An AI inference platform from Baseten Labs, Inc. (United States). It lets you publish trained open-source models and your own fine-tuned models as production-ready APIs. Beyond LLMs, it covers a wide range of modalities including image generation, transcription, speech synthesis, and embeddings, and its main selling points are GPU autoscaling and high availability through a multi-cloud setup. It also offers “Model APIs” that give you instant access to major open models such as DeepSeek, Kimi, GLM, and gpt-oss, so you can test performance and cost before bringing your own model. The platform is SOC 2 Type II certified and HIPAA compliant, and you can choose between cloud, self-hosted, and hybrid deployment.
Key Features
- Turn open models into production APIs: Deploy models published on places like Hugging Face, or models you fine-tuned in house, as inference endpoints. Packaging is handled through the open-source Truss framework
- Instant access to major open models via Model APIs: Popular models such as DeepSeek, Kimi, GLM, and gpt-oss are served pre-optimized as token-billed APIs. You can try them without provisioning any infrastructure
- GPU autoscaling with no charge for idle time: GPUs scale with request volume, and you are not billed while they sit idle. Baseten also puts significant effort into reducing cold starts
- Multi-cloud, multi-region high availability: You are not locked into a single cloud or region, and inference can be placed across multiple environments. Baseten advertises 99.99% uptime
- Broad modality coverage: In addition to text generation, it covers image generation (including ComfyUI workflows), transcription with speaker diarization, real-time speech synthesis, and embeddings (Baseten Embeddings Inference)
- Deployment choice and compliance: Pick fully managed Baseten Cloud, self-hosting inside your own VPC, or a hybrid of the two. SOC 2 Type II certification and HIPAA compliance are supported, and single-tenant configurations are available
Pricing
There are three tiers. Basic has no fixed monthly fee and is pay-as-you-go. Your actual cost comes down to how many hours you run a given GPU, or how many tokens you process through Model APIs.
| Plan | Monthly price | Main features |
|---|---|---|
| Basic | $0 (pay-as-you-go) | Dedicated deployments, Model APIs, training, SOC 2 Type II / HIPAA compliance, email and in-app chat support |
| Pro | Contact sales (volume discounts available) | Everything in Basic plus priority GPU access, dedicated compute, hands-on engineering support, Slack / Zoom support |
| Enterprise | Contact sales (volume discounts available) | Everything in Pro plus custom SLAs, self-hosted deployment, advanced security, custom regions, and team-level RBAC |
Hourly rates for the main GPUs (dedicated deployments) are as follows.
| GPU | Hourly rate |
|---|---|
| T4 | $0.6312 |
| L4 | $0.8484 |
| A100 | $4.00 |
| H100 | $6.50 |
| B200 | $9.98 |
Model APIs are billed per 1M tokens, with rates that vary by model (for example, GPT OSS 120B is $0.10 input / $0.50 output, DeepSeek V4 Flash is $0.13 input / $0.26 output, and Kimi K3 is $3.00 input / $15.00 output). New accounts come with starter credits.
Pricing is current as of August 2026. Please check the official site for the latest information.
Pros & Cons
✅ Pros
- Turn open models or your own fine-tuned models into production APIs without procuring GPUs or designing orchestration yourself
- Pay-as-you-go billing with no charge for idle time makes costs easier to predict in early phases with unstable traffic
- You can start with off-the-shelf models through Model APIs and move to dedicated deployments only when you need to
- Image, audio, and embedding workloads live on the same platform as LLMs, so multi-modality applications can be consolidated in one place
- SOC 2 Type II and HIPAA compliance plus self-hosting make it viable in demanding domains such as healthcare and finance
⚠️ Cons
- Because billing centers on GPU hours, always-on large models can become expensive on a monthly basis
- Pro and Enterprise prices are not published, so a serious rollout starts with a quote negotiation
- Packaging and optimizing models requires machine learning and infrastructure knowledge; this is not a no-code service
- The console and documentation are primarily in English, and Japanese-language material is limited
Comparison with Similar Services
| Criteria | Baseten | Modal | Together AI | Replicate |
|---|---|---|---|---|
| Main positioning | Production inference for your own models | General-purpose serverless GPU runtime | Open model APIs plus training infrastructure | Easy API wrapper for public models |
| Off-the-shelf model APIs | Model APIs (major open models) | Not the main focus | Extensive | Extensive |
| Bring your own model | Yes (Truss) | Yes (per Python function) | Yes | Yes (Cog) |
| Self-hosted / VPC deployment | Yes (Enterprise) | Partially | Offered for enterprise | Generally not available |
| Billing model | GPU hours plus token usage | Per second of execution | Tokens / GPU hours | Per second of execution |
Who Is It For
- Teams that want to ship a fine-tuned open model as a production service, internally or to customers
- Developers moving from closed commercial APIs to their own models to control unit cost and latency
- Anyone building products that involve non-text inference such as transcription, speech synthesis, or image generation
- Companies with compliance requirements like SOC 2 or HIPAA that also want in-VPC inference as an option
- People who want to gauge the cost of off-the-shelf open models through Model APIs first, then move to their own models step by step
Summary
Baseten is a platform for taking open models and in-house models from “a demo that works” to “a production API that stays up.” It shifts the operational burden of GPU procurement, scaling, and monitoring onto the platform, but because billing is based on GPU hours, running always-on without understanding your load profile translates directly into cost. A practical path is to measure the quality and cost of off-the-shelf models through Model APIs first, then move to dedicated deployments once bringing your own model clearly pays off.