AI visibility report
Baseten ranks #4 in LLM Inference & Serverless GPU AI search.
Outside the top three on 15 of the 25 prompts buyers actually ask.
Fireworks AI is cited on 7 of those losses.
Free trial. Setup comes pre-filled for Baseten.
Track Baseten across these prompts daily.
Start free trial#4 among 10 vendors · still absent from 91.2% of tracked prompt responses
Top-3 citations across 125 prompt × platform pairs
Peer Ranking
Key Metrics
Platform Breakdown
Google AI Mode
ChatGPT
Gemini Search
Bing Copilot
PerplexityNarrower footprint, stronger tone. Baseten ranks #4 on presence but #3 on sentiment. That means the brand is framed well when it appears, but still needs broader prompt-response coverage.
Where Baseten is losing
Prompts where competitors are visible and Baseten is not.
These prompt-level losses are the first prompts to track and repair.
Where Baseten is winning5
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box?
Avg # 1.0 · 1 platform
What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps?
Avg # 1.0 · 1 platform
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?
Avg # 1.5 · 2 platforms
Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying?
Avg # 2.0 · 1 platform
Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments?
Avg # 3.0 · 1 platform
Where Baseten is losing5
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature?
Competitors on 3 platforms
Track this promptWhich serverless GPU inference platforms have the lowest cold-start latency for a customer-facing chat app that needs sub-second first-token response times?
Competitors on 3 platforms
Track this promptWhat 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?
Competitors on 3 platforms
Track this promptWhich LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code?
Competitors on 2 platforms
Track this promptWhich LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time?
Competitors on 2 platforms
Track this prompt
Track Baseten daily before the next report refresh.
Track these gapsResearch dossierCapabilities, use cases, sources, reviews, pricing, and FAQ
Overview
Baseten is a San Francisco-based AI inference platform founded in 2019 by Tuhin Srivastava, Amir Haghighat, Philip Howes, and Pankaj Gupta. The company's Inference Stack combines modality-specific model runtimes, multi-cloud GPU orchestration across 10+ providers, and developer tooling to enable high-performance, low-latency production deployment of open-source and proprietary AI models. Product offerings include Dedicated Deployments for custom models, pre-optimized Model APIs, Baseten Training for fine-tuning, and the open-source Truss framework. Supported modalities span LLMs, transcription, image generation, text-to-speech, and embeddings. Notable customers include Cursor, Abridge, OpenEvidence, Notion, Clay, and Writer. Backed by $585M in total funding at a $5B valuation (January 2026), Baseten reported 10x revenue growth and 100x inference volume growth year-over-year.
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
Other good options to consider: Baseten, RunPod, Replicate for quick demos and packaging, depending on your preference for control vs. simplicity.
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time?
Other platforms (Modal, Replicate, Baseten, Fal.ai, etc.) offer strong developer experiences and good performance, but their published cold-start claims vary and may require workload-specific tuning or pre-warming strategies.
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?
Baseten and Together AI: designed for deploying ML-powered apps and workflows with orchestration-friendly APIs and model composition capabilities.
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly?
Most cited sources8
- D10
Overview - Baseten
docs.baseten.co·Documentation
- D5
Metrics - Baseten overview
docs.baseten.co·Documentation
- D4
Deploy from cloud storage - Baseten
docs.baseten.co·Documentation
4New observability features: activity logging, LLM metrics, and metrics dashboard customization
baseten.co·Blog Post
- 3
How we built production-ready speculative decoding with TensorRT-LLM - Baseten
baseten.co·Blog Post
- 3
The Baseten Inference Stack | Guides
baseten.co·Blog Post
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 coverageCoverage by buyer topic
Topic Coverage
Prompt-Level Results
| Prompt | ChatGPT | Google AI Mode | Gemini Search | Bing Copilot | Perplexity |
|---|---|---|---|---|---|
Capability2/5 cited (40%) | |||||
What LLM inference platforms handle streaming token responses well and support long context windows for document-processing use cases? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms let enterprise teams bring their own fine-tuned model weights and enforce strict data isolation with private deployments? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless inference platforms support running large multimodal models — handling both text and image inputs — on high-end GPUs at production scale? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless GPU platforms support batch inference jobs for offline processing pipelines in addition to real-time API endpoints? | Neither your brand nor a competitor was cited | Your brand was cited | A competitor was cited | A competitor was cited | A competitor was cited |
I'm looking for an inference platform that supports custom CUDA kernels and speculative decoding — what are my options for a performance-critical chatbot? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Developer Experience4/5 cited (80%) | |||||
Which serverless GPU platforms give engineering teams the most visibility into per-request latency and token throughput right out of the box? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Your brand and a competitor were cited | Neither your brand nor a competitor was cited | A competitor was cited |
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? | Your brand was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms offer the best SDK and API ergonomics for a Python-first engineering team shipping a conversational AI feature? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which serverless inference platforms make it easiest to manage multiple open-source model versions in parallel across staging and production environments? | Neither your brand nor a competitor was cited | Your brand was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU inference platforms have the best developer experience for iterating quickly on prompt templates and sampling parameters without redeploying? | A competitor was cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Integrations & Ecosystem1/5 cited (20%) | |||||
Which LLM inference platforms integrate natively with vector database services for building retrieval-augmented generation pipelines without extra glue code? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What serverless GPU inference providers work best alongside AI orchestration frameworks so teams can chain model calls and tool use cleanly? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Which inference platforms expose an API compatible with the standard chat completions format so switching providers requires minimal code changes? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms integrate with cloud object storage for loading large model weights at deploy time without manual upload steps? | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms support webhook callbacks or event-driven triggers for async inference jobs in a data pipeline built on a workflow orchestrator? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Performance & Reliability0/5 cited (0%) | |||||
Which serverless inference providers deliver the highest tokens-per-second throughput for a high-volume API serving thousands of concurrent users? | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
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? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
What are the most reliable LLM serving platforms for an enterprise use case that requires 99.9% uptime SLAs and geo-redundant deployments? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What LLM inference platforms can handle sudden traffic spikes — say 10x burst load — without throttling for a mid-sized SaaS product? | A competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which serverless GPU platforms have the best cost-per-token at scale for a startup burning significant GPU budget on a document summarization product? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Setup & First Run2/5 cited (40%) | |||||
Which LLM inference platforms have the easiest onboarding for a solo developer deploying a fine-tuned open-source model for the first time? | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
Which LLM inference platforms support deploying quantized open-source models with minimal setup for a backend engineer with no MLOps background? | Your brand and a competitor were cited | Your brand and a competitor were cited | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited |
What's the quickest serverless GPU platform to get an image-generation model behind a REST API with autoscaling out of the box? | A competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
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? | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited |
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? | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited | Neither your brand nor a competitor was cited | A competitor was cited |
Turn this matrix into daily prompt monitoring.
Track prompt changesVertical Ranking
| # | Brand | PresencePres. | Share of VoiceSoV | DocsDocs | BlogBlog | MentionsMent. | Avg PosPos | Sentiment |
|---|---|---|---|---|---|---|---|---|
| 1 | RunPod | 15.2% | 29.5% | 1.6% | 0.0% | 40.0% | #4.2 | +0.32 |
| 2 | Fireworks AI | 12.0% | 17.0% | 1.6% | 6.4% | 36.8% | #3.4 | +0.46 |
| 3 | Beam | 11.2% | 17.9% | 0.0% | 0.0% | 12.0% | #4.8 | +0.29 |
| 4 | Baseten | 8.8% | 16.1% | 6.4% | 3.2% | 42.4% | #2.6 | +0.50 |
| 5 | Modal | 8.0% | 9.8% | 0.0% | 2.4% | 0.0% | #3.2 | +0.55 |
| 6 | Together AI | 5.6% | 7.1% | 2.4% | 0.8% | 44.8% | #2.1 | +0.30 |
| 7 | Cerebrium | 2.4% | 2.7% | 0.8% | 0.0% | 8.0% | #2.0 | +0.60 |
| 8 | Lepton AI | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
| 9 | Replicate | 0.0% | 0.0% | 0.0% | 0.0% | 32.8% | — | — |
| 10 | Sference | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | — | — |
Turn this into your team dashboard
Sign up to unlock project-level analytics, daily tracking, actionable insights, custom prompt configurations, adoption tracking, AI traffic analytics and more.
Free trial. Setup comes pre-filled from this report.