Back in the day, our version of a "free tier" was simply: "Go use the open-source version."
While that open-source version now gets hit millions of times per month (which, FWIW, is incredible ๐คฏ), Weaviate has evolved into so much more than just a database
๐งต๐
Just back from Greece with our rockstar Weaviate Engineering & Product team ๐ One week, fully locked in ๐ฅ
We aligned on our roadmap, had a real debate about the coding agents transformation, and ran a hackathon where we shipped a TON of great new features, all building on Weaviate ๐
This team is unreal. Remote-first works, but put them in one room for a week and you see magic!
๐ฅ The Weaviate team is on absolute fire this month, shipping one major update after another!
๐ฃ๏ธ Today, I am incredibly excited to roll out a feature requested by MANY of our community members
๐ We have just launched a ๐๐ฅ๐๐ ๐๐ข๐ฅ๐๐ฉ๐๐ฅ ๐ง๐๐๐ฅ on Weaviate Cloud!
๐ข Toot! The team is shipping again!
Weโre seeing a massive wave of developers & agents building advanced AI memory capabilities on top of Weaviate. To make that even easier, we just launched Engram, a dedicated memory service built right on our database.
Most agent memory systems are just glorified context windows.
And this is exactly why production agents fail at scale.
We've been working on this for months, and it's finally here: ๐๐ป๐ด๐ฟ๐ฎ๐บ ๐ถ๐ ๐ป๐ผ๐ ๐๐.
If you've been building agentic applications, you know the problem. Agents that should get smarter over time stay flat instead. They forget user preferences, re-solve the same problems repeatedly, and waste tokens on work that can't be reused. Long context windows help, but cramming them full degrades accuracy, inflates costs, and increases latency.
๐๐ป๐ด๐ฟ๐ฎ๐บ ๐๐ผ๐น๐๐ฒ๐ ๐๐ต๐ถ๐.
It's a managed memory service built on Weaviate that ๐ข๐ค๐ต๐ช๐ท๐ฆ๐ญ๐บ ๐ฎ๐ข๐ช๐ฏ๐ต๐ข๐ช๐ฏ๐ด memory instead of just storing it. Asynchronous pipelines extract relevant information from raw data, reconcile it with existing memories (handling deduplication, preference changes, time-evolving facts), and persist clean, structured memory state ready for retrieval.
๐๐ฒ๐ ๐ฐ๐ฎ๐ฝ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ถ๐ฒ๐:
๐๐ถ๐ฟ๐ฒ-๐ฎ๐ป๐ฑ-๐ณ๐ผ๐ฟ๐ด๐ฒ๐ ๐๐ฃ๐ โ Add raw data and continue working. Pipelines run asynchronously in the background with durable execution.
๐ง๐ผ๐ฝ๐ถ๐ฐ๐ ๐ฎ๐ ๐บ๐ฒ๐บ๐ผ๐ฟ๐ ๐บ๐ฎ๐ด๐ป๐ฒ๐๐ โ Natural language descriptions that pull matching information from raw data. You control what's worth remembering.
๐ฆ๐ฐ๐ผ๐ฝ๐ฒ๐ ๐ณ๐ผ๐ฟ ๐ถ๐๐ผ๐น๐ฎ๐๐ถ๐ผ๐ป โ Project-wide, user-scoped, or property-scoped memories with hard and soft isolation enforced at the platform level.
๐๐ผ๐บ๐ฝ๐ผ๐๐ฎ๐ฏ๐น๐ฒ ๐ฝ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ๐ โ Extract, transform, buffer, and commit steps that manage memories dynamically based on data type and preferences.
๐๐๐ถ๐น๐ ๐ผ๐ป ๐ช๐ฒ๐ฎ๐๐ถ๐ฎ๐๐ฒ โ Memory retrieval inherits Weaviate's vector + keyword + metadata search on the same production stack you already trust, using native multi-tenancy to isolate instances.
Whether you're building chatbots that remember user preferences, agents that learn from experience, or multi-agent systems that need shared context, Engram gives you memory as infrastructure.
As a promotional offer, weโre giving $75 in credits for your first three months of Engram! Sign up before July 15th to claim it.
Read the blog: https://t.co/24koipQIjO
Get started: https://t.co/v9fMGMNCej
A few weeks ago, @philipvollet, @victorialslocum and @aestheticedwar1 joined the ๐๐ถ๐ด ๐๐ฒ๐ฟ๐น๐ถ๐ป ๐๐ฎ๐ฐ๐ธ with a mission: build an epic project with Weaviate, and win the top prize.
They only had 36 hours start to finish, and the concept was ambitious - combine trend-dectection on Twitter with persona-based AI content generation.
๐ง๐ต๐ฒ ๐๐ฒ๐ฐ๐ต ๐๐๐ฎ๐ฐ๐ธ:
โข Weaviate for storing and semantically searching Twitter data
โข Gemini models for content generation
โข Tavily for additional context retrieval
โข A custom scoring model built with Pioneer to rank trends
The system monitors Twitter to identify emerging trends using semantic clustering. When it finds something relevant, it analyzes your writing style and previous content to build a persona model. Then it generates new posts about those trends - but in ๐บ๐ฐ๐ถ๐ณ voice, not generic AI-speak.
The question is: did their sleep-deprived hackathon project manage to take home the 10k top prize?
Watch our new video to find out ๐
https://t.co/Uy1CeQT1TU
๐ฏ
The ability to mix structured and semantic information in a single query/prompt is what makes Weaviates Query agent so powerful.
This is not 2023 style vector search, this is 2026 agentic retrieval.
Small Change, Big Impact: Day 1/5: PQ Speed-up ๐โ๏ธ
Product Quantization just got ~60% faster on average between v1.34.7 and v1.34.8.
How? Why? It uses an optmization technique that's probably as old as coding itself. More in ๐งตโฌ๏ธ
Big update from the Weaviate Cloud team!
Weโve rolled out a ๐ป๐ฒ๐ ๐ฝ๐ฟ๐ถ๐ฐ๐ถ๐ป๐ด ๐บ๐ผ๐ฑ๐ฒ๐น to make it easier to understand what youโre paying for and ensure pricing scales with how you actually use the platform - no matter if you're testing ideas or running production workloads:
Here's what's new:
- New names: Serverless Cloud โ ๐ฆ๐ต๐ฎ๐ฟ๐ฒ๐ฑ ๐๐น๐ผ๐๐ฑ, Enterprise Cloud โ ๐๐ฒ๐ฑ๐ถ๐ฐ๐ฎ๐๐ฒ๐ฑ ๐๐น๐ผ๐๐ฑ
- New pricing dimensions: ๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฑ๐ถ๐บ๐ฒ๐ป๐๐ถ๐ผ๐ป๐, ๐๐๐ผ๐ฟ๐ฎ๐ด๐ฒ, ๐ฏ๐ฎ๐ฐ๐ธ๐๐ฝ๐
- New plans: ๐๐น๐ฒ๐ , ๐ฃ๐น๐๐, and ๐ฃ๐ฟ๐ฒ๐บ๐ถ๐๐บ
- Real-time cost visibility with a new pricing calculator
The result: transparent, predictable pricing that scales with your actual usage ๐
๐ฌย Read the full breakdown in our launch blog: https://t.co/v4vB2RhrfE
๐ย See the details on our pricing page: https://t.co/EIm0pUyC1A
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Check it this webinar to learn more! https://t.co/qIyKgAplja
Awesome company, awesome product - using @weaviate_io's vector database under the hood ๐
๐ข Big Shipping News: ๐๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ถ๐ป๐ด ๐๐ต๐ฒ ๐ค๐๐ฒ๐ฟ๐ ๐๐ด๐ฒ๐ป๐!
๐ Today is a major milestone for Weaviateโweโre officially launching the first of our three Weaviate Agents, starting with the ๐ค๐๐ฒ๐ฟ๐ ๐๐ด๐ฒ๐ป๐!
๐ Read the release blog: https://t.co/T8PZImp4XC
๐ ๐๐ถ๐ ๐ผ๐ณ ๐๐ถ๐๐๐ผ๐ฟ๐
When RAG (Retrieval-Augmented Generation) first started making waves, we immediately asked ourselves: What if RAG could go back into the database? This led us to develop the concept of ๐๐๐ฃ๐๐ง๐๐ฉ๐๐ซ๐ ๐๐๐๐๐๐๐๐ ๐๐ค๐ค๐ฅ๐จโa way to improve retrieval and generation continuously. As the idea evolved and agents took off, we refined our approach and terminology, making agentic AI a core part of the Weaviate ecosystem.
๐ Read more here: https://t.co/zhXnRzkhSX
๐ค๐๐ฒ๐ฟ๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ ๐๐ถ๐๐ต ๐๐๐ป๐ฐ๐๐ถ๐ผ๐ป ๐๐ฎ๐น๐น๐ถ๐ป๐ด
Out of this, the publication ๐๐ช๐๐ง๐ฎ ๐ฟ๐๐ฉ๐๐๐๐จ๐๐จ ๐ฌ๐๐ฉ๐ ๐๐ช๐ฃ๐๐ฉ๐๐ค๐ฃ ๐พ๐๐ก๐ก๐๐ฃ๐ was born. The Weaviate Labs team conducted extensive research to explore how we could build truly database-centric agentsโagents that donโt just retrieve data but actively enhance and evolve database management. This represents a major leap forward, blending AI-driven automation with traditional database operations in a way thatโs never been done before.
๐ Read the research paper: https://t.co/xw3eJpdS5K
๐ช๐ต๐ฎ๐โ๐ ๐ก๐ฒ๐ ๐โ
At Weaviate, we truly believe this is a huge step forward in building AI-native database technology. By merging traditional database operations with AI-powered agents, we're redefining what's possible in database management. And this is just the beginning.
๐ง๐ฟ๐ ๐๐ ๐ข๐๐ ๐ณ๐ผ๐ฟ ๐๐ฟ๐ฒ๐ฒโ
The Query Agent is now ๐ณ๐ฟ๐ฒ๐ฒ ๐๐ผ ๐๐ฟ๐ ๐ผ๐ป ๐ช๐ฒ๐ฎ๐๐ถ๐ฎ๐๐ฒ ๐๐น๐ผ๐๐ฑ. Check out the documentation and our press release for more details. We canโt wait to see what you build! ๐
๐ Documentation: https://t.co/XrAxPY7yM6
๐ Press release: https://t.co/cawFd7Gp0b
๐ to the whole Weaviate team, you all rock!
#๏ธโฃ #Weaviate #AI #VectorSearch #RAG #AgenticAI
PS:
Soon even more agentic releases!
๐ฅ The Weaviate team is on fire!
๐คฏ The month is almost over, but look at this laundry list of things shipped!
๐ข ๐ช๐ฒ๐ฎ๐๐ถ๐ฎ๐๐ฒ ๐๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐ ๐ถ๐ ๐ป๐ผ๐ ๐๐ ๐ถ๐ป ๐ช๐ฒ๐ฎ๐๐ถ๐ฎ๐๐ฒ ๐๐น๐ผ๐๐ฑ, featuring @SnowflakeDB's Arctic Embed 2.0
๐ข Dedicated Enterprise Deployment on ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐๐๐ฟ๐ฒ
๐ข ๐ต๐ฐ% ๐ณ๐ฎ๐๐๐ฒ๐ฟ ๐ธ๐ฒ๐๐๐ผ๐ฟ๐ฑ ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต withย BlockMax WAND
๐ข ๐๐๐น๐น ๐ฅ๐๐๐ ๐๐๐ฝ๐ฝ๐ผ๐ฟ๐ is now available as an enterprise feature
๐ข ๐ ๐๐น๐๐ถ-๐๐ฒ๐ฐ๐๐ผ๐ฟ ๐ฒ๐บ๐ฏ๐ฒ๐ฑ๐ฑ๐ถ๐ป๐ด๐ย now enable both queries and objects to be represented by multiple vectors (ColBERT-style "late interaction")
๐ข @nvidia integrationsย make it easier to build AI applications with embedding, search, and RAG
๐ข Async Replicationย enables ๐ฎ๐๐๐ผ๐บ๐ฎ๐๐ถ๐ฐ ๐ฐ๐ผ๐ป๐๐ถ๐๐๐ฒ๐ป๐ฐ๐ ๐บ๐ฎ๐ถ๐ป๐๐ฒ๐ป๐ฎ๐ป๐ฐ๐ฒ across a distributed deployment
๐งโ๐ฌ ๐ค๐๐ฒ๐ฟ๐๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ ๐๐ถ๐๐ต ๐๐๐ป๐ฐ๐๐ถ๐ผ๐ป ๐๐ฎ๐น๐น๐ถ๐ป๐ด on Arxiv
๐บ Three New podcasts
๐ Many new AI educational materials
๐ก Stay up-to-date here: https://t.co/RPuMA0eGT9
@TousEnsembleBe @weaviate_io Hi๐ The Weaviate Explorer Tool is currently in Early Access. Please DM me if you want me to add you to our Early Access program.
Imo the productivity amplification here is so large that organizations should be thinking about it as a basic work tool, like a new kind of spreadsheets++, given out eagerly and by default.
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