Baseten
Baseten is a production inference platform for deploying open-source, fine-tuned, and custom AI models on dedicated GPUs, with per-minute billing and no charge
Baseten earns its place when inference is already a business problem — a latency SLO you can't hit, a GPU bill you can't flatten, or a model you can't serve through a token-priced API. Named features justify the pick: per-minute dedicated deployments with no idle billing, Baseten Chains for compound AI (the vendor claims 6x GPU utilization and half the latency), and Baseten Embeddings Inference at 2x throughput and 10% lower latency. If you only need token-priced calls, a Model API on Baseten or a peer like Together AI or Replicate covers you with less surface area to own.
Verified 8d ago · liveness 93/100 · cite: rightaichoice.com/tools/baseten
- Engineering teams serving proprietary, fine-tuned, or open-source models in production
- Voice, transcription, and embeddings products with tight latency requirements
- Teams that need dedicated GPUs, autoscaling, and VPC or hybrid deployment
- Model labs that want to train, serve, and distribute models on one platform
- Hobbyists or small teams with low, spiky traffic that a serverless token API handles cheaper
- Buyers who want a plug-and-play endpoint and no deployment pipeline to own
- Workloads that tolerate high latency and don't need dedicated GPU control
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Skip Baseten if your traffic is low and spiky and a token-priced serverless API already meets your latency target, or if you don't want to own model packaging, autoscaling, and observability yourself.
Dedicated GPU billing runs per minute of compute, so an idle-but-scaled deployment policy that keeps replicas warm still shows up on the invoice even though idle time itself isn't charged.
Basic is $0/mo plus pay-as-you-go, which fits prototype and early-production teams testing on Model APIs before they commit GPU spend — new accounts come with credits. Pro adds priority GPU access, dedicated compute, higher Model API rate limits, and Slack/Zoom support at negotiated volume discounts, which suits funded startups and scale-ups whose inference spend is now a line item. Enterprise adds custom SLAs, self-hosting, flex compute, and data residency control. Against token-only peers
In short
Baseten — Baseten is a production inference platform for deploying open-source, fine-tuned, and custom AI models on dedicated GPUs, with per-minute billing and no charge. Best for Engineering teams serving proprietary, fine-tuned, or open-source models in production, Voice, transcription, and embeddings products with tight latency requirements, Teams that need dedicated GPUs, autoscaling, and VPC or hybrid deployment. Free to use.
What's new in Baseten
Checked 8 days agoAcross the latest 5 updates: 2 feature updates, 1 launch, 1 changelog entry and 1 news mention.
Upcoming change to Baseten API key format
Starting October 1 at 15:00 UTC, newly created Baseten API keys add a b10_ prefix so they are easier to identify in code, logs, and secret scanners.
Web search with Baseten Hosted Tools
Baseten Hosted Tools adds server-side web search to Model APIs through Baseten Grounded Inference, letting models search and fetch current web content via Exa, Keenable, Parallel, or You.com.
Baseten CLI 1.0.0
The Baseten CLI reaches general availability at version 1.0.0, marking the command surface as stable.
Model API Deprecation (GLM 4.7, Kimi K2.7, Kimi K2.6, Inkling, Inkling Small, DeepSeek v4 Pro)
Six Model API endpoints, including GLM 4.7 and DeepSeek v4 Pro, are deprecated at 5pm PT on September 25, so anything pinned to them needs a migration plan.
OIDC and AWS AssumeRole for training jobs
Training jobs can use OIDC or AWS AssumeRole during setup to pull private container images and download model weights or training data without storing long-lived cloud credentials in Baseten.
What people actually say about Baseten — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
20 mentions across 4 sources (Hacker News, YouTube, Product Hunt, Lemmy), 43 more we could not attribute · researched Sep 14, 2026.
Weighted by the 63 posts each of 4 sources contributed.
- +Named alongside Modal, Fireworks, and Together as a credible independent inference host
- +Former engineer confirms zero-data-retention is genuinely enforced, not just marketing
- +Dedicated GPU options from T4 to B200 give fine-grained hardware control
- +Pre-optimized Model APIs with OpenAI-compatible endpoints simplify frontier-model access
- +Product Hunt backers say it can save thousands of engineering hours on deployment
- −A customer reportedly obtained admin access to Baseten's GitHub, raising serious security questions
- −Thin ~$15M ARR against a $5B valuation invites skepticism about long-term stability
- −Advanced, CLI-first platform with little hand-holding — unsuitable for non-engineers
- −Recent Bloomberg coverage generated more valuation snark than product substance
- −No independent Reddit, Stack Overflow, or GitHub data to verify reliability claims
- • Self-managed deployments shift operational cost back onto your team
- • Per-task economics can lose to cheaper competitors on simpler workloads (HN price comparisons)
- • Dedicated GPU instances cost far more than shared/auto-scaled API calls at low volume
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: October 2026
How we score →Key Features
- Dedicated GPU inference on T4, L4, A10G, A100 80 GiB, H100 80 GiB, H100 MIG, and B200 180 GiB instances
- Per-minute GPU billing with no charge for idle time — you pay only for deploying, scaling, and predicting
- Pre-optimized Model APIs served through OpenAI-compatible endpoints, priced per 1M tokens
- GLM-5.3 with a 1M-token context window, plus GLM 5.3 Fast and GLM-5.3-Flash variants
- DeepSeek V4.1 Flash (552B-parameter multimodal), DeepSeek V4 Pro 0813, and Kimi K3 via Model APIs
- Baseten Hosted Tools server-side web search for Model APIs via Exa, Keenable, Parallel, or You.com
- Baseten Chains for compound AI with per-step hardware selection and autoscaling
- Real-time audio streaming for text-to-speech targeting voice agents and AI phone calls
- Transcription with speaker diarization, cited at sub-300ms latency by a Baseten customer
- Image generation with custom models or ComfyUI workflows
- Baseten Embeddings Inference with over 2x throughput and 10% lower latency
- Training on on-demand GPU instances with the Loops SDK and one-click deploy to inference
- Deploy any framework via Truss, the open-source model packaging standard
- Runtime OIDC and AWS AssumeRole authentication for builds and training jobs using short-lived credentials
- Regional deployments that restrict every replica to a chosen Baseten region for data residency
About Baseten
Baseten is a production inference platform for engineering teams that treat model serving as part of the product. You deploy open-source, fine-tuned, or fully custom models on dedicated GPU instances — T4 through B200 — billed per minute of compute, with no charge for idle time, fast cold starts, and autoscaling. Workloads run in Baseten Cloud, in your own VPC, or in a hybrid setup with on-demand flex capacity from Baseten Cloud and your existing cloud commitments. The other half of the platform is Model APIs: pre-optimized endpoints for GLM-5.3 (Fast and Flash variants), DeepSeek V4.1 Flash, DeepSeek V4 Pro 0813, Kimi K3, NVIDIA Nemotron 3 Ultra, and GPT OSS 120B, priced per 1M tokens with cache input at roughly a tenth of standard input. GLM-5.3 publishes a 1M-token context window. As of September 2026, Baseten Hosted Tools adds server-side web search to Model APIs through Baseten Grounded Inference, with Exa, Keenable, Parallel, or You.com as search providers. The Baseten Inference Stack bakes in custom kernels, advanced decoding, and caching. Baseten Chains handles compound AI with per-step hardware and autoscaling; Baseten Embeddings Inference reports over 2x throughput and 10% lower latency; transcription ships speaker diarization and real-time audio streaming aims at voice agents and AI phone calls. Training runs on the same on-demand GPU instances using the Loops SDK, with OIDC or AWS AssumeRole used to pull private images and weights. Compliance covers SOC 2 Type II and HIPAA. It sits above Replicate and Together AI on infrastructure control, and below running your own Kubernetes inference stack on operational burden.
Behind the Verdict
Baseten's strongest argument is that it names a specific cost and a specific latency headroom rather than a general promise. The pricing page lists GPU rates down to the minute — T4 at $0.01052, B200 180 GiB at $0.16633 — and states plainly that you don't pay for idle time, only for deploying, scaling, and predicting. For a team whose GPU bill is the constraint, that is a concrete lever. Model APIs are the low-commitment entry point: GLM-5.3-Flash at $0.15 per 1M input and $0.50 output, DeepSeek-V4-Flash-0731 at $0.13/$0.26, GPT OSS 120B at $0.10/$0.50, with cache input as low as $0.007 on DeepSeek V4.1 Flash. You can benchmark a workload on tokens before you rent a card. The engineering surface is broader than a plain model host. Baseten Chains gives compound AI per-step hardware and autoscaling. Baseten Embeddings Inference claims over 2x throughput and 10% lower latency than alternatives. Transcription adds speaker diarization, and real-time audio streaming targets voice agents — the vendor's own customer quotes cite sub-300ms transcription and 160ms latency figures. Training runs on the same stack via the Loops SDK, so train-then-deploy doesn't mean a second pipeline. September 2026 additions are security-grade rather than cosmetic: Runtime OIDC and AWS AssumeRole for builds and training jobs remove long-lived cloud credentials, regional deployments pin every replica to a chosen region for data residency, and a Viewer role gives read-only access without deployment rights. Three things to weigh. First, dedicated deployments mean you own a serving pipeline — Truss packaging, autoscaling policy, observability — which is the cost of control. Second, the Model API lineup moves fast in both directions: DeepSeek V4.1 Flash, GLM 5.3 Fast, and Nemotron 3 Ultra arrived recently, while GLM 4.7, Kimi K2.7/K2.6, Inkling, and DeepSeek v4 Pro were deprecated on September 25, 2026, so anything pinned to a retired endpoint needs a migration plan. Third, GPU availability beyond listed regions and negotiated compute discounts run through a sales conversation, which matters if procurement wants published numbers for every region. Where it fits: voice, transcription, embeddings, image generation with ComfyUI workflows, and proprietary fine-tunes at sustained load. Where it doesn't: low, spiky traffic that a serverless token API absorbs for less, and teams that want an endpoint with no pipeline attached.
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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.
You fine-tune a customer-facing model and need sub-300ms responses at sustained load. You package it with Truss, deploy on dedicated A100 or H100 instances, set autoscaling, and watch p50/p99 latency and per-minute GPU spend in the dashboard.
Outcome: You get consistent latency on hardware you control and a bill that tracks actual compute minutes rather than tokens you can't forecast.
Before renting GPUs, you run the workload against Model APIs — DeepSeek V4.1 Flash or GLM-5.3-Flash — with server-side web search via Baseten Hosted Tools, then pull per-day spend from GET /v1/billing/model_apis.
Outcome: You pick a model and prompt design on real usage data, then move the winner to a dedicated deployment when volume justifies the card.
You need inference in your own VPC, every replica pinned to an approved region, short-lived cloud credentials instead of stored keys, and read-only access for auditors.
Outcome: Runtime OIDC plus AWS AssumeRole, regional deployments, and the Viewer role let you meet the security review without running a Kubernetes inference stack in-house.
Use Cases
- Serve a fine-tuned LLM for a customer-facing chatbot with sub-300ms latency
- Stream ultra-low-latency text-to-speech for voice agents and AI phone calls
- Run transcription and speaker diarization at scale without latency spikes
- Serve real-time image generation with ComfyUI workflows
- Benchmark a new model on a Model API before committing dedicated GPU spend
- Train a model on GPU instances and deploy it in one click on the same stack
- Build compound AI systems with per-step hardware control via Baseten Chains
- Pin replicas to a specific region to satisfy data residency requirements
Models Under the Hood
as of 2026-09-15
Limitations
- Baseten is a developer-focused inference platform, so you own a real serving pipeline: model packaging (typically Truss), autoscaling behavior, and observability.
- Pricing is usage-based, and priority GPU access, higher Model API rate limits, negotiated compute discounts, custom SLAs, self-hosted and hybrid deployment, advanced RBAC with Teams, and custom global regions sit behind the Pro and Enterprise tiers.
- Compute in countries and regions outside the listed set requires a sales conversation rather than a published rate.
- The Model API catalog turns over quickly — GLM 4.7, Kimi K2.7, Kimi K2.6, Inkling, Inkling Small, and DeepSeek v4 Pro were deprecated on September 25, 2026 — so pinned endpoints need a migration plan.
as of 2026-09-29
Verification history
We have re-verified Baseten 20 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
- — 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
- — 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
Showing the 6 most recent of 20 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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 + pay as you go
Ideal for
Prototype and early-production engineers who want to test Model APIs and spin up dedicated deployments without a contract.
What this tier adds
Starting tier at $0/mo plus pay-as-you-go: dedicated deployments, Model APIs, training, fast cold starts, SOC 2 Type II and HIPAA compliance, and email and in-app chat support.
Pro
Volume discounts available
Ideal for
Funded startups and scale-ups whose inference is now production-critical and who hit GPU contention or Model API rate limits on Basic.
What this tier adds
Adds priority access to high-demand GPUs, dedicated compute, higher Model API rate limits, hands-on engineering expertise, and Slack/Zoom support, with volume discounts available.
Enterprise
Volume discounts available
Ideal for
Regulated enterprises and platform teams that need inference in their own VPC, custom SLAs, and control over data residency and regions.
What this tier adds
Adds custom SLAs, self-hosted VPC deployments, on-demand flex compute, use of existing cloud commitments, advanced security and compliance, advanced RBAC with Teams, and custom global regions.
Where the pricing makes sense
The company stage and team size where Baseten's pricing actually pencils out — and where peers do it cheaper.
Basic is $0/mo plus pay-as-you-go, which fits prototype and early-production teams testing on Model APIs before they commit GPU spend — new accounts come with credits. Pro adds priority GPU access, dedicated compute, higher Model API rate limits, and Slack/Zoom support at negotiated volume discounts, which suits funded startups and scale-ups whose inference spend is now a line item. Enterprise adds custom SLAs, self-hosting, flex compute, and data residency control. Against token-only peers
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.
Model APIs are the fastest path in — new accounts include credits, so you can call an endpoint within minutes of signing up. A first dedicated deployment via Truss is typically an afternoon for someone comfortable with container packaging, plus tuning time for autoscaling. Enterprise VPC or hybrid deployments run through forward-deployed engineering and will take longer, since network, IAM, and
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: repackage your model with Truss and deploy to dedicated instances when per-prediction pricing stops scaling.
- →From Together AI: move token-priced endpoints to Baseten Model APIs, then graduate high-volume workloads to dedicated GPUs for cost and latency control.
- →From a self-run Kubernetes inference stack: keep the model and drop the cluster ops by self-hosting Baseten in your VPC or running in Baseten Cloud.
- →From OpenAI or another hosted frontier API: shift open-weight and fine-tuned workloads to Model APIs or dedicated deployments when token spend or data residency demands it.
- →From Modal or RunPod: point the same containerized model at Baseten's inference stack and pick up autoscaling, cold-start, and observability tooling.
- ↗To your own Kubernetes stack: export the containerized model and rebuild autoscaling, observability, and GPU scheduling in-house if you want full control.
- ↗To a token-only provider: move simple workloads back to a serverless token API when you no longer need dedicated GPU instances or a serving pipeline.
- ↗To a peer inference platform: re-deploy the same containers on Together AI or Replicate if you want less operational surface area and don't need VPC deployment.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Baseten”, and we withheld 6: 6 could not be judged, because “Baseten” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Baseten.
Official links
Tools that pair well with Baseten
Common stack mates teams adopt alongside Baseten, with the specific reason each pairing earns its keep.
Together AI
Together AI is an AI cloud for running open-source models — serverless inference, batch jobs, GPU clusters, and fine-tuning on one bill.
Modelscope
ModelScope is Alibaba Cloud's open-source Model-as-a-Service hub for finding, fine-tuning, and deploying AI models.
Inference Engine by GMI Cloud
Multimodal AI inference platform with OpenAI-compatible APIs, dedicated GPUs, and day-zero frontier models like Qwen3.8-Max and Kimi K3.
Featured Head-to-Head Comparisons
Alternatives to Baseten
View allTogether AI
Together AI is an AI cloud for running open-source models — serverless inference, batch jobs, GPU clusters, and fine-tuning on one bill.
Modelscope
ModelScope is Alibaba Cloud's open-source Model-as-a-Service hub for finding, fine-tuning, and deploying AI models.
Inference Engine by GMI Cloud
Multimodal AI inference platform with OpenAI-compatible APIs, dedicated GPUs, and day-zero frontier models like Qwen3.8-Max and Kimi K3.
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