

Find out in this report how the two AI Data Analysis solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
If I find myself stuck in a cyber recovery situation, this tool can help me avoid spending my money on ransom payments.
The level of confidence that Cohesity Data Cloud delivers to the clients is worth that cost.
I have achieved a 30 to 40% reduction in time to go through the documentation because now I can ask a query from the chatbot, and it provides the result with the appropriate source link.
The task that was happening before developing this product was taking around one hour, but now it is done in hardly one or two minutes.
I have seen a return on investment with Pinecone, as the application we built received positive feedback from internal stakeholders about how much it's helping them make business decisions and access information quickly at their fingertips.
issues with Cohesity Data Cloud have not been encountered, suggesting a robust service.
They need to work faster to meet client requirements, especially when business is affected.
They probably upstaffed and made sure their knowledge was more up-to-date.
The customer support of Pinecone is very good; you send an email and receive a response within a few hours, typically four to five hours.
I haven't needed support because the documentation is good enough to help developers get up to speed.
I would rate the customer support a nine out of ten.
Scaling depends on subscription levels - when customers exceed their subscribed storage capacity, they can pay Cohesity to scale the resources.
There are no issues with scalability on the cloud end.
It's easy to add additional nodes to a current existing cluster, making it quite easy to expand.
It splits vector data into shards, and each shard can be independently indexed and queried, helping with parallel query execution.
We are storing close to around 600K items or entries in the database, and our indexing and retrievals are within seconds, often in microseconds.
We've rolled out the early version as a beta access to a few, maybe twenty to thirty customers.
Compared to other tools, it is very efficient and simple to learn.
I couldn't find anything negative about Cohesity Data Cloud specifically.
Cohesity Data Cloud is quite reliable.
It is able to withstand the enormous data load and manage it effectively.
Issues such as ransomware protection and fixing vulnerabilities should be prioritized.
Cohesity Data Cloud scans backups by default for ransomware and malware, sending notifications if there are any security concerns or compromised systems.
The primary drawback is the need to transfer large amounts of data to the cloud via an internet connection, requiring significant bandwidth.
When we started two years ago, there weren't any vector databases on AWS, making Pinecone a pioneer in the field.
In LangSmith, end-to-end API calls can be analyzed, showing what request came from the customer, what vector search was performed, what prompt was created, what call was given to the LLM, and what response was received from the LLM to the UI.
One major issue I have noticed with Pinecone is that it does not allow me to search based on metadata.
Cohesity Data Cloud is more costly in the long term compared to physical tapes.
Comparatively, compared to IBM and Commvault, Cohesity Data Cloud offers the best deal for my environment.
All organizations are very interested in as-a-service model where they do not pay upfront cost, but they only get the services and pay for what they use as they use it.
The setup cost for us is nil, and the licensing and pricing are pretty decent.
Pricing was handled by the procurement team, but it follows a usage-based pricing model, and I have to pay for storage, read operations, and write operations.
It replicates data to the cloud in a tamper-proof manner, offering protection against ransomware attacks since it is not under administrative control.
They have a feature called DataSock, which enhances data protection.
The initial deployment of Cohesity Data Cloud, from my experience, is easy.
The namespaces feature allows us to break down or store data for each user separately, reducing interference and maintaining privacy as an important feature.
Pinecone has positively impacted my organization by helping people in needle-in-a-haystack situations, as previously they had to grind through PDF documents, PowerPoint documents, and websites, but now with Pinecone, they can ask questions and receive references to documents along with the page numbers where that information exists, so they can use it as a reference or backtrack, especially for things such as FDA approvals where they can quote the exact page number from PDF documents, eliminating hallucination and providing real-time data that relies on an external vector database with enough guardrails to ensure it won't provide information not in the vector database, confining it to the information present in the indexes.
Pinecone, on the other hand, is pay-as-you-go on the number of queries. You only pay for the queries that you hit.
| Product | Mindshare (%) |
|---|---|
| Cohesity Data Cloud | 0.6% |
| Pinecone | 0.5% |
| Other | 98.9% |

| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 1 |
| Large Enterprise | 7 |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 2 |
| Large Enterprise | 8 |
Consolidate your backups, file shares, object stores and data for dev/test and analytics on a web-scale data management platform.
Pinecone is a powerful tool for efficiently storing and retrieving vector embeddings. It is highly praised for its scalability, speed, and ease of integration with existing workflows.
Users find it particularly useful for similarity search, recommendation systems, and natural language processing.
Its efficient search capabilities, seamless integration with existing systems, and ability to handle large-scale datasets make it a valuable tool for data analysis and retrieval.
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