Overview

With the launch of ChatGPT and other AI tech, companies are all in on adding AI to their products. But here's the thing: AI isn't just about fancy algorithms; it's also about handling a ton of data, specifically vectors. Vectors are basically the structured form of all the messy data like images, text, and videos that AI needs to understand.

In pretty much every AI application you can think of—whether it's recommending a movie on Netflix or spotting weird stuff in security footage—a neural network takes an input and turns it into a vector. Then it does whatever it needs to do, like making a recommendation or flagging an anomaly.

Big tech companies like Google and Amazon get this. They've got their own custom tech to handle vector data efficiently. But what about everyone else? According to a recent report, 80% of all data will be unstructured by 2025. That's a massive 140 billion terabytes that needs to be turned into vectors and analyzed. Simply put, that's a huge headache.

The problem is that vectors are a pain to work with. Imagine trying to quickly find one specific item in a warehouse the size of a city—yeah, it's like that but for computers. You need special methods to search through and handle all these vectors, especially when you're talking about billions of them.

So here's the big opportunity: we need a database just for vectors. If someone can figure out how to store and search through vectors as easily as we do with regular data, they're going to make a killing. It's a multi-billion-dollar problem waiting for a solution.

Where Does Pinecone Fit In All This?

Pinecone is a database built specifically for vectors, making it super easy for developers to store and search through the vectors generated by their AI models. It's a game-changer for building all sorts of AI applications.

Here's how Pinecone makes life easier for engineering teams:

• Quick Database Queries

Normally, searching through vectors in a database is complicated and needs special indexing methods based on algorithms like approximate nearest neighbors (ANN). Pinecone takes care of all that behind the scenes. You can just use a simple REST API to query your data, saving you the hassle of figuring it out yourself.

• Easy Scaling

To handle more data and run faster, databases need to be spread out across multiple machines. Pinecone does this automatically. It shards and distributes itself over multiple compute instances, parallelizing requests and spreading out replicas to make the system faster and more reliable.

One cool thing about Pinecone that sets it apart is its free tier for small projects. It might sound trivial, but having a free tier is a big deal. It lets developers try out Pinecone without any roadblocks, making it easier to test ideas and see if Pinecone is the right fit for the long haul.

The free tier gives you enough storage for about 1 million vectors with 768 dimensions each. If you need more, they've got paid plans that bill by the hour, similar to how AWS does it.

Pinecone hit the scene in 2021 and has been quickly picking up customers, from startups that are taking off to big names in the Fortune 500.

The company was started in 2019 by Dr. Edo Liberty, who knows his stuff. Before Pinecone, he was the Head of Amazon AI Labs and played a key role in developing Amazon's SageMaker and ElasticSearch. In 2022, the company raised a solid $28 million in a Series A funding round.

At its heart, Pinecone is all about being a Vector database that works in the cloud. Now, you might think they're up against tough competition, especially with big cloud providers like GCP and AWS also offering Vector databases. Azure is catching up too. But here's where Pinecone stands out: their proprietary indexing algorithms. These algorithms are serious engineering work, and they're hidden behind a super user-friendly developer experience. This gives Pinecone a big leg up over the competition.

But Pinecone isn’t just its indexing algos. In this article, I will convince you that Pinecone is set to be the next MongoDB.

Market Leadership

Pinecone isn't the only player in the vector database game. There are other open-source options like Milvus and Weaviate to name a few. But even with open source competitors, Pinecone stands out

Pinecone is The Superior Product

Picking the best vector database is not exactly straightforward— you can’t just go with the one that has the best tech specs, like highest recall, fastest queries, and lowest latency.

It's not that simple for a couple of reasons:

• People look for more than just top-notch tech. Pricing, compliance, developer support — there’s a million things that organisations need to consider before investing a large chunk of capital
• The idea of "best technology" gets fuzzy when open-source options are in the mix. The technical performance of all competitors tend to become quite similar due to public facing codebases. In fact, the leading vector databases, Pinecone included, all have pretty similar core performance. This is a pattern that has already been demonstrated with graph and document-based databases.

When it comes to managed developer services, Your indexing accuracy or the latency of API calls isn’t really all that relevant. What you're actually selling is a better overall experience for developers: something that's easier to use, quicker to set up, and less of a hassle to manage.

The best product is the one that offers the top developer experience, and Pinecone nails it in this department.

• Pinecone stands out as the only managed service that offers a free tier. This is a game-changer for developers who want to try things out without reaching for their wallets. With Pinecone, you can easily sign in using Github, set up an index, and upload your vectors. If you go with an open-source option, you're on your own for installation and setup. Thanks to its hassle-free free tier, Pinecone gets you up and running way faster than other options out there, making it a favorite for startups and fast-growing companies that need vector search solutions.
• Pinecone's early entry into the managed vector database market gave them a leg up. They started gathering insights from their paying customers before anyone else. This two-year advantage has allowed them to fine-tune their offerings based on real developer needs. For example:
• Pinecone introduced hybrid search in their database, letting developers mix vector queries with metadata filters.
• They were pioneers in allowing partial updates on vectors and metadata. With other platforms, you'd have to delete and then re-upload vectors, which isn't efficient.

Developers working with Pinecone can move at a faster pace, thanks to their array of developer-centric features and tools.

• From the moment you start with Pinecone to the time you're deep into development and even after deployment, Pinecone offers a seamless developer experience, making it the top choice in the vector database category.

Competitive Landscape

We've delved into what makes Pinecone stand out in the market. Now, let's compare it with other solutions designed for vector storage and search.

Libraries for Vector Search

There are several libraries out there that assist developers in searching vast vector collections for groups or similar items. Some of the renowned ones are Google’s ScaNN with 24k stars, Facebook’s Faiss boasting 17.3k stars, and Spotify’s Annoy with 10k stars.

While these libraries excel in vector search, they aren't full-fledged databases and can struggle when scaling up. For instance, distributing indices that exceed system RAM over multiple systems for scalability and reliability can be a hurdle for developers.

Conventional Search Tools

Classic search solutions like Elasticsearch are now incorporating vector search features. Built on Apache Lucene, Elasticsearch 8.0 brought in advanced ANN search, and its 8.2 version added hybrid search with boolean filters. AWS's Opensearch offers similar functionalities.

However, these tools have their limitations. For starters, they aren't genuine databases. Elasticsearch isn't crafted to be the main data repository. It lacks fault tolerance (data can be lost with minor component failures) and isn't user-friendly (fixed index sizes make adding or removing vectors a challenge). Moreover, they are significantly slower than databases specifically designed for vectors.

Dedicated Vector Databases

Databases specifically designed for vectors address many challenges faced by other solutions. They cater to the complex querying needs of various applications and tackle the sharding issues linked with scalable storage. Some notable ones are:

• Milvus - An open-source vector database that's docker-friendly. It's a recognized project under the Linux Foundation’s AI & Data wing, boasting 11.2k stars on Github.
• Vespa - Another open-source vector database with 4k Github stars.
• Weaviate - A unique open-source vector search engine and database that uses a Graphql-like query language. It has 2.5k stars on Github.
• Qdrant - An open-source vector search engine and database offering a REST/gRPC API interface. It's garnered 1.9k stars on Github.
• Vald - An open-source, cloud-native vector search engine with 946 Github stars.

While these self-hosted vector databases are an improvement over simple vector search libraries, they demand massive setup efforts from developers to ensure scalability without compromising on speed or availability. They lack certain security assurances (like GDPR or SOC 2 Type 2) and make developers have to deal with the operational challenges of maintaining infrastructure, overseeing services, and troubleshooting issues. This is where managed vector databases shine.

Managed Vector Databases

Managed vector databases take the hassle out of scaling, security, monitoring, and availability concerns, drastically cutting down both the launch time and upkeep expenses.

Pinecone pioneered the managed vector database domain. Even with other entities slowly entering the space, Pinecone remains the top choice, especially with its free tier offering. Here are some other contenders in the space:

• Weaviate - Spearheaded by SeMI Technologies, a Dutch startup from 2019 that has secured $17.2 million in funding. Their managed service is currently on a waitlist and doesn't offer a free tier.
• Milvus - Backed by Zilliz, a Chinese startup from 2017 with $53 million in funding. They're yet to launch a managed service but have plans in the pipeline.
• GCP Vertex AI Matching Engine - A product by Google, it's quite complex and arguably not very user-friendly. It demands a specific setup, including a Virtual Private Cloud (VPC) and has a lengthy index update process. More details on its intricate developer experience can be found here.

In terms of offering a smooth developer experience, Pinecone clearly stands out from its main rival, Vespa. With a significant lead in the market, Pinecone is poised to be a dominant force in the sector.

Comparable Companies

To give you a clearer picture, let's draw parallels with some leading companies in the database domain. These companies have carved a niche for themselves in the OLTP (online transaction processing) database sector and have achieved multi-billion dollar valuations. If Pinecone can position itself as the go-to Vector Database Company, its potential is vast, especially considering the growing significance of vector databases.

MongoDB

MongoDB, a US-based software firm, is a frontrunner in the document-centric NoSQL database arena. They offer storage solutions in JSON-like formats with adaptable schemas. In 2022, they reported revenues of approximately 880 million USD and have a market valuation hovering around 20 billion.

Redis

Originating from Israel, Redis is a key player in the dictionary-centric NoSQL database segment. Their specialty lies in providing a distributed, in-memory key-value storage system. In April 2021, they secured a 110 million USD in a Series G funding round, pushing their valuation to 2 billion.

Looking Ahead

At present, Pinecone's primary goal is to perfect their vector database offering. Edo has mentioned that they're not diversifying their product range in the near future.

However, looking further down the road, Pinecone could evolve beyond just being a database provider. We envision a scenario where Pinecone might venture into model hosting services, essentially becoming a comprehensive platform for the entire vector data workflow, somewhat akin to a commercial version of huggingface.

In such a scenario, Pinecone could emerge as a one-stop solution for storing, indexing, and serving unstructured data. Developers could directly upload unstructured content, which Pinecone would then process, vectorize, index, and partition. It's even conceivable that Pinecone could branch out into the OLAP (online analytical processing) database domain, positioning itself alongside giants like Snowflake or Databricks but for unstructured data.

Wrapping Up

The AI wave is surging, with vector data at its core. Pinecone, with its well-rounded, easy to use and robust vector database offering, in my opinion, is set to profit the most from this wave.