AI visibility report for Beam
Vertical: LLM Inference & Serverless GPU
AI search visibility benchmark across 3 platforms in LLM Inference & Serverless GPU.
Presence Rate
Top-3 citations across 75 prompt × platform pairs
Sentiment
Peer Ranking
Key Metrics
Platform Breakdown
Gemini Search
Perplexity
ChatGPTOverview
Beam (beam.cloud) is an open-source, serverless AI infrastructure platform founded in 2021 and backed by Y Combinator (W22), Tiger Global, and angel investors including the founders of Snyk and GitHub. Built around a custom container runtime called beta9, Beam enables developers to run GPU inference endpoints, secure code sandboxes, async task queues, and scheduled jobs using simple Python or TypeScript decorators—with no YAML or Dockerfile configuration required. Containers launch in under one second, billing is per-millisecond, and apps scale to zero when idle. Beam differentiates as the only major serverless GPU platform with a fully open-source, self-hostable runtime (AGPL-3.0), enabling deployment across Beam's managed cloud, AWS, or on-premises infrastructure. Named customers include Coca-Cola, Magellan AI, Geospy, and Frase.
Beam is an open-source serverless cloud platform for AI inference, sandboxes, and background jobs. Developers decorate Python or TypeScript functions to run on GPU or CPU-backed containers that launch in under one second, autoscale to thousands of replicas, and bill only for active compute time. The platform supports REST endpoint deployment, async task queues, scheduled cron jobs, sandbox environments with checkpoint/restore for long-running agent sessions, and self-hosting via its open-source runtime (beta9). It is used by startups and Fortune 100 companies to run custom ML models and execute LLM-generated code securely at scale.
Key Facts
- Founded
- 2021
- HQ
- New York, NY, USA
- Founders
- Eli Mernit, Luke Lombardi
- Employees
- 5-10
- Funding
- $7M
- Customers
- hundreds (self-reported)
- Status
- Private
Target users
Key Capabilities10
- Serverless GPU and CPU inference endpoints with pay-per-millisecond billing
- Sub-second container launch via custom Go-based runtime (beta9)
- Secure LLM-generated code execution in gVisor-isolated sandboxes
- Sandbox snapshots and GPU checkpoint/restore for stateful agent sessions
- Async task queues and scheduled cron jobs with no timeouts
- Instant autoscaling to thousands of containers with scale-to-zero
- Open-source, self-hostable runtime (AGPL-3.0) deployable on AWS or local machine
- Distributed storage volumes and S3 bucket mounting
- Python and TypeScript SDKs with decorator-based deployment (no YAML required)
- CI/CD integration via GitHub Actions and versioned endpoint deployments
Key Use Cases8
- Serverless GPU inference for custom ML and generative AI models
- Secure code sandbox execution for AI agents and LLM-generated code
- Async background batch processing and data pipelines on GPU/CPU
- Scheduled ML training jobs and distributed function execution
- Rapid deployment of Dockerized AI models as REST APIs
- Hybrid cloud and on-premises AI workloads requiring self-hosting
- Image generation and video transcription services with autoscaling
- Conversational AI and LLM endpoint hosting for production apps
Beam customer outcomes
Hours vs. weeks to build GPU app component
The team credited Beam with enabling them to ship their product significantly faster than expected, building the GPU-powered portion of their application in hours rather than weeks.
Coca-Cola is cited as a production customer using Beam for serverless GPU inference workloads at enterprise scale.
Recent Trend
How AI describes Beam3
| | Beam | 2–3 Seconds | Optimized weight loading via Tigris storage and pre-cached runtimes.
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays?
Beam (formerly Beam.cloud) Beam focuses on low-latency serverless and utilizes a custom container runtime (beta9) to make model loading extremely fast.
What serverless GPU platforms charge per-second so I'm not paying for idle time?
Beam Beam has gained significant traction by moving away from standard Docker runtimes in favor of a custom, lazy-loading approach.
Which LLM inference providers have the lowest cold start times for serverless GPU workloads?
Most cited sources1
Alternatives in LLM Inference & Serverless GPU6
Beam positions itself explicitly as an open-source alternative to Modal, differentiating through its self-hostable runtime (beta9, AGPL-3.0), portable workloads across cloud and on-premises, and a Python/TypeScript decorator-based developer experience requiring no YAML or Dockerfile configuration.
- Its primary wedge is vendor-lock-in avoidance: the same CLI and SDK work identically on Beam cloud, AWS self-hosted, or a local machine.
- Beam targets AI teams building bursty inference, agent sandboxes, and background jobs who want serverless economics without proprietary platform dependency.
- Compared to Modal (developer experience, closed), RunPod (price/GPU breadth, closed), and Baseten (enterprise inference, closed), Beam is the only OSS-first, self-hostable option in the segment.
Reviews
Praised
- Excellent developer experience and onboarding
- Fast GPU deployment with minimal configuration
- Pay-per-millisecond billing reduces idle compute costs
- Highly responsive founder/support team
- Open-source and self-hostable runtime
- Eliminates VM infrastructure management overhead
- Python decorator-based API requires no YAML or Dockerfiles
Criticized
- Cold starts (2–3s) slower than Modal's sub-second performance
- Narrower GPU catalog compared to RunPod
- Small team may limit enterprise support capacity
- TypeScript SDK still in beta
- No publicly confirmed SOC 2 or formal enterprise SLA
- Limited published information on geographic regions
Public developer sentiment is broadly positive, with users citing fast onboarding, strong developer experience, and elimination of VM management overhead. Testimonials highlight the ability to ship GPU-backed features in hours rather than weeks, and praise the responsiveness of the Beam team. Third-party comparison analyses position Beam as the preferred choice for teams requiring portability and self-hosting, while noting that cold start times (2–3 seconds) lag behind Modal's sub-second performance and that the GPU catalog is narrower than RunPod's. No formal review scores from G2, Gartner Peer Insights, or Capterra were found at time of research.
Pricing
Beam uses pay-per-millisecond billing with no upfront commitments. Published rates: CPU at $0.190/core/hr, RAM at $0.020/GB/hr, RTX 4090 at $0.69/hr, A10G at $1.05/hr, H100 at $3.50/hr. File storage is included at no charge. Cold start time (container spin-up) is not billed. New accounts receive 15 hours of free credit on signup. Beam claims up to 80% savings versus always-on VM instances for bursty workloads. No tiered plan structure or minimum spend requirement is documented; enterprise pricing is available via direct contact.
Limitations
- Cold start times of 2–3 seconds cited by third-party comparisons for most workloads, slower than Modal's sub-second Rust-based runtime.
- GPU catalog is narrower than RunPod (T4, RTX 4090, A10G, A100, H100 listed; no H200 or B200 published).
- No formal enterprise SLAs or uptime guarantees documented publicly (unlike Baseten's 99.99%).
- Very small team (approximately 5–7 people) may limit enterprise support and feature velocity.
- No egress-free regions noted (unlike RunPod).
- TypeScript SDK remains in beta.
- No published model marketplace or pre-hosted foundation model library.
- Limited geographic region information disclosed.
- No SOC 2 certification publicly confirmed at time of research.
Frequently asked questions
Topic Coverage
Prompt-Level Results
| Prompt | Perplexity | ChatGPT | Gemini Search |
|---|---|---|---|
Capabilities0/5 cited (0%) | |||
Which GPU clouds support multi-modal model inference including vision, audio, and image generation? | |||
Which serverless AI providers offer EU data residency and sovereign infrastructure for regulated workloads? | |||
Which inference providers support custom model deployment beyond just popular open-source weights? | |||
What platforms offer fine-tuning APIs alongside inference for the same open-source models? | |||
What inference platforms provide LoRA adapter swapping at request time? | |||
Cost & Pricing1/5 cited (20%) | |||
Which inference platforms offer batch or async pricing tiers with significant discounts for non-realtime workloads? | |||
What serverless GPU platforms charge per-second so I'm not paying for idle time? | |||
Which GPU cloud providers offer spot or preemptible pricing for AI workloads? | |||
What's the most cost-effective way to run a high-volume RAG pipeline against an open-weights model? | |||
Which LLM inference providers offer the cheapest pricing per million tokens for open-source models? | |||
Performance1/5 cited (20%) | |||
What inference platforms deliver the highest tokens-per-second for Llama 70B and similar large models? | |||
Which LLM inference providers have the lowest cold start times for serverless GPU workloads? | |||
Which serverless AI platforms can handle bursty traffic to long-running model endpoints? | |||
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays? | |||
What are the best inference platforms for low-latency real-time agent workflows? | |||
Production Readiness0/5 cited (0%) | |||
Which LLM inference platforms have the most reliable uptime and SLAs for production workloads? | |||
What inference providers offer dedicated capacity or reserved GPU instances for predictable performance? | |||
Which GPU compute providers support running models inside a customer's VPC for compliance? | |||
What inference platforms include built-in observability, logging, and alerting for production model deployments? | |||
Which serverless GPU platforms have proven track records with high-traffic AI applications? | |||
Setup & First Run0/5 cited (0%) | |||
I need a hosted inference API for Llama or Mistral that I can hit with an OpenAI-compatible client — what are my options? | |||
What's the fastest way to deploy an open-source LLM behind an API endpoint without managing GPUs? | |||
Which inference platforms have the lowest learning curve for a frontend developer who just wants an API key? | |||
Which serverless GPU platforms let me run a Hugging Face model with a single CLI command? | |||
What's the easiest way to run my own fine-tuned model in production without provisioning GPUs? | |||
Strengths1
Which LLM inference providers have the lowest cold start times for serverless GPU workloads?
Avg # 2.0 · 1 platform
Gaps5
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays?
Competitors on 2 platforms
Which GPU clouds support multi-modal model inference including vision, audio, and image generation?
Competitors on 1 platform
What serverless GPU platforms charge per-second so I'm not paying for idle time?
Competitors on 1 platform
What inference providers offer dedicated capacity or reserved GPU instances for predictable performance?
Competitors on 1 platform
What platforms offer fine-tuning APIs alongside inference for the same open-source models?
Competitors on 1 platform
Vertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 20.0% | 47.5% | 0.0% | 0.0% | 17.3% | #5.9 | +0.28 |
| 2 | Together AI | 6.7% | 17.5% | 0.0% | 1.3% | 6.7% | #5.0 | +0.33 |
| 3 | Beam | 4.0% | 15.0% | 0.0% | 0.0% | 4.0% | #5.3 | +0.08 |
| 4 | Modal Labs | 4.0% | 7.5% | 0.0% | 4.0% | 4.0% | #6.3 | +0.08 |
| 5 | Cerebrium | 2.7% | 7.5% | 0.0% | 0.0% | 1.3% | #4.3 | +0.25 |
| 6 | Baseten | 1.3% | 2.5% | 0.0% | 0.0% | 1.3% | #4.0 | +0.65 |
| 7 | Sference | 1.3% | 2.5% | 0.0% | 0.0% | 1.3% | #5.0 | +0.00 |
| 8 | Fireworks AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 10 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
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