# Cerebrium AI visibility in LLM Inference & Serverless GPU

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

[Website](https://cerebrium.ai/)

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


## Platforms

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

Rank: 6
Total brands: 10
Measured responses: 125
Presence percent: 8
Share of voice percent: 6.440677966101695
Average position: 5.2105263157894735
Docs presence percent: 0.8
Blog presence percent: 0
Brand mention percent: 12.8


## Profile

Overview: Cerebrium is a New York-based serverless AI infrastructure platform founded in 2021 and backed by Gradient Ventures, Y Combinator, and Authentic Ventures. The platform enables engineering teams to deploy, scale, and operate multimodal AI workloads—including LLMs, voice agents, video generation, and digital avatars—without managing servers or DevOps infrastructure. Cerebrium's core technical differentiator is its proprietary container runtime with GPU and memory snapshotting, delivering cold starts of 2–4 seconds across 12+ GPU types from T4 to B200. It charges per second of actual compute usage, supports custom Dockerfiles without code rewrites, and provides native multi-region deployment, OpenTelemetry observability, and enterprise compliance certifications (SOC 2, HIPAA, GDPR, ISO 27001). Notable customers include Tavus, Deepgram, Vapi, and Resemble AI.
Product summary: Cerebrium is a managed serverless GPU platform for real-time, multimodal AI applications. It allows developers to deploy any AI workload—LLMs, voice pipelines, video models, or custom containers—using a simple CLI or Dockerfile, with automatic autoscaling, per-second billing, and built-in observability across multiple cloud regions.


### Key capabilities

- Serverless GPU compute with 2–4 second cold starts via memory and GPU snapshotting
- 12+ GPU types (T4, L4, A10, L40s, A100 40/80GB, H100, H200, B200) with per-second billing
- Bring-your-own-Dockerfile deployment with no SDK rewrites or decorators required
- Elastic autoscaling from zero to thousands of concurrent GPU instances
- Multi-region deployments (US, EU, Asia) with data residency and sovereignty controls
- Full observability: real-time logs, metrics, scaling events, and native OpenTelemetry integration
- SOC 2, HIPAA, GDPR, and ISO 27001 compliance with gVisor container isolation
- WebSocket, streaming, async, and REST endpoint types with concurrency and batching controls
- CI/CD pipeline integration with gradual rollouts and versioned deployments
- 99.999% uptime with multi-region failover and automatic traffic rerouting



### Target users

- ML engineers building real-time voice and video AI applications
- AI startups deploying multimodal inference pipelines without dedicated DevOps
- Enterprise teams requiring compliant (HIPAA/GDPR/SOC 2) GPU infrastructure
- Developers fine-tuning open-source LLMs at scale
- Companies with bursty, unpredictable AI compute workloads
- Platform teams building AI-native SaaS products on serverless infrastructure



### Key use cases

- Real-time voice agent infrastructure (sub-500ms end-to-end latency pipelines)
- LLM inference serving (custom and open-source models at scale)
- LLM fine-tuning on multi-GPU clusters (H100, H200)
- Generative video and digital avatar rendering
- Image generation and computer vision inference
- Large-scale batch data processing and ETL pipelines
- Multimodal AI application pipelines combining ASR, LLMs, and TTS
- Regulated-industry AI deployments requiring HIPAA/GDPR compliance

Integrations ecosystem: Cerebrium integrates natively with LiveKit, Vapi, Deepgram, Tavus, Resemble AI, and Rime Labs for real-time voice and video pipelines. It supports OpenTelemetry for metrics export into existing monitoring stacks, and custom private Docker registries for container images. Framework support includes PyTorch, TensorFlow, vLLM, and Stable Diffusion XL. Multi-cloud routing across AWS, GCP, and regional providers (us-east-1, eu-west-2, eu-north-1, ap-south-1) is built-in. REST, WebSocket, streaming, and async endpoints are available via the platform API.
Pricing summary: Per-second, usage-based billing for all compute. GPU rates range from $0.000164/s (T4) to $0.00167/s (B200), with A10 at $0.000306/s and H100 at $0.000944/s. Memory is billed at $0.00000222/GB/s; CPU at $0.00000655/vCPU/s. Storage costs $0.05/GB/month (first 100 GB free). Three plan tiers: Hobby (free base + compute, up to 3 apps, 5 concurrent GPUs), Standard ($100/month + compute, unlimited apps, 30 concurrent GPUs, custom domains), and Enterprise (custom pricing, unlimited concurrency, dedicated Slack, volume discounts, ML engineering services). Volume discounts and capacity guarantees (e.g., up to 50 H100s with $10,000/month minimum spend) are available for enterprise deployments.
Review summary: No verified third-party review scores exist on G2 (profile unclaimed, 0 reviews) or Gartner Peer Insights as of May 2026. Community sentiment on Product Hunt and Hacker News is positive, with developers praising the speed and simplicity of GPU deployment. Published case studies from Tavus, Creatium, DistilLabs, and bitHuman document strong customer satisfaction around cold-start performance, developer experience, and support responsiveness.
Competitive positioning: Cerebrium positions itself as a developer-first, multimodal serverless GPU platform purpose-built for real-time AI workloads—voice agents, LLMs, video generation, and digital avatars—rather than a general-purpose GPU marketplace or a model-API aggregator. Its key differentiators are sub-4-second cold starts enabled by a proprietary container runtime and GPU/memory snapshotting, bring-your-own-Dockerfile deployment (no SDK rewrites), per-second billing, and a compliance stack (SOC 2, HIPAA, GDPR, ISO 27001) that supports enterprise data-residency requirements. Against Modal Labs and Beam, Cerebrium emphasizes multimodal/voice-video specialization and deeper compliance. Against Baseten and Replicate, it highlights full Dockerfile control and broader GPU diversity. Against RunPod and Together AI, it stresses managed orchestration and 99.999% uptime SLAs over raw GPU access or hosted-model APIs.
Limitations: AWS and GCP cloud credits cannot be applied to Cerebrium usage, limiting its appeal for teams with existing hyperscaler commitments. The platform is optimized for bursty and variable workloads; always-on, high-utilization workloads may be more cost-effective on reserved instances. The G2 profile is unclaimed with zero published reviews, limiting third-party social proof. With approximately 13 employees as of early 2026, enterprise feature requests and dedicated SLA support may be constrained relative to larger vendors. Guaranteed capacity commitments require a minimum monthly spend (e.g., $10,000/month for H100 guarantees).


### Source urls

- https://cerebrium.ai/
- https://cerebrium.ai/pricing
- https://cerebrium.ai/about
- https://cerebrium.ai/blog/cerebrium-raises-8-5m-led-by-gradient-to-scale-the-leading-high-performance-serverless-ai-platform
- https://cerebrium.ai/blog/scaling-ai-tutors-how-creatium-achieved-18x-faster-cold-starts-with-cerebrium
- https://cerebrium.ai/blog/how-distillabs-is-delivering-50percent-lower-inference-costs-with-production-grad
- https://cerebrium.ai/blog/how-bithuman-scaled-digital-humans-10x-faster-with-cerebrium
- https://www.ycombinator.com/companies/cerebrium
- https://maxitech.com/insight/investing-in-cerebrium
- https://www.gradient.com/blog/posts/investing-in-cerebrium/
- https://tracxn.com/d/companies/cerebrium/__nemqqIFFtnkNO7dKsUJEoUGJ8PE3RnVa67qR1iTsUo
- https://www.g2.com/products/cerebrium/reviews

Research approved: 2026-05-06T17:53:53.976+00:00


### Customer outcomes

| Customer | Summary | Metric |
| --- | --- | --- |
| Creatium | Migrated from multi-cloud GPU setup to Cerebrium for AI tutor and avatar workloads, eliminating complex scaling logic and allowing engineers to focus on product development. | 18x faster cold starts (from several minutes to ~10 seconds) |
| DistilLabs | Deployed customer-specific SLM inference at up to 150 requests/second per model with production-grade autoscaling, reducing costs while improving model accuracy. | 50% lower inference costs; accuracy improved from 83% to 92% |
| bitHuman | Replaced Azure Functions and reserved instances with Cerebrium for digital human avatar deployments, cutting cold-start times dramatically and eliminating idle infrastructure costs. | Cold starts reduced from 30s to under 3s (warm); $5K–$10K/month in infrastructure savings |
| Tavus | Runs real-time audio and video AI models at scale on Cerebrium, maintaining compute reliability through rapid viral growth and usage spikes. | Not available |



### Reviews breakdown





### Review themes



#### Praised

- Sub-4 second cold starts via GPU snapshotting
- Bring-your-own-Dockerfile with no code rewrites
- Highly responsive engineering support via Slack
- 40% cost savings vs traditional cloud providers
- 12+ GPU types with per-second billing
- Production-grade autoscaling from zero to thousands of instances
- Developer-friendly CLI and deployment experience
- SOC 2, HIPAA, GDPR, ISO 27001 compliance out of the box



#### Criticized

- AWS and GCP credits cannot be applied to Cerebrium spend
- Not cost-optimal for always-on, high-utilization workloads
- No verified third-party reviews on G2 or Gartner (early-stage brand recognition)
- Capacity guarantees require minimum monthly spend commitments




### Company facts

Founded year: 2021
Hq: New York, USA


#### Founders

- Michael Louis
- Jonathan Irwin

Employees range: 11-50
Total funding: ~$9M
Valuation: Not available
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 | 4 | 16 |
| chatgpt-search | 3 | 12 |
| perplexity | 2 | 8 |
| google-ai | 1 | 4 |



## Strengths

| Prompt text | Platform count | Avg position |
| --- | --- | --- |
| Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | 1 | 7 |



## Gaps

| Prompt text | Competitor presence count |
| --- | --- |
| What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | 5 |
| 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 support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | 4 |



## Topic scores

| Topic name | Prompt count | Cited prompt count |
| --- | --- | --- |
| Capability | 5 | 2 |
| Developer Experience | 5 | 0 |
| Integrations & Ecosystem | 5 | 2 |
| Performance & Reliability | 5 | 3 |
| Setup & First Run | 5 | 0 |



## 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: Not available
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: 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: Not available
Chatgpt-search: 5
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: 7
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: 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 | 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: 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

| 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: 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 |
| 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: Not available
Perplexity: Not available
Google-ai: 6



#### 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: 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: 5
Chatgpt-search: 4
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: 3
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: 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 | 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: 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: 3
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: 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: 1
Chatgpt-search: 2
Perplexity: 7
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://cerebrium.ai/resources/serverless-provider | Serverless Providers for AI Inference: 2026 Buyer Guide | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | comparison | 26 | 26 |
| https://cerebrium.ai/resources/modal-alternatives-serverless-gpu-inference | Modal Alternatives for Serverless GPU Inference \| Cerebrium | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | documentation | 10 | 10 |
| https://cerebrium.ai/resources/loading-model-weights-object-storage-deploy-time | Loading Model Weights from Object Storage at Deploy | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | blog_post | 8 | 8 |
| https://cerebrium.ai/ | Serverless GPU Infrastructure for Real-Time AI \| Cerebrium | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | documentation | 8 | 8 |
| https://cerebrium.ai/resources/modal-vs-runpod-llm-inference-cost-cold-starts | Modal vs RunPod: LLM Inference Cost & Cold Starts | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | comparison | 3 | 3 |
| https://cerebrium.ai/docs/scaling/batching-concurrency | Batching and Concurrency - Cerebrium | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | documentation | 1 | 1 |
| https://cerebrium.ai/resources/northflank-alternatives-gpu-inference-workloads | Northflank Alternatives for GPU Inference: 6 Compared | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | comparison | 1 | 1 |
| https://cerebrium.ai/use-cases/large-language-models | LLM Deployment on Serverless GPUs \| Cerebrium | cerebrium.ai | https://izgwnlozsmjmqjsnddmg.supabase.co/storage/v1/object/public/domain-logos/9dbab6f8-54b2-49a0-8181-89a0ed130318/16f1426c-4fe3-4ca3-83a1-36e76ba0b4b5/1ea07f96fec6611fa4719a8a80d48940fab599d6.png | commercial | documentation | 1 | 1 |



## Response excerpts

| Prompt text | Platform | Excerpt |
| --- | --- | --- |
| What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | google-ai | Cerebrium Best For: Production-grade machine learning pipelines with strict sub-second latency targets. |
| Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | google-ai | Cerebrium * Why it excels for iteration: Cerebrium is built specifically for machine learning engineers who want to ship serverless GPU endpoints with minimal configuration. |
| Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | google-ai | Cerebrium * Overview: Cerebrium is a Python-native serverless ML platform that lets developers deploy custom models and inference logic without managing complex container pipelines. |



## Competitor excerpts

| Platform | Competitor name | Excerpt |
| --- | --- | --- |
| bing-copilot-search | Modal | Best serverless GPU inference providers for chaining model calls and tool use are Modal, Baseten, Fireworks AI, CoreWeave, and Databricks AI Runtime. |
| google-ai-mode | Baseten | Baseten * * Best For: Production-grade model APIs with heavy framework integrations and custom Triton/vLLM runtimes. |
| google-ai-mode | Modal | Modal * Best For: Code-first, highly dynamic pipelines where your tools and your model inference live in the same serverless deployment graph. |
| chatgpt-search | RunPod | The best serverless GPU inference providers for AI orchestration are generally Runpod, Modal, Baseten, Fireworks AI, Together AI, and fal. |
| chatgpt-search | Modal | The best serverless GPU inference providers for AI orchestration are generally Runpod, Modal, Baseten, Fireworks AI, Together AI, and fal. |
| perplexity | Together AI | For chaining model calls and tool use, Together AI and Fireworks AI are strong starting points: both offer managed serverless inference and fit naturally into common orchestration stacks. |
| google-ai | Modal | Modal Best For: Code-first pipeline execution, custom tool environments, and complex multi-model pipelines. |
| 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. |



## Trend

Visibility delta: -4.800000000000001
Avg position delta: 0.08149405772495744
Citation count delta: -12