Cohere Reviews

I have used Cohere in a RAG use case where I had to vectorize some data. I used multiple models in RAG to find a better model that could give superior results. I was trying to find a cloud-hosted model, and Cohere's Embed English v3.0 is a cloud-hosted model that took less time to embed the textual data. When I was trying to get the similarity search after embedding that data, Cohere provided much better results.

Let's suppose I had to embed 100 documents at a time. Most other models, including all-MiniLM-L6-v2, took more time when I was trying to embed using that model. When I tried Cohere, it was much faster. I would say it was more than 50 to 60% faster than those models. It was even somewhat faster than text-embedding-3, which is from OpenAI. So Cohere helped to reduce the development time and embedding times.

I believe Cohere offers excellent features, especially the cloud-hosted model and the API calls. The number of times I can call the API within a minute is very good. The ping is great; I have started a request to Cohere model, and it was very quick to respond. The best part was the free tier because most models do not provide a free tier.

Regarding benefits, Cohere is less costly than other models. If I talk about OpenAI or Google embedding models, they charge highly compared to Cohere. Regarding the training data, Cohere has the most data embedded or trained with the most English. Cohere's Embed English v3.0 has been trained with much more data than other models, including OpenAI. This gives an extra benefit to my organization.

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.

I have been using Cohere for the last seven or eight months.

The scalability was very good because of the response time. Even though I do not need that much processing at a time, I have had a good experience with Cohere so far.

Previously, I was using all-MiniLM-L6-v2 and switched to Cohere because all-MiniLM-L6-v2 needed to be locally deployed. That model was processing locally, and the results I was getting from that model, even though it was open source, I was not satisfied. That is why I switched to Cohere.

I can highlight two benefits. Cohere charges less than OpenAI, so it saves cost. In the second use case, the timing is significant. Cohere's Embed English model took less time to embed than OpenAI's embedding ada-002 model. In this case, it also saves time. These two benefits I can highlight.

I have evaluated OpenAI's Embed English v3 and text-embedding-3 models. I have evaluated multiple models, and I even evaluated some models from Hugging Face.

Cohere provides a free tier, and any developer who is starting their journey can use Cohere for RAG use cases. They can utilize the model benefits. After using Cohere, I got distances after the similarity search that were much lower compared to other vectorization and embedding models. The only model that performed better than Cohere was OpenAI's text-embedding-3-large. It was good, but Cohere was the second-best performing model in my use case.

I think Cohere's use cases are excellent, and I would suggest Cohere to others because of the less response time and time-saving in the process. It is also cheaper than other models. I would give this review a rating of eight out of ten.

I am working on test automation, specifically an intelligent test automation framework. Based on the existing framework, which is handled in TypeScript and Selenium, I used Cohere intelligence to create new tests based on the test data and test cases that we provide. It will read through all the test cases in natural language, process them, analyze the internal working of our existing framework, and create the artifacts, test data, and test source based on the existing framework.

Currently, we are using Cohere APIs. First, I used the chat in the application itself to identify how it works by providing RAG sources, including PDF and text files. After confirming it worked fine, we moved to find an API, and we are using that API to handle all these tasks. The APIs are very functional for all our current use cases, mainly the intelligent test automation.

Cohere is very useful because I have been in scenarios where code was written with multiple reusable concepts containing many functionalities covered as different functions, but without descriptions of what particular functions were doing. We used Cohere intelligence and its knowledge on Oracle ERP PPM, and it was able to read through all the TypeScript code and create descriptions intelligently, which were almost 90% correct when reviewed.

It was very useful because we had 500-plus reusables, and it was able to analyze all of them and put them into a catalog. This makes it very easy to find and use the catalog to determine whether existing functionality is already implemented, preventing redundant implementations.

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. We can complete many more tests in a day or sprint with Cohere's help.

Along with test automation, we handle analysis tasks, and now we have more time for better analysis. We are planning to implement test analysis capabilities as well. Once you receive the requirements and test cases, you can directly use them as input, and it will generate all artifacts and test data.

When performing similarity matching between text descriptions and the catalog descriptions created using Cohere, the matching could be improved. Because it does not have extensive understanding of Oracle functionalities in ERP, it sometimes gives wrong results or the confidence score is lower than desired. Improving that understanding would provide better matches.

When working with Cohere and providing large data sets, there was some hallucination, though it mostly works fine without many issues.

I have been using Cohere for almost seven to eight months.

I have not faced any downtime or related issues. It works fine.

I used Llama but it was not giving results comparable to what I get from Cohere when comparing the two solutions. We only had these two options at that time, and we chose Cohere over Llama.

The setup was pretty smooth. I was able to find things easily. The documents were readily available on the internet, and I was able to find and integrate them without any issues. I subscribed to emails about new model updates, which allowed me to stay current. Oracle has now wrapped it inside their own AI, and we are using the latest version of Cohere as our chosen model.

I started with the public version and then they wrapped it inside Oracle's system. I believe it is private, only accessible to Oracle employees with proper authentication and sign-in details. The pricing and setup were handled by the organization, so I am not aware of those aspects.

We only had two options at that time: Llama and Cohere. After trying both, we chose Cohere over Llama.

Try it and use it. If you find it worthy, then implement it. I have shared all my experiences with you. My rating for Cohere is 7 out of 10.

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 generation and summarization of long documents and draft creation for internal content, so we used Cohere's command model with AWS Bedrock.

For another customer, there was a similar use case but they also wanted semantic search and RAG, and instruction-based responses for chat and workflow automation were required, so we used Cohere's command model for that.

Cohere's command model is particularly useful for scenarios where consistent controlled output is more important, especially where we need creative responses, so I think Cohere's command model fits better in that case. We also found it well suited for structured enterprise tasks such as policy drafting, knowledge extraction, and generating standardized text for operational workflows.

It struck a good balance between fluency and predictability, which helps our team and is valuable for our business-critical applications, giving better insight to our team.

One of the major benefits I saw was data isolation and governance since Cohere has been implemented.

Consistent output quality, strong instruction following, and excellent embedding performance for retrieval tasks have benefited our organization. It was also offered from Amazon Bedrock, so this complete offering and strength from Cohere's command model helped our customers, and it is enterprise-friendly with deployment options such as VPC and data isolation that helped significantly.

Data privacy was a major concern because we operate from Asia-Pacific, and there is strong governance for data privacy in our country, so data privacy is the major compliance that helped us here.

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 analytics in the dashboard could be more detailed and fine-tuned, which would enhance the experience.

Fine-tuning could be simplified to support broader teams without deep ML expertise.

For speeding up, what I have already suggested is that it can be more creative, and their reporting and analytics can be improved, as this would help teams without machine learning expertise and speed up their end goals.

The dashboard reporting can be improved.

We have been using Cohere for around one year.

Cohere is stable.

The scalability and performance are quite good.

We have not reached out to customer support yet, but once we encounter an issue and need to raise a ticket, we will provide feedback.

Cohere helped us with all three aspects: money is saved, time is saved, and we needed fewer resources to meet our end goals.

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

We have tried multiple models, but we found that Cohere's command was a better fit for our needs.

We explored models from Anthropic and AWS native models such as AWS Titan Text before choosing Cohere.

Data privacy was a major concern because we operate from Asia-Pacific, and there is strong governance for data privacy in our country, so data privacy is a major compliance that helped us here.

Cohere offers great customization options.

If governance, consistency, and data privacy are priorities, Cohere meets our organization's requirements well.

I recommend that anyone, especially in environments where governance, consistency, and data privacy are priorities, should choose Cohere, particularly the command model for teams looking for a controlled enterprise-safe alternative for text generation, summarization, and instruction automation.

Currently, we have used Cohere from the AWS Bedrock offering only, but since AWS has changed their third-party model availability from partner accounts, in the future, we are going to be a reseller for Cohere.

The documentation and learning resources were very helpful.

Our overall review rating for Cohere is 8 out of 10.

We founded this company two and a half years ago, and since the middle of 2022, we foresaw the trending of generative AI and large language models, so my startup is working on developing generative AI applications for our clients, including enterprises and a few other startups across America and Canada.

I started using Cohere when we first got information from the community about their reranking models almost one and a half years ago.

In some clients' projects, we were required to introduce reranking model in the RAG flow (Retrieval-augmented generation). In this flow, we use different components to allow users to select and pick up from the UI components, drag and drop to their flow to enhance their RAG pipeline. That's where we introduced Cohere models as one of the providers for reranking.

Cohere's reranking model helped us complete this request

From our data, I can tell that at least 15% of end users were actively using reranking to enhance their RAG pipeline because we have the UI to indicate that reranking is recommended as it can enhance the quality of the retrieval.

For clarification, I want to describe this data more clearly. As mentioned, 15% of end users chose to enable this module based on the fact that we have the pricing tier with an extra cost for their API call.

In general, I'm satisfied with the speed, and I can confirm this because we have the long fields to track all conversations, and we see that this loop for reranking actually costs relatively less time throughout the whole chat flow. Regarding quality, it's hard to tell because we don't have a benchmark. In our enterprise applications, we are trying to build up evaluation pipelines, do AB testing, and other analysis, but it's not a conventional computer science application, so it's very hard to build up evaluation pipelines with objective criteria. It's challenging for us to make a conclusion about quality, but the speed is good.

A direct benefit of using Cohere's reranking model is that we can tell clients we have this module rather than missing this piece, as reranking is a very important component that companies discuss to enhance RAG quality.

Although it's not impacting our business model, I'm pushing for the evaluation system because it can expand our business scope. We want to sell our system to clients, and while they may not be aware of evaluation initially, it's beneficial to have. Once we have these systems, we can showcase to end users that employing such a reranking system improves quality. We need proof to convince ourselves that after implementing reranking, we get better quality.

It would be better to have a dashboard for users to showcase how reranking helps improve quality. When end users choose the service, they want to see the actual output. The evaluation part is challenging for recent large language model applications but remains very important.

If Cohere could provide a dashboard where we can employ an LLM as a judge to check quality before and after reranking, that would be helpful. We could either have another large language model evaluate this part or allow UAT users to manually check with humans in the middle. As an enterprise provider, we want such features because when chatting with clients, we can demonstrate that employing Cohere's reranking model significantly improves results compared to not using it.

Documentation is not a major blocking issue for us as we are sophisticated software engineers. Integration and the API provided for reranking models are not complicated, so we can easily handle that. The documentation is good. The major point is to prove the value through evaluation. We need a sophisticated solution to showcase visibly to our clients and engineering team to convince them that using this model creates improvements.

I started using Cohere when we first got information from the community about their reranking models almost one and a half years ago.

That's only what we need in our product currently. I will communicate when we have other requirements.

We haven't had any issues to escalate to Cohere's support because reranking is an optional feature in our product, and we haven't seen any significant issues so far.

We don't observe many scaling problems because it's an enterprise application.  There are a few hundred people using this. The concurrent user rate is not significant, which might be why we don't see many scaling issues so far.

We haven't had any issues to escalate to Cohere's support because reranking is an optional feature in our product, and we haven't seen any significant issues so far.

Positive

For reranking, Cohere was our only solution.

I'm more focused on the speed and overall quality of the model itself and the chat flow as a whole solution. That's why I'm not in the position to comment on the price and setup cost as there are DevOps working on this piece.

Hard to estimate the overall ROI. but if you see the ROI for the feature of reranking, it's a positive number

I'm not in the position to answer that question because I was not the one who deployed that model, but I believe it is because we see the model name as ARN name, so it's most likely coming from Bedrock.

For reranking, Cohere is the only solution we have used so far.

As a feature developer, I'm more focused on the speed and overall quality of the model itself and the chat flow as a whole solution. That's why I'm not in the position to comment on the price and setup cost as there are DevOps working on this piece. My rating for this solution is 8 out of 10.

My main use case for Cohere is to use a Cohere embedded model to create our own vector databases and check conversations.

A specific example of how I use Cohere's embedding model for our vector databases or conversation checking involves abilities that take customer approvals and convert that information into vectors. I save this information in our own systems and also store small vectors on customer devices to use during custom customer requests.

My use case involves indexing and saving small portions of information.

In my experience, Cohere offers reliable embedding models for customers who do not want to use standard OpenAI models.

I find that the choice of embedding models is limited, and Cohere was available for Azure, which makes it a good alternative for customers who prefer not to use OpenAI.

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.

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. Quality can only be understood after using it with customer requests, and during the embedding process, measurable metrics are not visible.

There are no particularly unique features distinguishing Cohere from other solutions.

I have been using Cohere for approximately nine to ten months.

Cohere is stable in my experience.

The scalability of Cohere showed that after sending a large amount of information and embeddings, it became slower, though we do not use any special solution for scaling.

I have not interacted with Cohere's support team. However, I contacted Azure about the slowness, and we decided to use smaller chunks of information during the embedding process.

Neutral

I previously used embedding models from OpenAI. I switched to Cohere because customers wanted to use something other than OpenAI models.

I did not purchase Cohere through the Azure Marketplace. I deployed unmanaged models and shared models.

I do not have relevant metrics about the return on investment from using Cohere yet because the customer's application is in a paging stage and has not been released. However, I understand that it is performing well, and we plan to continue with it.

My experience with pricing, setup cost, and licensing indicates that it does not require a special license, and the prices are competitive compared to competitors.

I did not evaluate other options before choosing Cohere. I looked at prices, and since we used Azure cloud, it did not provide many models for selection. Only OpenAI and Cohere were available for embedding.

For others looking into using Cohere, I advise that it is a good model for people who want to be agnostic when using models and creating something flexible to switch from one model to another. I would rate this product an eight out of ten.

My main use case for Cohere is that it's a good embedding model. I have used it with Titan, but Cohere came out better.

A specific example of how I've used Cohere for embeddings is when I was working with one of our clients where we were establishing a chatbot that can help us go through 31 PDFs. For embedding, we used Cohere and Titan, and Cohere was a superior product.

I have integrated Cohere in that chatbot project using SageMaker, and it was an easy API call that I used.

In my opinion, the best features Cohere offers are the embedding flexibility and the normal way the LLM reacted to the embeddings of Cohere. I used OpenSearch to integrate and store all the embeddings, and I used Titan as well to store the embeddings in OpenSearch, but the result was much better.

The flexibility I mentioned is evident because when we were using Titan, it was hallucinating a lot and not giving proper answers because I felt the embedding was poor. When we used it with Cohere, the embeddings were better and the chatbot with the LLM that used the embeddings from Cohere answered in a better way.

Cohere has positively impacted my organization as the project was a success. Clients were really happy with the results, and we received more business from them.

Cohere can be improved by having more integrations beyond its current offerings with Amazon. Integrations with Databricks, Azure, and Google Cloud would be beneficial.

I have been using Cohere for the last two years.

Cohere is stable.

Cohere's scalability is pretty good, as we used it for 31 PDFs and there was no bottleneck.

Cohere handles large-scale data and workloads really well. I did not see any bottleneck at all because the project was relatively small.

We did not require any customer support.

I did not previously use a different solution; this was the first time we were developing it and Cohere was from the start a competitive alternative to Titan, so we were always going to choose between Cohere or Titan.

It was extremely easy for my team to learn and start using Cohere; it was just one API call.

Cohere integrates really well with the tools I use, and I did not experience any challenges with OpenSearch or SageMaker.

Regarding the return on investment and any relevant metrics such as money or time saved or reduced resource needs, that is confidential.

My experience with pricing, setup cost, and licensing was that it was all managed by AWS, and we had AWS credits, so I did not have to dive into that.

Before choosing Cohere, I evaluated AWS Titan.

The key factors that led me to choose Cohere over other AI development platforms are that I was experimenting, and Cohere was better than Titan.

Cohere has helped my organization innovate and stay ahead in our industry as Cohere was better than Titan, and it helped us to secure the client's confidence and we moved from proof of concept to production.

The advice I would give to others looking into using Cohere is to go for it. Cohere is the best embedding model, and I would not recommend wasting time with AWS Titan.

I rate Cohere a 10 out of 10 because Cohere was way better compared to Titan.

I work with Cohere and have been doing so for about two months.

Currently, I am working with AWS Cloud and cloud services, and we use models like GPT-4o mini, 2.1, and Cohere.

We primarily use English only, with no other languages.

I assess the value of Cohere's API support in my business operations as easy to integrate.

The specific benefits I have seen from using Cohere include saving time to summarize information and for chat completion and related tasks.

I use Cohere for chat completion purposes.

My thoughts on the summarization feature are that it is beneficial for impacting our data analysis tasks.

English is where the language understanding was specifically beneficial for us.

Cohere is a solid LLM that processes all files well.

I would appreciate additional features such as OCR and similar capabilities.

I have been working with Cohere for about two months.

There are no disadvantages or drawbacks of Cohere in comparison to ChatGPT or other AI solutions.

There are no complexities with Cohere; the setup process is straightforward.

I have not escalated any questions to the technical support team.

Negative

I have experience working with several different AI products, but I do not perceive any significant difference between them; they are nearly identical with accuracy varying slightly across certain areas.

There are no complexities; the setup process is straightforward.

I have not observed any measurable benefits or return on investment with Cohere.

In my opinion, the pricing is reasonable.

There are no key differences or notable advantages or disadvantages of Cohere in comparison to other AI tools and LLM products that I am working with.

I intended to clarify that I use Cohere for chat completion.

My primary concern stems from the missing OCR capabilities.

My overall review rating for Cohere is seven out of ten.

My main use case for Cohere is Retrieval Augmented Generation.

A specific example of how I use Retrieval Augmented Generation with Cohere is for information retrieval systems.

The best feature Cohere offers is the Reranking model.

What stands out for me about the ranking model is that it improved performance in my work.

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

I noticed a 10% improvement in my log system after using Cohere.

Cohere is good enough, and I think it can be improved.

I have been using Cohere for two years.

Cohere is stable.

The scalability of Cohere is good.

The customer support for Cohere is good.

Negative

My experience with pricing, setup cost, and licensing for Cohere is good.

I have not seen metrics for return on investment, and I have no metrics to share.

My experience with pricing, setup cost, and licensing for Cohere is good.

My advice to others looking into using Cohere is to try it.

My company does not have a business relationship with this vendor other than being a customer.

I gave this review a rating of 8.

My main use case for Cohere is for LLM and chatbot development.

I use Cohere to fill boxes about documents, specifically about tenders.

Cohere helps me fill boxes about documents, and I work with docx documents for a private company.

The best features Cohere offers are that it is fast and great.

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

Cohere has positively impacted my organization because I use it with Oracle, and in an enterprise way, it helped me offer clients a unique place to develop and use LLM. I can tell you that it helped me offer clients a unique place to develop and use LLM, as I use Oracle services.

I am uncertain about how Cohere can be improved.

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

Cohere is stable.

I am uncertain about Cohere's scalability.

I am uncertain about customer support.

I used GPT-4 before Cohere, and it is great.

Before choosing Cohere, I evaluated other options, specifically GPT-4.

I am uncertain if I have seen a return on investment or any relevant metrics such as time saved, money saved, or fewer employees needed.

My experience with pricing, setup cost, and licensing is that it is expensive to use all Oracle services.

I do not want to add anything else about the features, including anything about accuracy or ease of use.

I do not have specific advice to give to others looking into using Cohere. I gave this review a rating of 6.

I use it for a personal project, a Discord bot for my Discord server. I haven't used it that much, but so far it's amazing. I like the support team. They are very good.

Everything is definitely intuitive, and whenever you have an issue, it's very easy to reach out to them on Discord. They're very active, so I'm not really complaining about having issues.

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. 

I think it's free for personal projects unless you want to go to production. I haven't really used it that much, but the features that I have used so far, I have no issues with them.

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.

I've recently started exploring Cohere.  It has been a few months now, two to three months.

I'll rate the stability a six out of ten since I haven't been using it much. I haven't really seen any issues.

I will rate the scalability a seven out of ten because I haven't explored it all the way.

The customer service and support are very good. 

Positive

It's very easy. You just need an API key, and all the configurations are there. It's very easy to start.

If you have worked with something that requires API keys, you should be good to go. I don't think you need a lot of experience.

Cohere has a free tier. You can use the API in development mode, so you can just use it for free. But if you go to production, you will have to pay.

I would advise someone to really consider it first if they really need it because it can be expensive.

So it might be a little expensive.

Overall, I would rate it a seven out of ten. 

I would recommend it to others because it is very promising, so it would be worth the time. Others should try it.