together.solutions

Problem

Needed efficient compute capacity that scaled from prototype to production. Having fast and efficient performance for training was a must. They needed to move quickly – they didn’t have time to worry about setting up their own training infrastructure and they needed a partner who could scale with their difficult-to-forecast traffic.

Solution

Pika used Together Inference API to rapidly prototype using the easy-to-use open-source model library. Once the team decided to build their own models from the ground up, they opted for the unparalleled compute power of Together GPU Clusters. And once they launched the product and saw user traction grow exponentially, Pika scaled inference seamlessly.

Results

Pika grew to millions of videos generated per month with the top users spending ~10 hours per day on the platform — all within 6 months of being founded.

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Problem‍

Upstage needed to host Solar, their most popular LLM, so that it could be used by the widest possible audience. When the model charted on the Hugging Face Open LLM Leaderboard, they also needed a place that could scale to handle high traffic while maintaining fast performance and cost efficiency.

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Solution

Upstage chose Together Inference serverless endpoints to host their model because of the user-friendly interface of the API, its competitive pricing, and Together AI’s expert support that made bringup super easy.

Result

The Solar model was deployed on Together Inference, and published on Poe.com. Together Inference easily scaled to serve over 2.8 million peak tokens per hour with exceptional performance — over 45 tokens per second. The Upstage team expanded their partnership and integrating Together AI into their own service.

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Wordware's mission is to enhance the machine learning workflow by removing the dependency on extensive 'ground truth' datasets. Their platform empowers domain experts to quickly refine prompts, improving collaboration and speeding up iterations. Wordware wanted to focus on building the best collaborative web-based IDE for language model programming with seamless model selection and not on the hassle of managing expensive infrastructure. 

Wordware adopted Together's infrastructure for its versatility and user-friendly interface. The ability to rapidly prototype and scale using Together's Inference API and the powerful compute capabilities of the service was integral to their progress. The platform's low latency, minimal cold start times, and cost-effectiveness allowed Wordware to experiment with various models, enabling their customers to transition from GPT-4 to Mistral, leading to significant cost reductions, enhanced reliability and reduced latency.

Wordware's innovative approach has led to groundbreaking applications. One notable customer example is the development of AI-powered NPC interactions, in which the cost of operation was reduced by 16x after transitioning to Wordware. This efficiency is attributed to Wordware's token-based pricing and the ability to integrate multiple models seamlessly, like Mistral and OpenChat, offering a unique balance of speed, flexibility, and cost-effectiveness, which Wordware attributes to Together’s API.

Problem

To enhance the capabilities of existing base models with public data, Nexusflow required a cost-effective, reliable, and scalable compute partner. Traditional cloud providers were not able to simultaneously offer the cost-efficiency and the level of guaranteed availability that Nexusflow needed to scale their specialized workloads.

Solution

The team at Nexusflow opted for Together GPU Clusters, seeing it as the perfect "trifecta" in terms of contract length, pricing, and compute availability. They utilized GPUs suitable for their specific workload requirements, and benefited from the unparalleled support that Together’s expert team offers.

Results

Nexusflow completed the onboarding process in <90 minutes and was able to run workloads. Initial hiccups were resolved by Together's support team, ensuring a smooth experience. Nexusflow managed to cut their R&D cloud compute costs by 40%, while experiencing faster response times and lower latency in technical support than other cloud providers.

Problem

Arcee was looking for more reliable, factual systems that are also cost effective to build domain adaptive language models for Arcee’s customers.

Solution

Arcee made a strategic decision to build a fine-tuned model with Together AI for several compelling reasons: the accessibility of Together API, the quality of the Together AI team, and their commitment to build a good model not just as a technical provider but as a collaborative partner.

Results

Arcee built their model using Together Custom Models including domain-specific data. To optimize the quality of the model, it was trained with a data mixture optimized using DoReMi, an algorithm for finding the optimal mixture of language datasets using Distributionally Robust Optimization.