Cerebras Fast Inference Cloud vs Cohere comparison

Cerebras Fast Inference Cloud

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Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Cerebras Fast Inference Cloud and Cohere based on real PeerSpot user reviews.

Find out in this report how the two Large Language Models (LLMs) solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Cerebras Fast Inference Cloud vs. Cohere Report (Updated: December 2025).

Cerebras Fast Inference Cloud vs. Cohere

December 2025

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Helped 881,082 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Cerebras Fast Inference Cloud

Ranking in Large Language Models (LLMs)

9th

Average Rating

10.0

Reviews Sentiment

1.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Cohere

Ranking in Large Language Models (LLMs)

5th

Average Rating

7.6

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

AI Development Platforms (12th), AI Writing Tools (3rd), AI Proofreading Tools (5th)

Featured Reviews

reviewer2787606

Co-founder at a tech services company with 1-10 employees

Fast inference has enabled ultra-low-latency coding agents and continues to improve

I use the product for the fastest LLM inference for LLama 3.1 70B and GLM 4.6 We use it to speed up our coding agent on specific tasks. For anything that is latency-sensitive, having a fast model helps. The valuable features of the product are its inference speed and latency. There is room for…

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AmanSingh5

Engineer at Roche

Have improved project workflows using faster response times and reduced data embedding costs

One thing that Cohere can improve is related to some distances when I am trying similarity search. Let's suppose I have provided textual data that has been embedded. I have to use some extra process from numpy after embedding the model. In the case of OpenAI embedding models, I do not have to use that extra process, and they provide lower distances compared to my results from Cohere. I was getting distances of approximately 0.005 sometimes, but in the case of Cohere, I was getting distances around 0.5 or sometimes more than that. I think that can be improved. It was possibly because of some configuration or the way I was using it, but I am not exactly sure about that.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The throughput increase has extended decision-making time by over 50 times compared to previous pipelines when accounting for burst parallelism."

"Cerebras' token speed rates are unmatched, which can enable us to provide much faster customer experiences."

"I recommend using it for speed and having a good fallback plan in case there are issues, but that's easy to do."

"Cohere has positively impacted my organization by helping our customers work more efficiently when creating requests, and the embedding results are of very high quality."

"The best feature Cohere offers is the Reranking model."

"When it creates a new test, it creates it almost 70 to 80% correctly without errors; the time savings are significant—what previously took one or two days can now be completed in two to three hours maximum."

"Cohere positively impacted my organization by improving the performance of my RAG system."

"The very first thing that I really like about it is the support team. They're really available on Discord, and they answer all of your questions."

"Speed has helped me in my day-to-day work, and I really notice the difference because it responds very quickly to LLM requests."

"A key advantage of integrating Cohere’s reranking model is that it aligns with client requests to include a reranking module — a widely recognized method for improving RAG quality. Additionally, the API demonstrates strong performance in terms of response speed."

"Cohere's Embed English v3.0 is a cloud-hosted model that took less time to embed the textual data and was more than 50 to 60% faster than other models, even somewhat faster than text-embedding-3 from OpenAI, helping to reduce development and embedding times."

More Cohere pros

Cons

"There is room for improvement in the integration within AWS Bedrock."

"There is room for improvement in supporting more models and the ability to provide our own models on the chips as well."

"The documentation and support could be improved, as there is limited documentation available on the web."

"Cohere could improve in areas where the command model is not as creative as some larger LLMs available in the market, which is expected but noticeable in open-ended generative tasks."

"I believe Cohere can be improved technically by providing more feedback, logs, and metrics for embedding requests, as it currently appears to be a black box without any understanding of quality."

"When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved."

"It's challenging for us to make a conclusion about quality enhancement by using reranking models, as solid evaluation methodology for reranking is still immature."

"Cohere has text generation. I think it is mainly focused on AI search. If there was a way to combine the searches with images, I think it would be nice to include that."

"One thing that Cohere can improve is related to some distances when I am trying similarity search."

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Top Industries

By visitors reading reviews

No data available

Manufacturing Company

11%

Educational Organization

Financial Services Firm

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	1
Large Enterprise	6

Questions from the Community

What is your experience regarding pricing and costs for Cerebras Fast Inference Cloud?

They are more expensive, but if you need speed, then it is the only option right now.

What is your primary use case for Cerebras Fast Inference Cloud?

I use the product for the fastest LLM inference for LLama 3.1 70B and GLM 4.6.

What advice do you have for others considering Cerebras Fast Inference Cloud?

Their support has been helpful, and I've had a few outages with them in the past, but they were resolved quickly. I recommend using it for speed and having a good fallback plan in case there are is...

What is your experience regarding pricing and costs for Cohere?

Compared to models available in the market, Cohere's pricing, setup cost, and licensing are better.

What needs improvement with Cohere?

Cohere could improve in areas where the command model is not as creative as some larger LLMs available in the market, which is expected but noticeable in open-ended generative tasks. Reporting and ...

What is your primary use case for Cohere?

We adopted Cohere primarily for their command model to support enterprise-grade text generation and NLP workflows. There was a use case for one of our customers where they required automated text g...

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Product Reports

Large Language Models (LLMs)

December 2025

Download Cerebras Fast Inference Cloud product report

Download Cohere product report

January 2026

Overview

Cerebras Fast Inference Cloud offers cutting-edge cloud capabilities tailored for AI and deep learning applications. Designed for rapid processing, it efficiently handles complex models and large data sets.

Specialized for AI, Cerebras Fast Inference Cloud provides seamless access to high-performance computing resources. Leveraging unique architecture and advanced features, it accelerates model deployment, allowing enterprises to rapidly iterate and innovate within their AI workflows. Scalable performance and intuitive cloud management contribute to a robust platform for diverse computational needs.

What are the notable features?

High Performance: Optimized for low-latency and quick data processing.
Scalability: Easily adjusts to workload demands, ensuring optimal resource utilization.
User-Friendly Interface: Aids in smooth operations and management.
Advanced Analytics: Comprehensive tools for monitoring and evaluating AI applications.

What benefits should users look for when evaluating?

Increased Efficiency: Streamlines AI workflow for faster time-to-market.
Cost-Effectiveness: Reduces infrastructure expenditure with scalable resources.
Reliability: Delivers consistent performance, ensuring project continuity.
Flexibility: Supports a wide range of AI applications and industry needs.

Cerebras Fast Inference Cloud has applications across finance, healthcare, and manufacturing, offering precise modeling, predictive analytics, and enhanced data interpretation tailored to industry demands. Its adaptability makes it a preferred choice for organizations leveraging AI to drive innovation and efficiency.

Cerebras Systems

Cohere provides a robust language AI platform designed for efficient implementation in various domains, offering advanced features for automation and data analysis.

Cohere delivers a scalable AI language model that facilitates automation in data-driven environments. Highly adaptable to industry-specific requirements, it supports tasks such as text generation, summarization, and anomaly detection. This flexibility, along with its integration capabilities, makes it valuable for tech-savvy users seeking seamless AI solutions.

What are the notable features of Cohere?

Adaptive Language Models: Efficiently handle diverse text-based tasks with customizable models.
Data Integration: Seamless integration into existing databases and applications for smooth operation.
Scalability: Support for scaling processes to meet enterprise-level demands without performance loss.

What benefits should users consider in reviews?

Cost Efficiency: Reduction in manual task overhead leading to budget savings.
Performance Improvement: Boost in operational productivity due to automation and precise data handling.
Customization: Ability to tailor AI functionalities to specific industry needs or unique processes.

Cohere sees significant use in finance, healthcare, and marketing, enabling precise data analysis and strategic insights. In finance, it assists with detecting market trends, while in healthcare, it supports clinical documentation and research analysis. Marketing uses include content creation and consumer sentiment analysis.