AI visibility report for Baseten
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
ChatGPT
Perplexity
Gemini SearchOverview
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.
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 Facts
- Founded
- 2019
- HQ
- San Francisco, CA, USA
- Founders
- Tuhin Srivastava, Amir Haghighat, Philip Howes +1 more
- Funding
- ~$585M
- Valuation
- $5B
- Status
- Private
Target users
Key Capabilities10
- 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
Key Use Cases8
- 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
Baseten customer outcomes
440 engineer-hours saved annually; $600K cost savings; 70% reduction in GPU costs
Deployed OpenAI Whisper on Baseten for auto-generated closed captions for creator content, eliminating the need for custom GPU infrastructure management.
90% inference cost savings; 65% lower median latency
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.
3x speed improvement; 160ms embedding latency
Used Baseten Embeddings Inference to power near-instant medical information retrieval for physicians, achieving ultra-low latency critical for clinical use cases.
2x faster code completions
Served AI code completions through Baseten's Inference Stack, improving response speed for the Zed code editor's AI features.
5x faster image generation
Leveraged Baseten for AI image generation powering presentation creation features, achieving a major improvement in generation throughput.
More than 1 million clinical notes generated weekly for tens of thousands of clinicians
Used Baseten's inference infrastructure to scale real-time medical conversation transcription and clinical note generation safely across health systems.
Recent Trend
How AI describes Baseten3
Baseten: Uses the Truss framework to package models. They specialize in keeping "fractional" GPUs active to reduce wake-up times to a few seconds.
Which GPU compute platforms scale to zero when idle and back up under load without minute-long delays?
### Baseten / Modal * The Edge: Infrastructure-as-code with rapid scaling.
What are the best inference platforms for low-latency real-time agent workflows?
Baseten / Modal: These are developer-focused infra platforms that allow you to deploy "cold-start" or "warm" models.
What inference platforms provide LoRA adapter swapping at request time?
Alternatives in LLM Inference & Serverless GPU6
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.
Reviews
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
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.
Pricing
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.
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.
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 & Pricing0/5 cited (0%) | |||
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? | |||
Performance0/5 cited (0%) | |||
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 Readiness1/5 cited (20%) | |||
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 GPU compute providers support running models inside a customer's VPC for compliance?
Avg # 4.0 · 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
Which LLM inference providers have the lowest cold start times for serverless GPU workloads?
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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