Baseten
Generative AI infrastructure software
Generative AI software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Baseten
Baseten is a managed platform for deploying, serving, and scaling machine learning and generative AI models in production. It targets ML engineers and platform teams that need low-latency inference endpoints, GPU-backed compute, and operational tooling without building a full serving stack in-house. The product focuses on model serving and inference operations, including packaging models, autoscaling, and monitoring, and it can support both open-source and custom models. Baseten is typically used to power real-time AI features in applications and internal services.
Production-grade model serving
Baseten provides infrastructure to deploy models as managed endpoints with operational controls geared toward production inference. It addresses common serving needs such as versioning, rollout management, and keeping endpoints available under variable traffic. This aligns with teams that need to operationalize models rather than experiment in notebooks. Compared with broader data/AI platforms, it is more focused on inference deployment and runtime operations.
GPU infrastructure abstraction
Baseten abstracts GPU provisioning and runtime configuration so teams can deploy models without directly managing clusters. This can reduce the engineering work required to set up and maintain inference environments across different model types. It is useful for workloads that require accelerators and careful resource sizing. The platform orientation fits organizations that want managed infrastructure rather than self-managed serving components.
API-first integration approach
Baseten exposes model endpoints that can be integrated into applications and services through standard API patterns. This supports embedding generative AI capabilities into products, internal tools, or automation workflows. The integration model is straightforward for software teams that already operate API-based services. It also supports iterative updates by allowing teams to redeploy models and adjust configurations without rewriting application logic.
Not an end-to-end AI suite
Baseten focuses on deployment and inference, not the full lifecycle of data preparation, feature engineering, and model training. Organizations that need a single platform for data science workflows, governance, and analytics may require additional tools. This can increase integration work across the ML toolchain. It is better positioned as a serving layer than as a comprehensive AI platform.
Platform lock-in considerations
Using a managed serving platform can create dependency on vendor-specific deployment workflows, runtime assumptions, and operational tooling. Migrating endpoints to another environment may require repackaging models and reworking CI/CD and observability integrations. This is a common trade-off for managed infrastructure. Buyers typically evaluate portability requirements and exit plans early.
Cost and capacity variability
Inference workloads—especially GPU-backed generative AI—can be sensitive to traffic patterns and model size, which can make costs less predictable. Teams may need to invest time in performance tuning, batching, and scaling policies to control spend and latency. If workloads spike, capacity planning and quota management can become a constraint. These operational factors can be material for high-volume, real-time use cases.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Basic | $0 per month (pay-as-you-go) | Dedicated deployments; Model APIs; Fast cold starts; SOC 2 Type II & HIPAA compliant; Email & in-app chat support. |
| Pro | Quote (volume discounts available) | Everything in Basic plus: priority access to high-demand GPUs; dedicated compute; higher Model API rate limits; hands-on engineering expertise; dedicated support (Slack & Zoom). |
| Enterprise | Custom pricing (contact sales) | Everything in Pro plus: custom SLAs; training; self-host deployments; on-demand flex compute; ability to use existing cloud commitments; full control over data residency; advanced security & compliance; custom global regions. |
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Usage-based details (published on Baseten official pricing page):
Pricing model: Pay-as-you-go
Model API (price per 1M tokens)
- MiniMax M2.5 — Input: $0.30; Output: $1.20
- Kimi K2.5 — Input: $0.60; Output: $3.00
- Kimi K2 Thinking — Input: $0.60; Output: $2.50
- Kimi K2 Instruct — Input: $0.60; Output: $2.50
- GLM 4.7 — Input: $0.60; Output: $2.20
- GLM 4.6 — Input: $0.60; Output: $2.20
- GPT OSS 120B — Input: $0.10; Output: $0.50
- DeepSeek V3.1 — Input: $0.50; Output: $1.50
- DeepSeek V3 0324 — Input: $0.77; Output: $0.77
(Prices listed per 1 million tokens; volume discounts available — contact Baseten.)
Dedicated deployments (compute billed down to the minute) (Prices shown are per minute; hourly-equivalent can be derived by multiplying by 60.)
GPU instances — Price per minute:
- T4 (16 GiB) — $0.01052/min
- L4 (24 GiB VRAM) — $0.01414/min
- A10G (24 GiB) — $0.02012/min
- A100 (80 GiB) — $0.06667/min
- H100 MIG (40 GiB) — $0.0625/min
- H100 (80 GiB) — $0.10833/min
- B200 (180 GiB) — $0.16633/min
CPU instances — Price per minute:
- 1x2 (1 vCPU, 2 GiB) — $0.00058/min
- 1x4 (1 vCPU, 4 GiB) — $0.00086/min
- 2x8 (2 vCPU, 8 GiB) — $0.00173/min
- 4x16 (4 vCPU, 16 GiB) — $0.00346/min
- 8x32 (8 vCPU, 32 GiB) — $0.00691/min
- 16x64 (16 vCPU, 64 GiB) — $0.01382/min
Training: On-demand training compute uses the same instance pricing (per minute). Volume discounts available.
Notes: New Baseten accounts receive free credits for testing/deployment (official docs). Billing initially occurs when usage exceeds $50 or at the end of the month. Volume/education/non-profit discounts are available via support.
Seller details
Baseten, Inc.
San Francisco, CA, USA
2019
Private
https://www.baseten.co/
https://x.com/basetenlabs
https://www.linkedin.com/company/baseten/
