This company focuses on enhancing developer experience by examining trends like platform engineering and automation. They provide insights into tools and strategies that reduce friction in software development workflows. The goal is to boost developer productivity and satisfaction through streamlined processes and self-service platforms.
Funding
Funding not disclosed
Founders
Product
Problem
Training and deploying large language models (LLMs) for specific tasks can be expensive and time-consuming, often requiring dedicated infrastructure and specialized expertise. Businesses face challenges in achieving GPT-4 level performance without incurring significant costs or becoming locked into specific vendor solutions.
Solution
Empower offers a developer platform that simplifies the process of fine-tuning task-specific LLMs, delivering comparable response quality to GPT-4 at a reduced cost and faster speed. The platform provides pre-built base models optimized for various tasks, which can be further customized using prompts or fine-tuning. Empower's pay-as-you-use pricing model eliminates the need for expensive dedicated instances, allowing businesses to deploy efficient LLMs without vendor lock-in and retain full ownership of their models. The platform ensures sub-second code start time for LoRAs, providing performance comparable to dedicated instance deployments.
Target Audience
Empower targets developers and businesses seeking cost-effective and efficient solutions for deploying task-specific LLMs without the overhead of managing infrastructure or specialized expertise.
Features
- Pre-built, task-specific base models offering comparable quality to GPT-4 in the focused task domain.
- Up to 3x faster latency and time to first token (TTFT) compared to GPT-4.
- Cost savings of up to 7x on input tokens and 20x on output tokens compared to GPT-4.
- Compatibility with PEFT-based LoRAs, allowing users to train models anywhere and deploy them on Empower.
- Pay-as-you-use pricing model based on token consumption, eliminating dedicated instance fees.
- Built-in Chain-of-Thought (CoT) support in empower functions models.
- Auto Fine-Tuning platform to fine-tune small language models.