The Same Pinecone, just without Servers, and The Cost...

Pinecone has unveiled their new serverless vector database, which is designed to reduce the cost of infrastructure management while helping to improve the accuracy of Gen AI applications. This is a game changer when you look at both the costs and the demand today do the explosion of all things Gen AI.

Pinecone Serverless is now in public preview in AWS cloud regions and work is underway to make the new database available on Microsoft Azure and the Google Cloud Platform next.

Based in New York City, Pinecone is a vector database?specialist whose capabilities enable users to store, discover and operationalize unstructured data to train applications and models used to inform and drive business decisions. To date, the vendor has raised $138 million, including $100 million in April 2023 and?$28 Million in March of 2022.

Vectors are numerical representations of unstructured data, such as text and video, that give the unstructured data a value, or structure, so it can then be searched and discovered. Vector databases, meanwhile, date back to the early 2000s, but until Gen AI came to the surface, they were very small players in the data space, that has dramatically changed.

Historically, vectors were used by search-driven organizations that collected massive and what seemed like (at the time) ridiculous amounts of data. Vectors and vector databases enable similarity searches much like graph databases also do, allowing users to sift through massive amounts of “messy” data to discover relevant information amid potentially billions of other data points.

While a niche product for nearly the last 20 years, vector databases have skyrocketed in popularity over the past 12 months. Enterprises have embraced their value in helping train generative AI large language models (LLMs), such as ChatGPT, Bing, Claude, and Google Bard, with proprietary data so they can understand a specific enterprise's operations and deliver useful and accurate results.

In particular, vector databases have gained popularity because they can help train RAG pipelines (retrieval-augmented generation), according to Doug Henschen, an analyst at Constellation Research.

"Vector embeddings help data scientists with RAG to improve the accuracy and relevance of LLMs based on an organization's own data," he said. "Developers can then deploy these more accurate models as well as more accurate search capabilities within the context of company- and industry-specific applications to drive better results."

Donald Farmer, founder, and principal of TreeHive Strategy, had this to add:

"Vector databases are critical for providing the large volumes of vectorized text and data that LLMs need," he said. "By efficiently storing and indexing vector data, they enable significant improvements to AI application quality, reducing hallucinations and enabling LLMs to generate outputs, which adhere to business rules for a specific scenario."

Pinecone isn’t the only player with Vector; however, other vector database specialists include Milvus and Chroma, among this growing field. Some other database and data platform providers that now offer vector databases include?MongoDB and Snowflake, just to name a couple.

New capabilities

The cost of cloud computing has caught many organizations off guard, even when they thought they were prepared for the costs.

Since cloud first burst onto the scene with promises of cost savings and performance gains of IT administrators and VPs dreams, the cloud has become the de facto environment for most data management and analytics operations as well. Meanwhile, the prices for using cloud-based platforms are seemingly low at just a few cents per user per minute or a few dollars for a given amount of compute power.

The massive and ever-growing volume of data organizations now collect, and rightfully so given the astronomical value of it, and the value of it for decision making, along with the number of users spending large amounts of time using cloud-based tools, the cost of using the growing cloud platforms that make up an organization's data infrastructure has, in many cases,?doubled or tripled (or more) expectations and budgets.

As a result, organizations are trying to better control cloud computing costs. Vendors are attempting to provide efficient tools that help customers predict and limit usage and spending. There is an almost limitless list of vendors that provide cloud cost tooling and more come to market every week.

One of those ways vendors are providing more efficient tools is with an architecture that is gaining in adoption as enterprises continue to look at efficiencies and optimization options, it’s serverless architecture, and most of us are big fans already. Serverless cloud services automatically scale up or down without committing or even paying in advance for compute and storage that may be used, or not used.

Pinecone’s introduction of Pinecone Serverless on Jan. 16 has the potential to benefit both existing customers looking to lower costs as well as attract new users seeking cost control, and in some cases, just to experiment with it.

"Pinecone is a startup, so it doesn't have a huge customer base at this point," Henschen said. "This will give existing customers the option to switch to the serverless cloud service. But the company's broader hope is surely to attract more new customers that might have balked at having to set up, administer and scale a database using the old-school, manual approach."

Pricing for Pinecone's traditional DB is based on usage, which includes storage and read and write units. They haven’t disclosed what they charge for use but they do provide a cost calculator on their website.

The vendor, meanwhile, claims that its new serverless database has the potential to result in significant cost savings compared with using databases that require back-end infrastructure management.

Public preview pricing for Pinecone Serverless is 33 cents per gigabyte, per month for storage; $8.25 per million read units; and $2 per million write units.

Future plans

It is worthwhile to note that search and storage capabilities are gaining popularity given their enablement of RAG pipelines within generative AI development, Pinecone and other vector database vendors will still certainly face stiff competition from other database vendors.

But these Vector DB specialists are positioning themselves by offering products that are better suited to the needs of users. These come with capabilities that differentiate them from the vector databases developed by broader-based data management vendors, such as?Databricks and others – this is important as they battle for market space.

Whether those capabilities?bring enough to the table to convince an enterprise to add a new product to their data infrastructure, especially when they can potentially add something similar from a provider they already use for other needs, remains to be seen. Competition always breeds innovation, and I can’t wait to see how this shakes out. Consumers typically win in situations like this, from better pricing to better products as a whole.