Baseten
High-performance inference platform for custom AI models in production.
Baseten is the go-to when you need sub-300ms latency and granular GPU control for production GenAI. It's not for the faint of heart—you'll manage your own deployments—but the performance gains are real. If you're a small team just testing ideas, Replicate is simpler, but Baseten delivers for serious workloads.
Verified 1d ago · liveness 97/100 · cite: rightaichoice.com/tools/baseten
- Engineering teams deploying custom LLMs or GenAI models at scale
- Companies requiring sub-300ms latency for real-time transcription or voice agents
- Organizations needing multi-cloud deployment with hybrid flexibility
- Teams building compound AI systems with granular hardware control
- Hobbyists or small teams prototyping with low traffic
- Teams seeking low-cost, pay-per-token inference API with transparent pricing
- Users who need a plug-and-play solution without deep infrastructure management
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Skip Baseten if you need a low-cost, pay-per-token API with transparent, predictable pricing for small-scale projects, or if you lack the engineering expertise to manage GPU infrastructure and prefer a plug-and-play solution.
Going past your free credits on Basic requires pay-as-you-go compute billed per-minute, which can add up quickly at scale; monitor your usage closely.
Baseten's per-minute GPU pricing (from $0.01052/min for T4) suits high-throughput scale, but pay-as-you-go can be costlier for spiky or low-traffic workloads. Compared to Replicate or Together AI, Baseten offers more hardware control; for predictable budgets, consider fixed-rate plans on those platforms.
In short
Baseten — High-performance inference platform for custom AI models in production. Best for Engineering teams deploying custom LLMs or GenAI models at scale, Companies requiring sub-300ms latency for real-time transcription or voice agents, Organizations needing multi-cloud deployment with hybrid flexibility. Free to use.
What's new in Baseten
Checked 9 days agoAcross the latest 5 updates: 4 feature updates and 1 launch.
Inkling Small available on Baseten
Inkling Small is now available through Baseten Model APIs, accessible via OpenAI-compatible endpoint.
Introducing Baseten for Model Labs
Baseten for Model Labs gives labs the infrastructure to bring models to market without building their own serving and distribution systems.
Kimi K3 available on Baseten
Kimi K3 is now available via Model APIs with OpenAI-compatible endpoints; dedicated deployments for larger workloads.
API key management keys
New org-scoped key type automates key administration, allowing creation, listing, and revocation of team API keys via API.
Observability APIs updates
Pull logs, metrics, and audit logs programmatically through the Management API for any deployment or environment.
Viability Score
How well maintained and how widely used is Baseten? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Dedicated GPU inference with T4, L4, A10G, A100, H100, B200
- Pre-optimized Model APIs with OpenAI-compatible endpoints
- Baseten Chains for compound AI with hardware autoscaling
- Real-time audio streaming for low-latency text-to-speech
- Optimized transcription and speaker diarization
- Rapid image generation with custom models or ComfyUI workflows
- Baseten Embeddings Inference with 2x throughput and 10% lower latency
- Training on GPU instances with one-click deployment to production
- CLI for deploying, calling, streaming logs, and metrics
- MCP server integration for coding agents
- Observability APIs for logs, metrics, and audit logs
- Org-scoped API key management via API
- Self-hosted and hybrid deployments (VPC + cloud)
- Baseten for Model Labs to distribute and monetize models
- 99.99% uptime and fast cold starts
About Baseten
Baseten is a high-performance inference platform for engineering teams who need to deploy custom, open-source, or fine-tuned AI models in production. It's built for applications that demand ultra-low latency and high throughput, like real-time transcription, voice agents, and image generation. The platform gives you granular control over dedicated GPU compute—from T4 to B200—so you can match hardware to your workload's needs. Pre-optimized Model APIs provide instant access to cutting-edge models like Kimi K3, DeepSeek V4 Pro, and GLM 5.2 Fast via OpenAI-compatible endpoints, with per-token pricing that's often a fraction of frontier APIs. The core of the platform is the Baseten Inference Stack, which combines custom kernels, advanced decoding, and caching to deliver speed out of the box. Baseten Chains enables compound AI with hardware autoscaling that improves GPU usage by 6x and cuts latency in half. You also get real-time audio streaming for text-to-speech, optimized transcription with speaker diarization, and Baseten Embeddings Inference that boasts 2x throughput and 10% lower latency. The stack runs across any cloud or region, with self-hosted and hybrid options for enterprises that need to keep data in their own VPC. For developers, Baseten shines with a CLI for deploying and managing models, an MCP server for coding agent integrations, and observability APIs to pull logs, metrics, and audit data. New org-scoped API key management automates key administration, and Model Labs lets you distribute and monetize your own models on the same infrastructure. Baseten is SOC 2 Type II certified and HIPAA compliant, making it a strong candidate for regulated industries. Compared to managed APIs like Replicate or Together AI, Baseten goes further on infrastructure control and performance tuning. It's a managed service, but it expects you to understand deployment trade-offs. If you need the raw speed and hardware flexibility for a serious production workload, Baseten
Behind the Verdict
Baseten is the kind of platform you graduate to. When you've outgrown prototyping and need consistent performance at scale, it delivers. The headline is sub-300ms transcription latency, which ClickUp and others have confirmed, and the per-token pricing on Model APIs like DeepSeek V4 Pro at $1.74 input / $3.48 output per 1M tokens is aggressive. If you're building a voice agent or real-time feature, that speed matters more than saving a few cents per call. The trade-off is that Baseten expects you to own the deployment. The CLI, the autoscaling config, the GPU selection—it's all there, but it's on you to get it right. That's fine if you have an infrastructure-minded engineer. If you don't, you'll be learning Truss on the job. Together AI is more of a plug-and-play token API, but you lose the ability to run your own fine-tuned model on your choice of GPU. Where Baseten really shines is compound AI. Chains autoscaling with hardware-aware routing is a differentiator—you get 6x GPU utilization and half the latency compared to juggling single calls. For teams building multi-step pipelines, that's a huge efficiency win. But watch out for costs. Pay-as-you-go compute down to the minute means you need to be disciplined about scaling. Idle time is free, but if you forget to scale down, the bills add up. The Basic tier is fine for a small start, but volume discounts kick in on Pro and Enterprise, so talk to sales before going all in. Also, if your traffic is spiky and unpredictable, you might find yourself price-exploring. For regulated industries, Baseten's self-hosted option is a strong card. You get the managed DevEx inside your own VPC, which is rare. But that comes with a sales conversation, not a self-serve button. In practice, we'd reach for Baseten when we need both
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Real-world workflow fit
Concrete scenarios for the personas Baseten actually fits — and what changes day-one when you adopt it.
Deploy a fine-tuned LLM for a customer-facing chatbot with sub-300ms latency.
Outcome: Deploy the model on a dedicated A100 GPU, achieving sub-300ms latency with autoscaling, and integrate with an OpenAI-compatible endpoint; monitor via observability APIs within a day.
Run real-time transcription and speaker diarization for telehealth calls.
Outcome: Use a pre-optimized Whisper model via Model APIs, achieving sub-300ms latency; scale seamlessly during peak hours, ensuring HIPAA compliance with SOC 2 Type II.
Bring a proprietary model to market without building serving infrastructure.
Outcome: Use Baseten for Model Labs to distribute and monetize the model, with dedicated deployments and a custom endpoint, going live within days.
Use Cases
- Deploy a fine-tuned LLM for a customer-facing chatbot with sub-300ms latency
- Serve real-time image generation with ComfyUI workflows
- Run transcription and speaker diarization at scale
- Monetize a custom model through the Frontier Gateway
- Train a model on GPU instances and deploy in one click
- Migrate from on-prem inference to a self-hosted VPC environment
- Build compound AI systems with granular hardware control (Baseten Chains)
- Stream ultra-low-latency text-to-speech for voice agents
Models Under the Hood
as of 2026-08-14
Limitations
- Baseten is a developer-focused inference platform for deploying and serving custom models, with pre-optimized Model APIs available at per-token costs.
- Self-hosted deployments require significant engineering effort.
- Pricing scales with usage, and advanced features like dedicated compute are on higher tiers.
as of 2026-08-14
Verification history
We have re-verified Baseten 18 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Baseten tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Basic
$0/mo
Ideal for
Small teams or individuals experimenting with inference, with pay-as-you-go pricing and free credits to get started.
What this tier adds
Free entry point with dedicated deployments and Model APIs; you pay only for compute time used.
Pro
Volume discounts available
Ideal for
Growing teams needing priority GPU access and higher rate limits, with volume discounts.
What this tier adds
Adds priority access to high-demand GPUs, dedicated compute, and hands-on engineering support via Slack and Zoom.
Enterprise
Volume discounts available
Ideal for
Large organizations requiring custom SLAs, self-hosting, and full control over data residency.
What this tier adds
Offers custom SLAs, self-host deployments, on-demand flex compute, and advanced RBAC with Teams.
Where the pricing makes sense
The company stage and team size where Baseten's pricing actually pencils out — and where peers do it cheaper.
Baseten's per-minute GPU pricing (from $0.01052/min for T4) suits high-throughput scale, but pay-as-you-go can be costlier for spiky or low-traffic workloads. Compared to Replicate or Together AI, Baseten offers more hardware control; for predictable budgets, consider fixed-rate plans on those platforms.
Setup time & first value
How long it actually takes to get something useful out of Baseten — broken out by persona, not the marketing-page minute.
Basic setup: deploy your first model via CLI or dashboard in under 10 minutes, with free credits for testing. Self-hosted: allow 1-2 weeks for VPC configuration and optimization. Model Labs: setup time varies, typically 1-3 days for endpoint creation.
Switching to or from Baseten
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Replicate or Together AI: Export your model weights and use Truss to package them, then deploy on Baseten with dedicated GPUs for lower latency and more control.
- ↗To a self-managed GPU cluster: Download your model artifacts and deploy on your own K8s with vLLM or TensorRT-LLM, but losing managed autoscaling and monitoring.
Integrations
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