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Lamini pricing, features, company info, and alternatives
A factual product page for Lamini as an enterprise LLM platform for tuning, inference, and self-hosted deployment.
Last updated April 2026 · Pricing and features verified against official documentation
Pricing
Current public pricing tiers on file for Lamini, last verified Apr 26, 2026.
On-Demand
Usage-based / usage
Includes $300 in free credit; the pricing page lists $0.50 per 1M inference tokens and $0.50 per tuning step.
Reserved
Custom
Dedicated GPUs hosted on Lamini's infrastructure, with unlimited tuning and inference plus enterprise support.
Self-Managed
Custom
Run Lamini in your own secure environment on your GPUs, including VPC, on-prem, and air-gapped deployments; pay per software license.
What You Can Do With It
The main capabilities that shape how people use Lamini today.
Memory Tuning and Memory RAG focus on factual accuracy for proprietary-data workflows, with structured JSON output support in the docs.
The platform exposes a Python client, REST API, OpenAI-compatible inference path, and web UI.
Deployment models cover on-demand usage, reserved GPUs, and self-managed infrastructure, including VPC, on-prem, and air-gapped options.
The supported-model docs cover on-demand open-source model families and self-managed Hugging Face CausalLM support.
Best For
Who Lamini is most clearly built for.
Developers and startups that want an SDK, API, and free-credit entry point for fine-tuning or inference.
Enterprise teams that need secure deployment options, including VPC, on-prem, or air-gapped environments.
Teams building text-to-SQL, classification, document reasoning, or other mini-agent workflows on proprietary data.
Company
Leadership and company context for Lamini Inc..
CEO
Sharon Zhou
Founders
Sharon Zhou, Greg Diamos
Investors
Amplify Partners, First Round Capital, Andrew Ng, Andrej Karpathy, AMD Ventures
Platforms
Where you can use Lamini today.
Web
API
Python SDK
Self-hosted
Privacy Notes
Publicly stated data-handling notes that matter when evaluating Lamini.
Lamini's privacy policy says the company collects account, billing, device, usage, and uploaded-content data to provide and secure the service.
The terms and privacy policy say self-managed deployments can run inside customer-controlled environments, including VPC, on-prem, and air-gapped setups.
The privacy policy says Lamini may share data with service providers and affiliates as part of operating the platform.
Access
How to integrate or build around Lamini.
Public API
Yes
Docs
Available
Alternatives
Other tools worth considering alongside Lamini.
AI infrastructure platform for running, fine-tuning, and training open-source models.
Developer platform for fine-tuning and serving open-source LLMs.
Platform for logging, fine-tuning, evaluating, and hosting LLMs.
Inference and training platform for serving open-source, fine-tuned, and custom AI models.
Product Snapshot
Lamini is an enterprise LLM platform for tuning, inference, and deployment. Its public docs cover Memory Tuning, Memory RAG, classifier tooling, OpenAI-compatible inference, and deployment modes that include on-demand, reserved, and self-managed infrastructure.
What You Can Do With It
- Use the Python SDK, REST API, or web UI to run inference and structured JSON output workflows.
- Build Memory Tuning, Memory RAG, and classifier workflows for proprietary data.
- Choose on-demand, reserved GPU, or self-managed deployment models.
- Work with supported open-source model families in on-demand, or use Hugging Face CausalLM models in self-managed and reserved setups.
Pricing
- On-Demand: usage-based pricing, with $300 in free credit and published per-token and per-step rates.
- Reserved: custom pricing for dedicated GPUs on Lamini-managed infrastructure.
- Self-Managed: custom pricing for customer-run VPC, on-prem, or air-gapped deployments.
Company
Lamini is run by Lamini Inc. The company’s public materials identify Sharon Zhou as CEO and describe Sharon Zhou and Greg Diamos as founders.
Privacy and access
Lamini’s privacy policy says the company collects account, billing, device, usage, and uploaded-content data to provide and secure the service. Its terms and privacy policy also describe customer-controlled deployment options, including VPC, on-prem, and air-gapped environments.
Best fit
- Developers and startups that want an SDK, API, and free-credit entry point for fine-tuning or inference.
- Enterprise teams that need secure deployment options, including VPC, on-prem, or air-gapped environments.
- Teams building text-to-SQL, classification, document reasoning, or other mini-agent workflows on proprietary data.
Tradeoffs to know
- The public on-demand plan is usage-based, while reserved and self-managed plans are custom.
- The official pages do not surface a headquarters address.
- The public materials focus on enterprise and developer workflows rather than a consumer chat product.
Sources
- lamini.ai/pricing
- lamini.ai/blog/lamini-on-demand-300-in-free-credit
- lamini.ai/policies/terms-of-service
- lamini.ai/policies/privacy-policy
- lamini.ai/blog/memory-rag-mini-agents-embed-time-compute
- lamini.ai/blog/classifier-agent-toolkit
- lamini.ai/blog/ai-in-2025-what-to-expect-in-the-year-ahead
- lamini.ai/blog/nvidia-gpus
- lamini.ai/blog/series-a
- lamini.ai/blog/introducing-lamini
- docs.lamini.ai
- docs.lamini.ai/about
- docs.lamini.ai/inference/json_output
- docs.lamini.ai/models
- lamini.ai/product
- docs.lamini.ai/quick_start
- docs.lamini.ai/inference/infv2
- docs.lamini.ai/api
- docs.lamini.ai/authenticate