MongoDB Atlas Vector is highly praised for its seamless integration capabilities, especially for developers working with PHP and AI applications. Users commend its robust vector search functionality and real-time data handling, as highlighted by the positive ratings averaging around 4.8/5 on platforms like G2. However, there are few mentions of specific complaints, suggesting a general satisfaction with the tool. Pricing sentiment appears positive, with users not expressing concerns over costs, and overall, MongoDB Atlas Vector enjoys a strong reputation for aiding skill development and promoting community engagement through initiatives like skill badges and user groups.
Mentions (30d)
37
15 this week
Avg Rating
4.5
20 reviews
Platforms
3
Sentiment
8%
10 positive
![MongoDB Atlas Vector screenshot 2](https://mongodb.com/atlas/"https://cdn.cookielaw.org/logos/static/ot_close.svg")
MongoDB Atlas Vector is highly praised for its seamless integration capabilities, especially for developers working with PHP and AI applications. Users commend its robust vector search functionality and real-time data handling, as highlighted by the positive ratings averaging around 4.8/5 on platforms like G2. However, there are few mentions of specific complaints, suggesting a general satisfaction with the tool. Pricing sentiment appears positive, with users not expressing concerns over costs, and overall, MongoDB Atlas Vector enjoys a strong reputation for aiding skill development and promoting community engagement through initiatives like skill badges and user groups.
Features
Use Cases
Industry
information technology & services
Employees
5,600
502,300
Twitter followers
Better Database Authentication with Better-Auth https://t.co/f0ufzb3Hm9
Better Database Authentication with Better-Auth https://t.co/f0ufzb3Hm9
View originalg2
What do you like best about MongoDB Atlas?I use MongoDB Atlas for hosting our MongoDB, and I appreciate its reliability; I've never had any problems with its uptime. I really like the tools it offers for scaling and performance monitoring, as they are easy to use with a nice user interface. It's great that new MongoDB versions are deployed as soon as they are released, allowing me to use new features without any delay. The initial setup was easy without any problems, which I really value. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?I think their alerting system may be a bit improved, for example so I can setup more granular alerts. For example, I can't set up composite rules (for example error rate of specific endpoint + CPU threshold, etc). Basically, it's limited in customization. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?easy UI, very intuitive, good documentation Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?there is nothing I dislike - customer support might respond faster Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?Automated scaling, backups, monitoring and performance alerts make it incredibly easy to maintain clusters without dedicating a team to infrastructure. Added to that, the UI is intuitive, queries run fast and features like Atlas Search, Charts and built-in security controls help ship features quickly. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?Some configuration options feel hidden behind tiers. Also, greater transparency around cost optimization or in-platform recommendations would make the experience even smoother. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?I appreciate MongoDB Atlas for making database management more user-friendly with its intuitive UI, which makes it more comfortable to work with compared to using only the terminal. The feature for managing MongoDB aggregations is particularly beneficial, allowing me to build queries from smaller parts efficiently and intuitively, making the process significantly easier than doing it manually. I highly value how MongoDB Atlas helps me work seamlessly with databases, especially when I am developing and need to check the state of my data. Its performance is fast, which I like, and it integrates well when I am working on the backend with Visual Studio Code. Overall, it's easy to set up if environment variables are configured correctly, making the whole process straightforward. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?I think it works great , I don't see anything to improve particularly Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?Flexibility, Ease of use and efficiency as well database connection and interaction while development phase. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?For the NoSQL databases, it's fine; I didn't feel that anything was wrong. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?simplicity, ease of use, scaling, sharding, data management Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?Sometimes the database clusters take time while loading the data Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?It good as it store different types of data structures, different types of documents as it as good scalability and has good performance. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?It doesn't support multi document ACID and contains high memory usage which has data inconsistencies sometimes. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?I use MongoDB Atlas for a document database and appreciate its faster response and easy search with indexing. I really like sharding in MongoDB Atlas because it splits the data equally to all the nodes, which allows it to handle multiple reads and write operations. Setting up MongoDB Atlas is straightforward and easy. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?I am not a fan of the default ID that MongoDB Atlas creates for every entry. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?I have nothing good to say about MongoDB Atlas. Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?My story with Mongo began when I started a new software position, and they had a legacy version of their software product using Atlas. Compared to our other infrastructure bills, Mongo was significantly higher for the amount of compute and storage we used ($3K per month). This is a managed service, so you would expect to pay a premium. Ok, sure, but then I expect great functionality, performance, and support. The main problem began with Mongo when we needed to delete some data because they tie the CPU and memory tiers to storage size, so we were overpaying. Our application would run fine off an M10 dedicated cluster (the smallest tier), but it had automatically scaled to an M50 because of storage. This is already a bit disappointing because they are forcing customers to pay for more compute and memory than they need. So we started deleting some data, but then we ran into problems. The data deletion process was really slow and also slowed our entire cluster down, causing lag and performance issues for our end users. But hang on, this makes no sense because we are paying for more CPU and RAM than we need, so why would we have this issue? It took us three months to delete 500GB of data. In the meantime, our bill remained the same because you can't claim the space back without compacting the database. Ok, fine. So we ran compact(), but we only freed ~100GB on the secondary clusters. Support gave us a script to run that can see how much storage can be freed. In the end, we had to activate an expensive additional support plan costing us $500 USD per month to get support to run a re-sync command. This should have taken their support people 10 minutes, but instead, they mucked us around going back and forth on the ticket, taking three weeks to resolve. A year later, we needed to delete some more data. We spent another five months deleting 800GB of data. Then we ran compact() and freed 300GB. Where is our other 500GB? We contacted some humans at Mongo, who really couldn't do much other than suggest we get funding to cover the $500 support for one month. Yes, we got the $500 credit, but when I went to reactivate support, it was going to charge us for three months for one month because Mongo retroactively bills you for three months when you reactivate. Wow, we started in a bad place, now I'm beyond frustrated; this is daylight robbery. To this day, I am still fighting to reclaim some storage, but at this point, I'm going to recommend to our CEO that our dev team put some effort into moving away completely from Mongo. I also need to mention that Mongo recommended we use their online archive features, but when we crunched the numbers, it was still quite expensive, and we would have to do significant work to make our application work between the regular clusters and online archive. So it was significantly more logical to just put the data in AWS S3, then delete it in Mongo. If I can summarize my experience with Mongo, and I acknowledge mine is probably quite different to most, here it is: Overpriced for the performance you get Sneaky billing model where they tie CPU and memory to storage Terrible and expensive support Sneaky extra charges on reactivating support Bad support escalation solutions - they couldn't just turn on free 'support' Poor database performance Slow delete operations Ecosystem lock-in Forced upgrades - no LTS releases Let me sum it up this way: if your compact() command does not free up the space that is available on your cluster, then provide the customer with free support to do so. I hate dealing with Mongo. Nothing is simple, everything is expensive, and the performance sucks. If you are considering using Mongo, find something else. Even if you have to take a bit more time to learn AWS Dynamo, S3, or Aurora, you should do it; you will save time and money in the long run. Mongo, you deserve this negative review. I have given you plenty of opportunities to resolve things and have escalated issues, but you just don't care. We wanted to move away from Mongo before; now I can't get rid of it fast enough. Review collected by and hosted on G2.com.
What do you like best about MongoDB Atlas?Portability, easy to fire up, and requires less resource Review collected by and hosted on G2.com.What do you dislike about MongoDB Atlas?So far nothing I have found that concerns me about mongo Review collected by and hosted on G2.com.
Continual learning in mid-2026. A map of everyone trying to crack it: memory layers, "dreaming" agents, and the Post-Transformer models that learn inside the network
Llion Jones said “2026 is the continual learning year” in the recent Post-Transformer debate. Sutton/Silver call the next phase the "era of experience”. What’s continual learning? Simply put, it’s a model’s ability to continuously improve as it gains experience – without exhibiting catastrophic forgetting. Essentially the stability-plasticity tradeoff for a reasoning model. Essentially it comes down to: where does the memory live? Outside the model. Memory files, vector dbs, graphs. Text is retrieved and pasted back into context. The model stays frozen. In the model's running state. Hidden states or fast weights that change while the model processes input. In the model's weights. What it actually knows. Encoded within the model weights to improve decision making patterns without forgetting. Dev docs today hint at #1 - memory outside the model. But the “2026 is continual learning year” notion does not come from it. Why? Part 1: The Memento stack (today’s stack) There are engineering fixes for the LLM’s memory problem. Julian Togelius & a16z compared it to Memento. In the movie, Leonard functions with his Polaroid and notes. But everyday he is the same man as day 0. Progress around these include: Anthropic's Dreaming: an async job to manage “memories”, explicitly modeled on sleep consolidation. Long context as memory: Visibly good, but with 3 problems. a) Position bias and "lost in the middle" challenge. b) Longer LLM windows come with bigger costs and we’re already discussing “token economics”. c). KV cache bottleneck, and everything evaporates when the request ends. Mem0, Letta, Zep: the popular memory-layer products from startups. AGENTS.md and git-style memory files: But, in this ETH Zurich paper (arXiv 2602.11988) it showed that LLM-generated context files actually reduce task success by about 3% while raising cost over 20%. And human-written ones barely helped too. Part 2: Continual learning, memory within the model (the big bet) Weight updates in large networks trigger catastrophic forgetting. A January 2026 paper tried continual fine-tuning on LRMs (arXiv 2601.18699) but catastrophic forgetting didn’t fade but rather increased. Promising directions that could solve this: TTT layers (arXiv 2407.04620, ICML 2025): the hidden state of the sequence layer is a small model, updated by gradient descent on tokens as they stream in. Matches or beats Transformer / Mamba baselines upto 1.3B params. Titans & Atlas: Titans add a neural long-term memory that decides what to store using a surprise signal. Atlas upgrades the memory's learning rule. Nested Learning + HOPE: Architecture updates different blocks at different frequencies. RNNs are also coming closer to Transformers via viral Memory Caching papers. Dragon Hatchling (BDH): From AI lab Pathway (arXiv 2509.26507). Working memory lives in Hebbian synapses rather than in a KV cache, allowing for an "infinite context window" without quadratic cost. AMI Labs, LFMs, etc. also mention continual learning but I didn’t find much specific info on them in this front. Current State and Future Outlook Where is continual learning in mid-2026? Solved with public access: nothing. Shipping in production: only the dossier stack, all frozen models. Demonstrated at research scale (< 2B params): TTT, Titans, Memory Caching, HOPE, and BDH. What would move the needle imo: Ship memory within the model with forgetting measurably controlled. Two questions though: What OpenAI is brewing in all of this? What’s the blocker to adoption for continual learning models: the missing breakthrough itself, or evals, serving economics, etc? submitted by /u/Ok_Can_1968 [link] [comments]
View originalSwitching from React Native + Node.js (4 YOE) to Agentic AI — need roadmap advice
I have 4 years of experience as a React Native and Node.js developer. I am comfortable with REST APIs, async/await, JSON, MongoDB, authentication, and shipping production apps. I am based in India. What I have learned so far: I recently completed an AI/LLM course that covered: • Pydantic (validation, models, serialization) • LLM theory (transformers, embeddings, attention, tokenization) • OpenAI and Gemini API integration • Prompt engineering (zero-shot, few-shot, CoT, persona prompting) • Prompt formats (ChatML, Alpaca, INST) • Ollama for local LLMs • FastAPI basics • Hugging Face model deployment • Agentic AI fundamentals — built a basic CLI coding agent What I understand conceptually: I understand that an AI agent = LLM brain + tools (Python functions) + agent loop + memory (messages list). I understand RAG, vector databases, the difference between fine-tuning and RAG, and how to structure a backend with Node.js calling a Python AI agent service when needed. What I want to do: I want to transition into Agentic AI / AI Engineer roles in India. I am not looking to become an ML researcher or train models. I want to build production AI agent systems — connecting LLMs to real business data, building tools, RAG pipelines, and shipping real products. My specific questions: 1. Is my current foundation strong enough to start building real agent projects or do I have gaps I am missing? 2. What should my learning roadmap look like for the next 3–6 months given my background? 3. Which frameworks should I prioritise — raw OpenAI API first, then LangChain/LangGraph, or jump straight to frameworks? 4. What kind of projects should I build for a strong portfolio targeting ₹20–35 LPA roles in India? 5. Any specific subreddits, communities, or resources beyond YouTube that helped you in this transition? My planned first 3 projects: • Simple agent with web search + calculator tool (no DB) • Agent connected to MongoDB with RAG • Full FastAPI backend wrapping the agent with a React frontend Any advice from people who have made a similar switch or are hiring in this space would be really helpful. Thanks. submitted by /u/rohitrai0101rm [link] [comments]
View original@AnthropicAI Learn more about MongoDB’s security investment in the age of AI: https://t.co/8P0CMnyunz
@AnthropicAI Learn more about MongoDB’s security investment in the age of AI: https://t.co/8P0CMnyunz
View originalThe software the world runs on has to be secure. MongoDB has joined Project Glasswing, @AnthropicAI's initiative to secure the world's most critical software for the AI era, working alongside organiz
The software the world runs on has to be secure. MongoDB has joined Project Glasswing, @AnthropicAI's initiative to secure the world's most critical software for the AI era, working alongside organizations like Apple, Google, Microsoft, and NVIDIA to strengthen the security and https://t.co/6dGvKlXSfY
View originalArek Borucki started with a MongoDB University course and ended up as an ML Engineer at @huggingface. 🧑💻 What he shared about scaling MongoDB in production applies to your stack right now.👇 https
Arek Borucki started with a MongoDB University course and ended up as an ML Engineer at @huggingface. 🧑💻 What he shared about scaling MongoDB in production applies to your stack right now.👇 https://t.co/RhcFDdcNUn
View originalMost agent failures are actually data failures. Teams keep tuning prompts, swapping models, and adding guardrails when the real issue is data that isn't retrieval ready, stale context, or workflows th
Most agent failures are actually data failures. Teams keep tuning prompts, swapping models, and adding guardrails when the real issue is data that isn't retrieval ready, stale context, or workflows that can't maintain state. MongoDB Field CTO Pete Johnson breaks down the three https://t.co/MXXCBRyzBG
View original"No matter what AI workload you run, you always need LLMs, a harness, and a data layer." That's MongoDB President and CEO @cj_mongodb on @Bloomberg Open Interest, making the case that the data layer
"No matter what AI workload you run, you always need LLMs, a harness, and a data layer." That's MongoDB President and CEO @cj_mongodb on @Bloomberg Open Interest, making the case that the data layer is where agentic AI lives or dies, and that it has to scale as those workloads https://t.co/VTTF4zKOEy
View originalHow long does it REALLY take to earn our new Gen AI Skill Badges? We put it to the test at MongoDB.local London. Can you beat her time? 👀 Start earning now: https://t.co/OTWIEtQOAC https://t.co/b
How long does it REALLY take to earn our new Gen AI Skill Badges? We put it to the test at MongoDB.local London. Can you beat her time? 👀 Start earning now: https://t.co/OTWIEtQOAC https://t.co/bgNQoig1hB
View original“Lift and shift” isn’t modernization. Today’s applications need to be modular, AI-ready, and built for real-time decision-making. Legacy systems can’t keep up with the demands of always-on consumer e
“Lift and shift” isn’t modernization. Today’s applications need to be modular, AI-ready, and built for real-time decision-making. Legacy systems can’t keep up with the demands of always-on consumer experiences. Hear how organizations in APAC are modernizing faster, scaling in https://t.co/hSbl5mcWHR
View originalMay was a busy month for our MongoDB Community members and startups! 💚 A BIG congratulations to: MongoDB Champion of the Month - Abirami Sukumaran MUG Leaders of the Month - Brice Fotzo and Abdul R
May was a busy month for our MongoDB Community members and startups! 💚 A BIG congratulations to: MongoDB Champion of the Month - Abirami Sukumaran MUG Leaders of the Month - Brice Fotzo and Abdul Rahman Masri Attal Creator of the month - Matteo Rossi Startup of the month - https://t.co/Jx2pSo21Gf
View originalRT @cj_mongodb: .@MongoDB just reported a strong first quarter. Total revenue reached $688 million — up 25% year-over-year — driven by fo…
RT @cj_mongodb: .@MongoDB just reported a strong first quarter. Total revenue reached $688 million — up 25% year-over-year — driven by fo…
View originalMongoDB just announced Q1 FY2027 earnings. Highlights include: 📈 Total revenue of $687.6M, a 25% increase YoY ☁️ A 29.4% YoY increase in Atlas revenue, accounting for 75% of total Q1 revenue 🤝
MongoDB just announced Q1 FY2027 earnings. Highlights include: 📈 Total revenue of $687.6M, a 25% increase YoY ☁️ A 29.4% YoY increase in Atlas revenue, accounting for 75% of total Q1 revenue 🤝 2,500+ additional customers for a total of more than 67,700 customers Read more https://t.co/dR1960oEfZ
View originalMeet PlanPass AI - the winner of the Agentic Evolution Hackathon at MongoDB.local London! In the UK, navigating building compliance for house design can be incredibly complex for builders and develop
Meet PlanPass AI - the winner of the Agentic Evolution Hackathon at MongoDB.local London! In the UK, navigating building compliance for house design can be incredibly complex for builders and developers. PlanPass AI uses an agentic workflow to: -Scrape local council documents https://t.co/tDdWqs4Vy9
View originalAfter talking to hundreds of customers and startups, @cj_mongodb sees three things that will hold in the AI wave: the model layer, the data layer, and the agent layer. 🎥 Watch the full interview wi
After talking to hundreds of customers and startups, @cj_mongodb sees three things that will hold in the AI wave: the model layer, the data layer, and the agent layer. 🎥 Watch the full interview with @HarryStebbings to learn why MongoDB is built to serve as the long-term https://t.co/7ltBYs39jA
View originalIn every tech transformation, something changes. But one thing has stayed constant. Our President & CEO Chirantan @cj_mongodb joined @HarryStebbings of @20vcFund to discuss why data remains the
In every tech transformation, something changes. But one thing has stayed constant. Our President & CEO Chirantan @cj_mongodb joined @HarryStebbings of @20vcFund to discuss why data remains the constant across every major technology shift — from mainframes, to cloud, to AI. https://t.co/5fJUpwf1qI
View originalMongoDB Atlas Vector uses a subscription + tiered pricing model. Visit their website for current pricing details.
MongoDB Atlas Vector has an average rating of 4.5 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: General Information, Documentation, Community Forums, University, Manage Consent Preferences, Strictly Necessary Cookies, Opt Out of Third Party Cookies, Cookie List.
MongoDB Atlas Vector is commonly used for: Medical consultation analysis, Recommendation systems for e-commerce, Natural language processing applications, Image and video content retrieval, Fraud detection in financial services, Customer sentiment analysis.
MongoDB Atlas Vector integrates with: VoyageAI for embeddings, Apache Kafka for data streaming, AWS for cloud infrastructure, Google Cloud Platform for scalability, Microsoft Azure for hybrid cloud solutions, Elasticsearch for advanced search capabilities, Grafana for monitoring and analytics, Kubernetes for container orchestration, Jupyter Notebooks for data science workflows, TensorFlow for machine learning model deployment.
Based on user reviews and social mentions, the most common pain points are: down, critical, right now.
Based on 122 social mentions analyzed, 8% of sentiment is positive, 92% neutral, and 0% negative.
