Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.
Pinecone is highly regarded for its robust performance and ease of integration, which users frequently highlight as main strengths. Users have minimal complaints, although some mention a learning curve initially. The pricing is perceived as reasonable for the advanced capabilities it offers. Overall, Pinecone enjoys a robust reputation as an effective and reliable tool in its category.
Mentions (30d)
0
Avg Rating
4.5
20 reviews
Platforms
2
GitHub Stars
424
118 forks
Pinecone is highly regarded for its robust performance and ease of integration, which users frequently highlight as main strengths. Users have minimal complaints, although some mention a learning curve initially. The pricing is perceived as reasonable for the advanced capabilities it offers. Overall, Pinecone enjoys a robust reputation as an effective and reliable tool in its category.
Features
Use Cases
Industry
information technology & services
Employees
170
Funding Stage
Series B
Total Funding
$138.0M
1,684
GitHub followers
104
GitHub repos
424
GitHub stars
20
npm packages
596,633
npm downloads/wk
Pricing found: $20/month, $50/month, $50/month, $300, $500/month
g2
What do you like best about Pinecone?Pinecone stands out for its low-latency similarity search, managed scalability, and developer-friendly APIs. It removes much of the operational burden of running vector databases, making production-grade semantic search significantly easier. Review collected by and hosted on G2.com.What do you dislike about Pinecone?Pinecone delivers excellent performance, but improved cost predictability, more granular configuration options, and greater transparency in scaling behavior would further enhance the developer experience. Review collected by and hosted on G2.com.
What do you like best about Pinecone?The service is self-managed by Pincone, so there is no need for separate billing; it can be handled directly through your cloud service provider, such as the AWS Marketplace. Defining and creating a vector instance according to the dimensions and parameters of your embedding models is straightforward. I found it quite simple to integrate with both AWS Bedrock and GCP Vertex AI services. In my experience, querying data is faster compared to other services I have used so far. This service is in our daily use as a backbone for our AI services. Review collected by and hosted on G2.com.What do you dislike about Pinecone?If you are using the trial version, you are required to create your instance in the US only. However, since I work in banking, this presents a compliance issue regarding data location. They should offer trial access in other countries as well, or consider implementing different limitations instead of restricting by region. Review collected by and hosted on G2.com.
What do you like best about Pinecone?its provide various of features and great vector db support Review collected by and hosted on G2.com.What do you dislike about Pinecone?may be it is close source and needed some features which are not there yet. Review collected by and hosted on G2.com.
What do you like best about Pinecone?It is specialised in AI driven use cases with real time and low latency search giving seamless integration into machine learning workflows with scalable infrastruture optimized for unstructured and semi-structured data in AI applications. Review collected by and hosted on G2.com.What do you dislike about Pinecone?It has limited focus that is related only with the vector data with no major focus on Business intelligence in data transformation tool. Also it's use case is little complex with lack of ecosystem integration. Review collected by and hosted on G2.com.
What do you like best about Pinecone?when iam creating embeddings,compared to other products,it feels hassle free& cheap. Review collected by and hosted on G2.com.What do you dislike about Pinecone?I am the beta tester of pinecone AI assiatant,it is not production ready so it feels like only for testing,i am expecting for the production ready version. Review collected by and hosted on G2.com.
What do you like best about Pinecone?I have been using pinecone for embeddings and it is cheaper and reliable compared to other embedding services. Review collected by and hosted on G2.com.What do you dislike about Pinecone?I dislike the overall feel which feels lightweighed for the product service documentation. I love to see pinecone assistant in deployable version because it is powerful yet it is in the beta version only for testing not for production Review collected by and hosted on G2.com.
What do you like best about Pinecone?Easy to use. very reliable and fast. Competitive price Review collected by and hosted on G2.com.What do you dislike about Pinecone?Maybe some extra features would be nice, and some more clarity into its AKNN algo, which is hidden from the user Review collected by and hosted on G2.com.
What do you like best about Pinecone?Pinecone was our primary choice and we have not considered changing since. - High performance (upsert and search in the ms) - Simple integration via API and deployment and now after their recent release of serverless indexes it's very simple to maintain and scale (it's autoscaling). - Low price (relative to the number of vectors) and free limited indexes. Free indexes are great to run development environment data. For a while it was impossible to upgrade a free index to a paying one, but this is now addressed. - Incredible support (we had an issue and was not expecting getting this quality of support without paying the usual business support fees of an AWS for example) - The ability to assign metadata is very useful (we still maintain a traditional db to keep track of the vectors) - The single stage query vector/metadata is very useful and saves the headache of over-querying - One feature we have meant to use is the use of sparse vectors in combination with the dense vectors. So, can't really comment yet Review collected by and hosted on G2.com.What do you dislike about Pinecone?Love most of it as is - The documentation using metadata and single stage queries is a bit light - They have a smart bot to help answer support questions. On the great side, it seems they use their own technology for RAG type of application, but on the other it often misses the mark. ChatGPT or Perplexity are surprisingly more effective. - There has been a few down times, but they are very communicative about them and maintain a server health page for each endpoint. It's usually related to a specific infrastructure (AWS or GCP) they run on. - They have been growing and improving the technology, and like with other player, sometimes to update their python library or the way to reference to the indexes. But each time it's been toward simplification, and I suspect it will stabilize. Review collected by and hosted on G2.com.
What do you like best about Pinecone?Pinecone excels in providing a seamless, high-performance vector search experience. Its ease of use, combined with powerful features like real-time updates and scalability, makes it a go-to solution for managing complex vector data. The ability to effortlessly integrate with existing workflows and its top-notch customer support are definite highlights. Review collected by and hosted on G2.com.What do you dislike about Pinecone?While Pinecone is robust, the pricing can be a bit steep for smaller projects or startups. Additionally, more granular control over indexing options would enhance customization for advanced users. However, the benefits far outweigh these minor drawbacks. Review collected by and hosted on G2.com.
What do you like best about Pinecone?Pinecode offers a simple API and lean management interface for a completely low maintenance vector storage and query solution. Review collected by and hosted on G2.com.What do you dislike about Pinecone?I started using Pinecone when it was new and had some rough edges. But support was proactive and smart. In the last year I can say there is nothing to not like. It has been awesome. Review collected by and hosted on G2.com.
Two months of coding with Claude code
My background started in sales, moved to product/tech about ten years ago culminating in my role as chief product officer at a large debt relief company. Today, around 7:30 am, after my fourth all nighter in a row I released a product (in stealth no heavy marketing yet) after two months of deep work with over 1,000 commits and a lot of sleepless nights. I used VS code, with ClaudeCode. Mostly opus high effort. Lots of CLI, no MCP - huge win - read about so many issues with MCP and it was never a thing. Built on/with railway, supabase, voyage AI, pinecone, resend, grafana, multi-AI provider with custom fallback (almost used liteLLM, and chose custom days before their incident), cloudflare for dns/R2/zerotrust, sentry (incredible tool - major part of how I shipped as much as I did as quickly as I did), redis upstash, bullMQ, Unsplash, stripe, huskyCI, Semgrep, and probably a few more I am missing. - Is it going to sell? I don’t know. - Is it technically capable and unique? I think so - Am I super proud of myself? Hell yes. - Are there bugs? You tell me, typically squash then in staging environment with help of sentry, but something may have gotten past me certainly! - What does it do? Convert web visitors to leads with custom agents, in under 5 minutes. Roast me, or give me some feedback! www.wengrow.app Moment that stand out: - The velocity in general - Shipping enterprise level SSO (supabase auth) in a few hours - Rapid CRO optimization of onboarding flow. having done this work before leading large engineering and product teams the work I did in 24 hours would have taken a cross functional team of 5 weeks at a minimum. - Cookie consent management. Having previously spent months at prior job trying to do CCM right with a paid tool, I was able to set up a compliant CCM process on www in hours with c15t including audit logs sent to my Supabase DB, and proper handing of California nuances. - so much more but I need to catch up on some sleep submitted by /u/berrism [link] [comments]
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Deep analysis of pinecone-io/pinecone-python-client — architecture, costs, security, dependencies & more
Pricing found: $20/month, $50/month, $50/month, $300, $500/month
Pinecone has an average rating of 4.5 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: Performant, Serverless, Reliable, Secure, Real-time indexing, Tiered storage, Fast accurate reads, Semantic search.
Pinecone is commonly used for: What teams build with Pinecone.
Pinecone integrates with: OpenAI, AWS Lambda, Google Cloud Platform, Microsoft Azure, TensorFlow, PyTorch, Kubernetes, Apache Kafka, Elasticsearch, Jupyter Notebooks.
Pinecone has a public GitHub repository with 424 stars.
Aidan Gomez
CEO at Cohere
1 mention

Come build with Pinecone
Apr 3, 2026