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Baseten

  • What it is:Baseten is a San Francisco-based AI infrastructure platform specializing in deploying, serving, and scaling machine learning models, especially large language models, with minimal MLOps expertise required.
  • Best for:Enterprise AI teams needing production inference, Companies running agentic workflows and reasoning models, Teams requiring guaranteed GPU availability
  • Pricing:Starting from $0/month, pay as you go
  • Rating:85/100Very Good
  • Expert's conclusion:Baseten excels for production ML teams prioritizing performance, control, and compliance over simplicity and minimal upfront costs.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Baseten and What Does It Do?

Baseten has been developing its AI infrastructure since 2019. Its primary focus has been on making it simpler for teams to deploy and operate their machine learning models into a production environment. To this end, Baseten offers a variety of products such as inference infrastructure, workflows and tools which allow developers to deploy AI applications across multiple cloud providers at large-scale.

Active
📅Founded 2019
🏢Private
TARGET SEGMENTS
EnterpriseAI CompaniesDevelopersMachine Learning Teams

What Are Baseten's Key Business Metrics?

📊
$5 billion
Valuation
📊
$585 million
Total Funding Raised
📊
Thousands of GPUs
GPU Infrastructure
📊
10+
Cloud Providers Integrated
👥
Millions
End Customers Served
👥
Abridge, Bland, Descript, Gamma, Writer
Notable Customers

How Credible and Trustworthy Is Baseten?

85/100
Excellent

Baseten has shown it has the credibility of being funded by top tier investors (IVP, CapitalG – Google, and Nvidia) and it has demonstrated it can attract and retain millions of users and thousands of GPUs in real-world production environments.

BREAKDOWN
Product Maturity85/100
Company Stability90/100
Security & Compliance80/100
User Reviews80/100
Transparency85/100
Support Quality80/100
TRUST SIGNALS
$5B valuation with $300M Series D led by IVP and CapitalG$150M investment from Nvidia demonstrating deep technical partnershipServes millions of end customers across fast-growing AI companiesAdopted NVIDIA Blackwell GPUs and latest inference frameworksUnified GPU pool across 10+ cloud providers in dozens of global regions

What is the history of Baseten and its key milestones?

2019

Company Founded

Baseten was started by Tuhin Srivastava (CEO), Amir Haghighat (CTO) and Philip Howes to address some of the issues associated with deploying machine learning models into production.

2020

Public Beta Launch

After 18 months of private development, Baseten launched its public beta introducing a new bundled approach to deploying entire full-stack machine learning applications.

2021

Series A Funding

Since launching, Baseten has raised in excess of $20 million in seed and series A funding to assist with further product development and hiring.

2024

Series B Funding

In March of 2024, Baseten raised an additional $40 million, and also secured another $75 million in February of 2024, followed by $150 million in September of 2024.

2026

Series D Funding and $5B Valuation

Baseten’s recent round of financing of $300 million in a Series D was led by IVP and CapitalG and included an additional $150 million investment from Nvidia. This investment increased Baseten’s value to $5 billion, and also made it the first company to be using NVIDIA Blackwell GPUs on Google Cloud.

What Are the Key Features of Baseten?

✨
Multi-Cloud GPU Infrastructure
Baseten’s system aggregates the scalable GPU pools from over 10 different cloud providers located in dozens of global regions. This allows developers to have the option of easily moving and deploying applications in order to optimize costs.
✨
Low-Latency Inference Engine
Baseten’s architecture ensures consistent, low-latency operation and high-availability even when running under heavy loads. Baseten does this by automatically allocating necessary resources and optimizing execution paths across all available hardware.
👥
Workflow Management and Orchestration
Baseten has developed various tools that help manage model versions, visibility, automated deployments and tracking model performance without requiring developers to build any custom infrastructure.
💬
Open-Source Model Support
Baseten is designed to work with a variety of standard machine learning frameworks and also support open-source models. As a result, Baseten allows developers to use a wide range of machine learning models and workflows.
📊
Advanced Inference Optimization
Utilizes NVIDIA Dynamo & TensorRT-LLM for maximum efficiency when serving edge-reasoning models like DeepSeek-R1 and Llama 4 for large context windows.
📊
Production-Grade Reliability
Built for mission critical AI/ML workloads providing scalable, cost-effective service to millions of end-users.
🔗
API-First Architecture
Offers APIs for developers to quickly deploy models and send predictions directly to end users with little or no overhead associated with creating an infrastructure.

What Technology Stack and Infrastructure Does Baseten Use?

Infrastructure

Multi-cloud infrastructure spanning 10+ cloud providers with dedicated GPU clusters across dozens of global regions; adopted NVIDIA HGX B200 with Blackwell GPUs on Google Cloud

Technologies

NVIDIA Blackwell GPUsNVIDIA Dynamo inference frameworkNVIDIA TensorRT-LLMPythonPyTorchKubernetes

Integrations

Google CloudAWSAzureMultiple cloud providers (10+)Open-source ML frameworksStandard machine learning frameworks

AI/ML Capabilities

Advanced inference platform supporting frontier large language models and reasoning models with support for massive context windows, built on NVIDIA Dynamo and TensorRT-LLM optimization frameworks

Based on official announcements, NVIDIA case study, and company blog posts

What Are the Best Use Cases for Baseten?

AI Product Companies
Enables the rapid deployment and scaling of large language models and reasoning models using optimized inference techniques to support millions of users, while minimizing latency and managing costs.
Enterprise Machine Learning Teams
Reduces the time it takes to put your ML model(s) into production by allowing you to deploy them with minimal configuration, manage versions/model updates as needed without having to build your own customized infrastructure, and provide insights into how your production environment is performing.
Data Scientists and ML Engineers
Allows you to ship your ML models faster by utilizing a pre-built inference infrastructure, workflow tools, and APIs versus requiring you to build a custom back-end infrastructure from scratch.
Companies Requiring Multi-Cloud Deployment
Enables a single unified pool of GPUs across 10+ cloud providers to maximize cost savings, redundancy, and deployment flexibility while avoiding vendor lock-in.
Organizations Serving Complex Reasoning Models
Enables frontier models such as DeepSeek-R1 and Llama 4 Scout to operate within massive context windows while balancing inference cost, latency, and throughput through the optimized use of Blackwell GPU infrastructure.
Developers Building ML-Powered Applications
Enables you to rapidly integrate ML predictions into your application(s) using simple APIs and bundled infrastructure tools, removing the need for extensive infrastructure expertise.
NOT FORApplications Requiring Sub-100ms Inference Latency
Although Baseten has optimized for low latency, extremely high demand real-time requirements may necessitate specialized solutions.
NOT FORSmall Teams with Limited ML Infrastructure Knowledge
While Baseten streamlines the process of deploying models, the platform is designed specifically for production scale workloads and may be overly engineered for simple proof-of-concept prototyping projects.
NOT FOROn-Premises-Only Deployments
Not Suitable – Baseten is a cloud-based multi-cloud infrastructure platform with no on-premises deployment option available.

How Much Does Baseten Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Basic$0/month, pay as you goModel APIs priced per 1M tokens (e.g. DeepSeek V3.1: $0.50 input / $1.50 output). 40% price reduction across all instance types.—
Model APIs (example)Per 1M tokensKimi K2.5: $0.60 input / $2.50 output; GPT OSS 120B: $0.10 input / $0.50 output; DeepSeek V3.1: $0.50 input / $1.50 outputOfficial pricing page
ProVolume discounts, get quoteUnlimited autoscaling, priority compute access, dedicated compute, higher rate limits, hands-on engineering support, dedicated Slack/Zoom support—
EnterpriseCustom quote (starts ~$5,000/month)Custom SLAs, training, self-host deployments, on-demand flex compute, use existing cloud commitments, full data residency control, advanced security/complianceThird-party analysis
Dedicated DeploymentsPer-minute GPU/CPU billingA10G: $1.207/hour (after 40% reduction). Costs vary by hardware (T4 cheaper, H100 more expensive) and traffic patterns. Autoscaling impacts costs.Changelog + third-party
Basic$0/month, pay as you go
Model APIs priced per 1M tokens (e.g. DeepSeek V3.1: $0.50 input / $1.50 output). 40% price reduction across all instance types.
Model APIs (example)Per 1M tokens
Kimi K2.5: $0.60 input / $2.50 output; GPT OSS 120B: $0.10 input / $0.50 output; DeepSeek V3.1: $0.50 input / $1.50 output
Official pricing page
ProVolume discounts, get quote
Unlimited autoscaling, priority compute access, dedicated compute, higher rate limits, hands-on engineering support, dedicated Slack/Zoom support
EnterpriseCustom quote (starts ~$5,000/month)
Custom SLAs, training, self-host deployments, on-demand flex compute, use existing cloud commitments, full data residency control, advanced security/compliance
Third-party analysis
Dedicated DeploymentsPer-minute GPU/CPU billing
A10G: $1.207/hour (after 40% reduction). Costs vary by hardware (T4 cheaper, H100 more expensive) and traffic patterns. Autoscaling impacts costs.
Changelog + third-party
💡Pricing Example: Serving DeepSeek V3.1 model via Model API, 10M input + 10M output tokens/month
Basic Pay-as-you-go$20,000/month
$0.50 x 10M input + $1.50 x 10M output = $20 per million tokens
Pro (with volume discount)Negotiated lower
Volume discounts available, exact pricing requires quote
💰Savings:40% lower compute pricing + volume discounts can substantially reduce costs

How Does Baseten Compare to Competitors?

FeatureBasetenReplicateTogether AIFireworks AIDeepInfra
Core FunctionalityModel APIs + Dedicated DeploymentsModel APIs + DeploymentsModel APIs + Fine-tuningModel APIs + ServerlessModel APIs
AutoscalingAdvanced (step-level)YesYesYesYes
Multi-CloudYes (Google Cloud + others)LimitedLimitedLimitedLimited
Inference Stack OptimizationProprietary (225% better perf)StandardStandardStandardStandard
Starting Price (per 1M tokens)$0.10+ (GPT OSS 120B)$0.15+$0.20+$0.12+$0.08+
Free TierPay-as-you-go from $0Limited creditsLimited creditsLimited creditsLimited credits
Enterprise SSOYes (Enterprise)YesYesYesPartial
API AvailabilityYesYesYesYesYes
Priority GPU AccessPro/EnterpriseEnterpriseEnterpriseEnterpriseNo
SOC 2 ComplianceEnterpriseYesYesYesPartial
Core Functionality
BasetenModel APIs + Dedicated Deployments
ReplicateModel APIs + Deployments
Together AIModel APIs + Fine-tuning
Fireworks AIModel APIs + Serverless
DeepInfraModel APIs
Autoscaling
BasetenAdvanced (step-level)
ReplicateYes
Together AIYes
Fireworks AIYes
DeepInfraYes
Multi-Cloud
BasetenYes (Google Cloud + others)
ReplicateLimited
Together AILimited
Fireworks AILimited
DeepInfraLimited
Inference Stack Optimization
BasetenProprietary (225% better perf)
ReplicateStandard
Together AIStandard
Fireworks AIStandard
DeepInfraStandard
Starting Price (per 1M tokens)
Baseten$0.10+ (GPT OSS 120B)
Replicate$0.15+
Together AI$0.20+
Fireworks AI$0.12+
DeepInfra$0.08+
Free Tier
BasetenPay-as-you-go from $0
ReplicateLimited credits
Together AILimited credits
Fireworks AILimited credits
DeepInfraLimited credits
Enterprise SSO
BasetenYes (Enterprise)
ReplicateYes
Together AIYes
Fireworks AIYes
DeepInfraPartial
API Availability
BasetenYes
ReplicateYes
Together AIYes
Fireworks AIYes
DeepInfraYes
Priority GPU Access
BasetenPro/Enterprise
ReplicateEnterprise
Together AIEnterprise
Fireworks AIEnterprise
DeepInfraNo
SOC 2 Compliance
BasetenEnterprise
ReplicateYes
Together AIYes
Fireworks AIYes
DeepInfraPartial

How Does Baseten Compare to Competitors?

vs Replicate

Baseten is an enterprise-grade platform that utilizes automated autoscaling and multi-cloud redundancy, whereas Replicate provides a simple experience for developers. Baseten has better cost-performance than Replicate (a 225% improvement), however it will require greater commitment from users in order to reach production scale.

Baseten should be used for critical enterprise inference needs and Replicate for rapid prototyping.

vs Together AI

Together focuses on developing models using open fine-tuning as well as research and development, whereas Baseten focuses on optimizing models for use in production environments. Baseten’s proprietary stack provides superior performance compared to Together’s cloud-based stack, however Together may provide a cost-effective option for users experimenting with their workload.

Baseten should be used for production-scale serving and Together for model training or fine-tuning.

vs Fireworks AI

Both are serverless inference platforms, however Baseten has developed a platform designed specifically for enterprise customers that allows them to customize Service Level Agreements (SLAs) and host their own applications and models, whereas Fireworks is focused on delivering fast results for Small- to Medium-Sized Businesses (SMB). Baseten has stronger multi-cloud resilience than Fireworks.

Baseten should be used by businesses looking for enterprise level reliability and Fireworks should be used by businesses looking for developer speed.

vs DeepInfra

DeepInfra offers lower commodity pricing than Baseten, however it lacks many of the same features that Baseten provides including inference optimizations as well as enterprise features. The higher pricing of Baseten can be justified due to its 225% better cost-performance and its production readiness.

DeepInfra should be used for budget testing and Baseten should be used for production optimization.

What are the strengths and limitations of Baseten?

Pros

  • Provides best-in-class inference performance – 225% better cost-performance on Google Cloud A4 VMs
  • Offers advanced autoscaling capabilities – step-level scaling prevents over-provisioning and decreases costs
  • Provides multi-cloud resilience – automatically fails over across clouds and maintains service availability
  • Has a proprietary inference stack – optimizes every model for speed, reliability and cost
  • Reduced prices by 40% recently – across all CPU/GPU instance types are being passed down to customers
  • Includes enterprise ready features – custom SLAs, self-hosting, data residency control
  • Guarantees priority access to GPUs – Pro plan ensures high demand hardware is always available

Cons

  • Has complex enterprise pricing – requires custom quote requests and minimum commitments from customers
  • Unpredictable costs — autoscaling + traffic spikes will be a surprise to those who have planned budgets
  • Overhead for integration — requires developers to write custom application logic and connect applications to business tools
  • Pay-per-Token for Model APIs — costs will grow quickly as the number of tokens increases when using high volume inference
  • Infrastructure management — teams will continue to manage scaling configurations and monitor their deployments
  • Lack of pricing transparency — exact prices for Pro/Enterprise plans will need to come through a sales representative
  • Requires developer time — development of an actual production application will add additional overhead

Who Is Baseten Best For?

Best For

  • Enterprise AI teams needing production inference — Justifies investment for advanced autoscaling, multi-cloud redundancy and 225% cost-performance improvements
  • Companies running agentic workflows and reasoning models — Designed for multi-step inference with independently scaled steps
  • Teams requiring guaranteed GPU availability — Priority access to Pro Plan during peak demand (prevents compute bottlenecks)
  • Organizations with compliance needs — Data residency control and advanced security are included with Enterprise plans
  • High-throughput inference applications — Superior performance is provided by Baseten’s proprietary stack and most recent NVIDIA GPUs

Not Suitable For

  • Small teams or startups with experimental workloads — Too expensive due to enterprise pricing and minimum commitments. Consider Replicate or DeepInfra.
  • Budget-conscious developers — A complex pricing structure that lacks transparency. Consider commodity rates from DeepInfra.
  • Teams wanting simple model APIs without infra management — Still requires custom integration work. Consider Fireworks AI or Together.
  • Low-volume inference needs — Expensive at small scales when using the pay-per-token model. Use native provider APIs.

Are There Usage Limits or Geographic Restrictions for Baseten?

Model API Pricing
Per 1M tokens (varies by model: $0.10-$0.77 input, $0.50-$2.50 output)
Dedicated Deployments
Per-minute GPU/CPU billing (A10G: $1.207/hour post-reduction)
Autoscaling
Provisions additional instances during traffic spikes, increasing costs
Pro Plan Rate Limits
Higher limits than Basic (exact limits not public)
Minimum Commitments
Often required for Enterprise/production deployments
GPU Availability
Priority access on Pro/Enterprise; Basic subject to availability
Deployment Options
Baseten cloud, customer VPC, hybrid (Enterprise)
Data Residency
Full control on Enterprise; multi-region on shared infrastructure
Custom Models
Dedicated deployments only (Model APIs use pre-optimized models)

Is Baseten Secure and Compliant?

Multi-Cloud RedundancyGlobal deployment across multiple clouds with automatic failover via Google Cloud DWS
Advanced Security (Enterprise)Custom security configurations, compliance frameworks, data residency control
Infrastructure ResilienceDynamic Workload Scheduler enables automatic recovery from cloud outages in minutes
Data Residency ControlEnterprise customers can choose regions and VPC deployment options
Production SLAs (Enterprise)Custom uptime and performance guarantees for mission-critical workloads
Self-Hosting OptionEnterprise can deploy in customer VPC for maximum control and compliance
SOC 2 / Compliance (Enterprise)Advanced compliance features available for regulated industries

What Customer Support Options Does Baseten Offer?

Channels
Available for all plans (support@baseten.co)Available for all plansDedicated support for Pro and EnterpriseDedicated support for Pro and Enterprise
Hours
24/7 for active compute usage support; dedicated support business hours for higher tiers
Response Time
Standard response via email/chat; priority for Pro/Enterprise
Specialized
Hands-on engineering expertise and dedicated forward-deployed engineers for Enterprise
Business Tier
Pro: Priority compute and dedicated Slack/Zoom; Enterprise: Custom SLAs and dedicated support

What APIs and Integrations Does Baseten Support?

API Type
REST API with Model APIs for pre-optimized models and dedicated deployment endpoints
Authentication
API keys and workspace-based authentication (details in docs)
Webhooks
Not explicitly mentioned; focus on polling APIs for inference results
SDKs
Python SDK available; supports major ML frameworks like PyTorch, TensorFlow
Documentation
Comprehensive docs at baseten.co with deployment guides, API references, and examples
Sandbox
Free credits and pay-as-you-go Basic plan for testing; no separate sandbox mentioned
SLA
Custom SLAs for Enterprise; autoscaling with fast cold starts (<1s)
Rate Limits
Higher limits for Pro; unlimited autoscaling based on demand
Use Cases
Production inference for custom/open-source models, embeddings, compound AI systems, high-throughput serving

What Are Common Questions About Baseten?

Baseten uses a pay-as-you-go pricing model with no platform fees. Model APIs are billed based on the number of tokens processed per million, while Dedicated Deployments charge for each minute of active GPU/CPU compute use during active deployment, scaling up/down, and predictions.

Options available include Model APIs optimized for specific models, Dedicated Deployments using a variety of GPU/CPU resources, Self-Hosting in your VPC, and Hybrid Setups. Active capacity is ensured through AutoScaling without paying for unused time.

No, you only pay for active compute time for Deployment, Scaling Up/Down and Predictions. Full control over AutoScaling configuration ensures predictable and preventable costs.

GPU options include T4 ($0.01052/min) to B200 ($0.16633/min) and other CPU options. Tiered Pricing provides priority access to Premium GPUs such as H100.

Yes, SOC 2 Type II and HIPAA compliant across all plans. Enterprise offers advanced security, data residency control, and VPC deployments.

Baseten specializes in ML inference with dedicated hardware options, fast cold starts, and production optimizations. Modal focuses more on general serverless containers with less ML-specific tooling.

Yes, dedicated deployments support any custom, fine-tuned, or open-source model. Model APIs provide instant access to optimized versions of popular models.

Basic plan includes email and in-app chat. Pro adds dedicated Slack/Zoom support. Enterprise provides custom SLAs and forward-deployed engineers.

Is Baseten Worth It?

Baseten is a mature production ML inference platform optimized for high-performance serving of custom and open-source models. Its pay-for-active-use pricing, extensive hardware options, and compliance features make it enterprise-ready, though higher costs suit established teams rather than early prototyping.

Recommended For

  • ML engineering teams deploying production inference at scale
  • Companies needing dedicated GPU infrastructure with autoscaling
  • Enterprise organizations requiring HIPAA/SOC 2 compliance
  • Teams optimizing inference costs for custom models

!
Use With Caution

  • Startups with unpredictable low-volume usage — minimum costs may exceed serverless alternatives
  • Teams needing simple token-based pricing without hardware management
  • Small projects better served by fully-managed model providers
  • Very cost-sensitive prototyping before production

Not Recommended For

  • Non-ML workloads — specialized for inference only
  • Budget-constrained teams under $5K/month spend
  • Casual experimentation — complex setup vs one-click alternatives
  • Teams without ML operations expertise
Expert's Conclusion

Baseten excels for production ML teams prioritizing performance, control, and compliance over simplicity and minimal upfront costs.

Best For
ML engineering teams deploying production inference at scaleCompanies needing dedicated GPU infrastructure with autoscalingEnterprise organizations requiring HIPAA/SOC 2 compliance

What do expert reviews and research say about Baseten?

Key Findings

The Baseten pricing model is based on a use case model that provides Basic as a pay-as-you-go option at no additional platform cost, Pro which has priority access to resources, and Enterprise, which includes VPC and self-hosting options. Baseten has a strong focus on Production ML Inference and does not charge for idle time. It is best suited for an established team of ML practitioners, rather than for prototyping or development.

Data Quality

Good - detailed pricing from official site and AWS Marketplace; support/compliance verified across multiple sources. Limited public info on customer satisfaction ratings and exact response times.

Risk Factors

!
Pricing in the Enterprise tier requires contact with a Sales person and may be subject to a minimum commitment of $5k+
!
The Baseten pricing model is significantly higher for small/medium scale users compared to other developers who use a transparent token-based pricing model.
!
Developers will need to spend their own time to optimize and integrate their models into the Baseten system.
!
Users can expect variable costs depending on the amount of traffic they receive.
Last updated: February 2026

What Are the Best Alternatives to Baseten?

  • •
    Modal: A Serverless GPU platform for both ML and general-purpose computing. Simpler for developers to work with than Baseten's Dedicated Deployments, it is also suitable for prototyping. Baseten and Modal offer a per-second pricing model, however Modal offers less optimization for ML Inference. Modal is best for individual ML researchers and rapid experimentation.
  • •
    WaveSpeedAI: Provides transparent per-use pricing for inference using exclusive models from ByteDance and Alibaba. Costs for small/medium-scale users are lower than the Baseten enterprise minimums, and there are no long-term commitments. However, the user will have less control over the infrastructure used for their models. Suitable for startups requiring predictable pricing for tokens/images/videos.
  • •
    Replicate: A managed service for hosting ML Models where users can find a variety of community models available through a marketplace. Provides a simpler workflow than Baseten's Custom Deployment Process, and users are billed per second for each prediction made. However, users have limited control over the hardware used. Suitable for demonstrating models quickly and for non-technical teams.
  • •
    Together AI: Provides high-performance inference capabilities while supporting Open Source Model frameworks and providing Flexible APIs. Offers competitive pricing to Baseten for most use cases, however, its scaling capabilities may be faster for certain use cases. Has fewer enterprise-focused compliance features. Suitable for cost-sensitive production inference scenarios.
  • •
    Banana.dev: An Auto-Scaling, GPU-serverless platform for ML Inference. Has a more simple pricing structure than Baseten's Hardware Tiers, optimized for Stateful Workflows. However, users do not have as much control over Dedicated Instances. Suitable for Rapid Deployment without Infrastructure Management.
  • •
    Northflank: Kubernetes-based platform supporting both containerized and machine learning workloads. Provides greater flexibility in support of full-stack applications as compared to Baseten’s inference-focused offering; potentially lower cost via use of an existing Kubernetes cluster. Most suitable for DevOps teams developing ML + back-end services. (northflank.com)

What Additional Information Is Available for Baseten?

Infrastructure Specializations

Baseten Embeddings Inference (BEI) is capable of delivering 2x higher throughput and 10% lower latency than competitive products. Optimized for compound AI systems and ultra-low-latency production serving.

Compliance & Security

SOC 2 Type II and HIPAA compliant across all plans. The following enterprise features are available including VPC deployments, data residency control and a variety of advanced security configurations.

Deployment Flexibility

Deployment options exist within Baseten’s cloud, customer VPC, hybrid setup, and additional regions. Complete control exists over autoscaling rules and there are no costs associated with idle time.

Startup Program

Pricing is usage-based, and includes free credits for startup workspaces. All deployment features are accessible without model limitations or platform fees.

AWS Marketplace

Baseten is available on AWS Marketplace with contract prices starting at $5,000 per month. This enables customers to more easily procure Baseten through their existing AWS commitments.

How Does Baseten's Deployment Model Support Matrix Compare?

Deployment ModelCost DriversRequired Tool CapabilitiesComplexity
Third-Party Closed SourceAPI call volume, token limits, rate limitingLow
Third-Party Hosted Open SourceInference endpoint utilization, model compilation time, autoscaling efficiencyMedium
DIY on CloudGPU instance costs, cross-cloud redundancy, Dynamic Workload SchedulingHigh

What Core Optimization Capabilities Does Baseten Offer?

Baseten Inference Stack Optimization

Model engines provide high performance by combining TensorRT-LLM, vLLM, and SGLang with NVIDIA GPUs to achieve maximum throughput.

Cross-Cloud High Availability

Deployment across multiple global clouds occurs utilizing a Dynamic Workload Scheduler which automatically implements failover and cost-efficient scaling.

Real-time Performance Monitoring

Developer workflow-integrated low p99 latencies, throughput metrics, and observability enable developers to monitor performance of their AI workloads.

Automated Model Compilation

A custom model builder increases throughput for optimized large language models (LLMs) via TensorRT-LLM compilation boosts of 60%+.

Compound AI System Optimization

Baseten Chains enables users to have granular hardware control and autoscaling capabilities to achieve 6x better GPU utilization.

NVIDIA Blackwell GPU Optimization

Baseten has demonstrated that it can deliver 225% better cost-performance when serving DeepSeek V3/R1 and Llama models on A4 VMs.

What Multi Cloud Ai Service Integration Does Baseten Offer?

An AI Hypercomputer built using A4 VMs, a Dynamic Workload Scheduler, and NVIDIA Blackwell GPUs optimizes AI inference.

Baseten is a cloud alliance partner enabling users to deploy and scale AI inference workloads.

From TensorRT-LLM, Dynamo, to Blackwell architecture optimization Baseten supports the entire stack of AI development.

Multiple open-source inference engines for peak model performance

Real-time metrics, logs, request traces export for comprehensive monitoring

What Is Baseten's Compliance Security And Governance Standards Status?

Cross-cloud redundancy with automated failover for mission-critical AI services
SOC 2 equivalent security for serving proprietary enterprise AI models
Secure dedicated deployments for custom models alongside shared model APIs
Comprehensive request tracing, metrics, and logs for compliance reporting

How Does Baseten's Business Use Case Alignment Compare?

Use CaseOrganization TypeCritical CapabilitiesExpected ROI Metric
High-Throughput Inference ServingAI-native platforms, SaaS companies225% better cost-performance, TensorRT-LLM optimization, Blackwell GPUs225% improvement in cost-performance ratio for DeepSeek/Llama serving
Latency-Sensitive Real-time AIVoice AI, financial services, mediaLow p99 latency, Baseten Chains compound AI, real-time observability25% better cost-performance while maintaining <100ms response times
Custom Model ProductionizationEnterprises with proprietary LLMsDedicated B200 deployments, automated model compilation, cross-cloud HA60%+ throughput improvement from optimized compilation
Multi-Cloud AI InfrastructureGlobal enterprises requiring redundancyDynamic Workload Scheduler, automated failover, GPU fleet managementZero downtime with spot pricing benefits across providers

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