor lock-in risk exists due to Modal-specific SDK primitives. No proprietary model catalog is offered.


### Source urls

- https://modal.com/
- https://modal.com/pricing
- https://modal.com/company
- https://modal.com/customers
- https://modal.com/blog/announcing-our-series-b
- https://modal.com/blog/lovable-case-study
- https://modal.com/blog/ramp-case-study
- https://modal.com/blog/substack-case-study
- https://modal.com/docs/guide
- https://techcrunch.com/2026/02/11/ai-inference-startup-modal-labs-in-talks-to-raise-at-2-5b-valuation-sources-say/
- https://sacra.com/c/modal-labs/
- https://tracxn.com/d/companies/modal/__tHK2ShUcB0Q1o6j-hbJ-xcZMxDsw0P3kCJ85veVeYjU
- https://www.capterra.com/p/10014407/Modal/
- https://siliconangle.com/2025/09/29/modal-labs-raises-80m-simplify-cloud-ai-infrastructure-programmable-building-blocks/

Reviewed at: 2026-05-06T17:53:54.136+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Ramp | Used Modal to fine-tune LLMs for automated receipt processing, enabling parallel training of hundreds of candidate models. Reduced receipts requiring manual intervention and cut infrastructure costs significantly versus major LLM providers. | 34% reduction in receipts requiring manual intervention; infrastructure ~79% cheaper than OpenAI |
| Lovable | Migrated code sandbox infrastructure to Modal Sandboxes ahead of a major promotional weekend with Anthropic, OpenAI, and Google. Modal handled a 2.5–3x surge in concurrent sessions and ran over 1 million sandboxes during the 48-hour event with zero on-call pages. Reduced sandbox | 1M+ sandboxes run; 250,000 apps created in 48 hours; 20,000 peak concurrent sandboxes |
| Quora | Offloaded code sandbox infrastructure to Modal, eliminating the engineering overhead of building and maintaining a distributed sandbox system in-house. | Saving ~2 engineers' worth of ongoing engineering time |
| Substack | Migrated training and inference pipelines from AWS SageMaker to Modal, dramatically reducing developer friction and container startup times from 5+ minutes to near-instant for model iteration and deployment. | Not available |



### Reviews breakdown

| Platform | Score | Score max | Review count | Url |
| --- | --- | --- | --- | --- |
| Capterra | 4 | 5 | 1 | https://www.capterra.com/p/10014407/Modal/ |



### Review themes



#### Praised

- Sub-second cold starts and fast container scaling
- Python-native SDK with minimal boilerplate (no YAML/config)
- Excellent developer experience compared to SageMaker and AWS Lambda
- Generous free tier ($30/month compute credits)
- High-quality documentation and example library
- Seamless local-to-cloud development workflow
- Elastic GPU autoscaling with scale-to-zero cost savings
- Active and responsive Slack community



#### Criticized

- Limited fine-grained infrastructure customization vs. traditional cloud providers
- Python-only function definitions (less suited for polyglot teams)
- Not a plug-and-play solution for non-technical business teams
- Higher effective per-GPU cost than raw GPU rental for sustained workloads




### Company facts

Founded year: 2021
Hq: New York City, USA


#### Founders

- Erik Bernhardsson
- Akshat Bubna

Employees range: 100-150
Total funding: $111M
Valuation: $1.1B
Arr: ~$50M
Customer count: Not available
Status: Private


Readiness: Not available


## Ranking

| Display name | Pair count | Total pairs | Presence percent | Avg position |
| --- | --- | --- | --- | --- |
| RunPod | 24 | 125 | 19.2 | 3.6 |
| Modal | 24 | 125 | 19.2 | 3.7 |
| Baseten | 15 | 125 | 12 | 2.761904761904762 |
| Fireworks AI | 14 | 125 | 11.200000000000001 | 3.235294117647059 |
| Replicate | 12 | 125 | 9.6 | 3.1666666666666665 |
| Together AI | 11 | 125 | 8.799999999999999 | 3.0526315789473686 |
| Beam | 4 | 125 | 3.2 | 3.857142857142857 |
| Cerebrium | 2 | 125 | 1.6 | 5 |
| Lepton AI | 0 | 125 | 0 | Not available |
| Sference | 0 | 125 | 0 | Not available |



## Platform breakdown

| Platform | Prompt count | Presence rate |
| --- | --- | --- |
| perplexity | 6 | 24 |
| bing-copilot-search | 5 | 20 |
| google-ai-mode | 1 | 4 |
| chatgpt-search | 9 | 36 |
| google-ai | 3 | 12 |



## 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 |
| Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | 1 | 1 |
| Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | 3 | 1.3333333333333333 |
| Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | 4 | 1.75 |
| What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps? | 1 | 4 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | 2 |
| Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | 2 |
| 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? | 2 |
| Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | 2 |
| Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | 2 |



## Topic scores

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



## Prompt results

- 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

Perplexity: 1
Bing-copilot-search: 2
Google-ai-mode: Not available
Chatgpt-search: 1
Google-ai: Not available



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 2 |



##### Google-ai-mode





##### Chatgpt-search

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

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



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Replicate | 1 |



##### Google-ai

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


- 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Google-ai




- 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

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



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Together AI | 2 |
| Baseten | 3 |
| Replicate | 6 |



##### 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 1 |
| Replicate | 7 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

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



##### 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

Perplexity: Not available
Bing-copilot-search: 6
Google-ai-mode: Not available
Chatgpt-search: 3
Google-ai: 5



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Replicate | 1 |
| RunPod | 4 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| RunPod | 3 |
| Modal | 6 |



##### Google-ai-mode





##### Chatgpt-search

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



##### Google-ai

| Display name | Position |
| --- | --- |
| Modal | 5 |


- 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### 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

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



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Beam | 1 |



##### Google-ai-mode





##### Chatgpt-search





##### 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

Perplexity: 4
Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 2
Google-ai: Not available



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 4 |
| Cerebrium | 7 |



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Beam | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Modal | 2 |
| Cerebrium | 3 |
| Together AI | 4 |
| Fireworks AI | 5 |



##### Google-ai

| Display name | Position |
| --- | --- |
| Fireworks AI | 1 |
| Beam | 2 |


- 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

Perplexity: 2
Bing-copilot-search: Not available
Google-ai-mode: Not available
Chatgpt-search: 1
Google-ai: Not available



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

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



##### Google-ai




- 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

Perplexity: 1
Bing-copilot-search: 1
Google-ai-mode: Not available
Chatgpt-search: 4
Google-ai: Not available



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search

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



##### Google-ai-mode





##### Chatgpt-search

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



##### Google-ai

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

Perplexity: 3
Bing-copilot-search: 2
Google-ai-mode: Not available
Chatgpt-search: 1
Google-ai: 1



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search

| Display name | Position |
| --- | --- |
| Modal | 2 |



##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 1 |
| Beam | 2 |
| RunPod | 3 |
| Replicate | 4 |
| Fireworks AI | 5 |



##### Google-ai

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


- 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 1 |
| Fireworks AI | 2 |
| Baseten | 5 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Together AI | 2 |
| Baseten | 3 |
| Modal | 4 |
| RunPod | 5 |
| Fireworks AI | 6 |



##### 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 1 |
| RunPod | 3 |
| Baseten | 4 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

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



##### Google-ai

| Display name | Position |
| --- | --- |
| RunPod | 9 |


- 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 3 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Google-ai

| Display name | Position |
| --- | --- |
| RunPod | 2 |


- 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode

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



##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Fireworks AI | 4 |
| Together AI | 5 |



##### 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### 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

Perplexity: 4
Bing-copilot-search: 1
Google-ai-mode: Not available
Chatgpt-search: Not available
Google-ai: Not available



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search

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



##### Google-ai-mode





##### Chatgpt-search





##### Google-ai




- 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| Together AI | 1 |
| Fireworks AI | 2 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Modal | 4 |



##### Google-ai




- 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

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



#### Platform rows



##### Perplexity

| Display name | Position |
| --- | --- |
| RunPod | 3 |



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

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



##### 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Google-ai

| Display name | Position |
| --- | --- |
| Baseten | 1 |
| Fireworks AI | 2 |


- 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

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



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

| Display name | Position |
| --- | --- |
| Replicate | 1 |
| RunPod | 2 |
| Baseten | 3 |
| Modal | 5 |



##### Google-ai

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


- 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

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



#### Platform rows



##### Perplexity

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



##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search

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



##### 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

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



#### Platform rows



##### Perplexity





##### Bing-copilot-search





##### Google-ai-mode





##### Chatgpt-search





##### Google-ai

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





## Top sources

| Url | Title | Domain | Logo url | Source vertical | Content type | Citation count | Last30d count |
| --- | --- | --- | --- | --- | --- | --- | --- |
| https://modal.com/resources/best-serverless-gpu-platforms-inference | Best Serverless GPU Platforms for Inference in 2026 - Modal | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | comparison | 37 | 37 |
| https://modal.com/products/inference | Products - Inference - Modal | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | documentation | 11 | 11 |
| https://modal.com/resources/best-serverless-gpu-platforms-fine-tuning | Best Serverless GPU Platforms for Fine-Tuning in 2026 - Modal | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | listicle | 10 | 10 |
| https://modal.com/ | Modal: High-performance AI infrastructure | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | documentation | 5 | 5 |
| https://modal.com/blog/serverless-gpu-article | Top 5 serverless GPU providers | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | blog_post | 5 | 5 |
| https://modal.com/blog/truly-serverless-gpus | How we achieved truly serverless GPUs | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | blog_post | 2 | 2 |
| https://modal.com/docs/guide/cold-start | Cold start performance \| Modal Docs | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | documentation | 2 | 2 |
| https://modal.com/resources/best-gpu-platforms-bursty-spiky-ai-workloads | Best GPU Platforms for Bursty and Spiky AI Workloads in 2026 - Modal | modal.com | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/ab5145456cf0340a33c3cbcbefa2ff6e288c455c.png | commercial | listicle | 2 | 2 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | bing-copilot-search | Leading options include OneInfer, Together AI, Fireworks AI, Baseten, Modal, Replicate, RunPod, DeepInfra, Groq, and Cerebras. These platforms differ in their GPU infrastructure, latency guarantees, and support for custom deployments.oneinfer.ai+3oneinfer.ai. |
| What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box? | bing-copilot-search | If you want broader flexibility and polished developer tooling, _Modal_ is the most developer‑friendly, while _RunPod_ offers the cheapest scaling and container freedom.gputracker.dev+3gputracker.dev. |
| Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | bing-copilot-search | The platforms that give engineering teams the strongest _out-of-the-box visibility_ into per-request latency and token throughput are Together AI, Modal, RunPod, and Cloudflare Workers AI. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| perplexity | Baseten | Baseten — Offers self-hosted and hybrid BYOC/VPC deployment, making it a candidate when you need to run models in your own environment. |
| chatgpt-search | Together AI | ...d, datacenter, Kubernetes, or NVIDIA infrastructure \| Excellent for highly regulated / security-sensitive teams \| \| Together AI \| Yes — HF or S3 custom/fine-tuned models \| Yes — single-tenant isolated GPUs; dedicated endpoints \| Together-... |
| chatgpt-search | Baseten | ...es — single-tenant isolated GPUs; dedicated endpoints \| Together-managed infrastructure \| Strong managed option \| \| Baseten \| Yes — custom/fine-tuned models \| Yes — dedicated clusters or self-hosted in your VPC \| Baseten, your VPC, or h... |
| perplexity | Together AI | For a backend engineer without MLOps experience, the easiest choices are Together AI and Hugging Face Inference Endpoints—but they suit slightly different needs. |
| chatgpt-search | Replicate | ...\| Hugging Face Inference Endpoints \| ⭐⭐⭐⭐⭐ \| Excellent \| Very little \| Easiest general-purpose choice \| \| Replicate \| ⭐⭐⭐⭐ \| Good \| Little \| Custom models / unusual runtimes \| \| RunPod Serverless \| ⭐⭐⭐⭐ \| Excellent \| So... |
| perplexity | Replicate | [1] [2] Replicate is another option, but it isn’t quite “no config” for a custom model: you package it with Cog, which requires a `cog.yaml` and prediction code before deploying. |
| bing-copilot-search | Beam | For a small startup, the easiest serverless platforms to deploy a custom open‑source LLM without touching Kubernetes, Docker, or YAML are Beam, GMI Cloud, Hugging Face Inference Endpoints, and SiliconFlow. |
| perplexity | RunPod | Among the options in the results, RunPod Serverless is a strong first platform to benchmark: it bills by worker execution time, per second, and lists GPU rates from about $0.58/hour for smaller cards, with H100 listed at $4.79/hour. |
| perplexity | Fireworks AI | If you want a managed API and predictable token pricing, Fireworks AI is a useful benchmark: it lists $0.90 per million tokens for models above 16B parameters, with cached-input pricing available. |
| chatgpt-search | Together AI | ...tandard \| Cheap open-weight models, especially long-context MoE \| Very compelling for lowest raw token cost \| \| Together AI \| Per-token serverless + dedicated endpoints \| Broad model selection and mature production serving \| Strong price/pe... |
| chatgpt-search | Baseten | ...ference pipelines and excellent autoscaling \| Excellent engineering/product choice; compute price competitive \| \| Baseten \| Per-token model APIs or dedicated GPU \| Production inference, autoscaling, enterprise features \| Good, but usually n... |
| perplexity | RunPod | Runpod Serverless provides useful built-in operational visibility—execution-time and queue-delay percentiles, cold-start metrics, and request counts—but its dashboard is less token-specific. |



## Trend

Visibility delta: 2.057142857142857
Avg position delta: 0.6000000000000001
Citation count delta: 10