Software Alternatives, Accelerators & Startups

Qdrant VS TmpState.dev

Compare Qdrant VS TmpState.dev and see what are their differences

Qdrant logo Qdrant

Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

TmpState.dev logo TmpState.dev

TmpState (temp state) - a tokenless temporary JSON database. One curl creates a database; the URL is the only credential. No signup, no API keys, 24h free, $1 to keep for a week. Also a zero-key MCP server: https://tmpstate.dev/mcp
  • Qdrant Landing page
    Landing page //
    2023-12-20

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.

  • TmpState.dev Database Demo
    Database Demo //
    2026-07-05

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

Qdrant features and specs

  • Advanced Filtering
  • On-disc Storage
  • Scalar Quantization
  • Product Quantization
  • Binary Quantization
  • Sparse Vectors
  • Hybrid Search
  • Discovery API
  • Recommendation API

TmpState.dev features and specs

No features have been listed yet.

Analysis of Qdrant

Overall verdict

  • Qdrant is generally well-regarded for its performance and ease of use in managing vector data. Many users find it effective for building applications that require advanced search capabilities, particularly those involving machine learning models. However, its suitability can depend on specific project requirements and constraints, such as the existing tech stack and expected workloads.

Why this product is good

  • Qdrant is a vector database and similarity search engine designed for storing and querying high-dimensional data. It's especially effective for applications like neural search or recommendation systems, due to its ability to efficiently handle large-scale vector embeddings. Qdrant offers features such as real-time updates, seamless integration with existing data pipelines, and high availability, which make it an appealing choice for developers looking for a robust and scalable solution.

Recommended for

  • Developers building AI-powered applications
  • Companies needing efficient similarity search mechanisms
  • Teams implementing recommendation systems
  • Projects requiring real-time data processing
  • Applications dealing with large-scale vector data

Category Popularity

0-100% (relative to Qdrant and TmpState.dev)
Databases
94 94%
6% 6
Developer Tools
83 83%
17% 17
Search Engine
100 100%
0% 0
Backend As A Service
0 0%
100% 100

Questions & Answers

As answered by people managing Qdrant and TmpState.dev.

Why should a person choose your product over its competitors?

Qdrant's answer

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

TmpState.dev's answer:

Compared to jsonbin.io, npoint.io, json-server, or standing up Firebase/Supabase, TmpState removes the entire setup step:

  • No account and no keys - you get a working database from a single request, versus signing up and managing credentials elsewhere.
  • Faster to first write - one curl, not a dashboard, a project, and a connection string.
  • Built for agents - a native MCP server means your AI agent wires up its own storage instead of you pasting secrets into it.
  • Safe to abandon - deletion by default means no orphaned data or surprise bills; you only pay ($1 extension or $8/month Pro) when the data actually matters.

Best for throwaway and prototype state. It is honest about when not to use it: it is not meant to be your permanent production database.

What makes your product unique?

Qdrant's answer

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

TmpState.dev's answer:

TmpState is a tokenless temporary JSON database. One curl tmpstate.dev creates a real database and returns its URL - and that URL is the only credential. No signup, no API keys, no .env, no OAuth.

  • Zero credentials by design. The database URL is a capability (30+ characters of entropy, hashed at rest), the same trust model as an unguessable Google Docs share link. Nothing to provision, rotate, or leak into a repo.
  • Agent-native. It is also a zero-key remote MCP server, so an AI agent can create and use its own backend with no auth handshake - it self-onboards from llms.txt.
  • Ephemeral by default. Databases are free for 24 hours and expire automatically unless you keep them, so nothing lingers or bills silently.
  • Honest, transparent pricing. Free for 24h, one-time extensions from $1, always-on Pro at $8/month. Every charge is disclosed before it is billed.

Which are the primary technologies used for building your product?

Qdrant's answer

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

How would you describe the primary audience of your product?

TmpState.dev's answer:

Developers and the AI agents working on their behalf. Primarily:

  • Builders using AI coding agents (Claude Code, Cursor, and similar) who want their agent to provision its own backend.
  • Indie hackers and solo builders prototyping quickly across several projects.
  • Hackathon participants who need a backend in the next ten minutes and will not sign up for anything.
  • Anyone who needs disposable, short-lived JSON storage without the ceremony of a full database.

What's the story behind your product?

TmpState.dev's answer:

TmpState came out of a recurring frustration in agent workflows: AI agents constantly need somewhere to keep state, but you cannot hand them your real cloud credentials, and wiring up a database mid-task kills the flow. So the model was inverted - build a database where the URL itself is the only credential, so an agent (or a person with one curl) can create its own backend instantly, with nothing to sign up for and nothing to leak. It is a solo, founder-built, agent-first product, launched in July 2026.

User comments

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Social recommendations and mentions

Based on our record, Qdrant seems to be more popular. It has been mentiond 63 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Qdrant mentions (63)

  • How to give Claude Code persistent memory with a self-hosted mem0 MCP server
    The stack runs on Qdrant for vector storage, Ollama for local embeddings, and optional Neo4j for a knowledge graph that I added later. I also set it up to route different operations to the best LLM for each task. It provides eleven tools for your Claude Code instance to manage long-term memory operations, and your memories data never leaves your machine. - Source: dev.to / 5 months ago
  • The Database Zoo: Vector Databases and High-Dimensional Search
    Qdrant: Open-source vector database optimized for hybrid search and easy integration with ML workflows. - Source: dev.to / 8 months ago
  • Java's Agentic Framework Boom is a Code Smell
    Yes, Java SDKs are critical. But you don't need to rebuild entire orchestration engines just to write agents in Java. The ecosystem already has platforms solving the hard problems: memory (Zep, Mem0, LangMem), tools (specialized platforms), vectors (Pinecone, Weaviate, Qdrant), observability (LangSmith, Helicone, Langfuse). Integrate, don't rebuild. - Source: dev.to / 8 months ago
  • What is the Most Effective AI Tool for App Development Today?
    James Allsopp adds, "LangChain or LlamaIndex for managing LLM workflows, especially if you're adding vector search or documents." These tools handle multi-step processes, essential for complex apps. - Source: dev.to / 11 months ago
  • ๐Ÿ”ฅ Build a RAG Chatbot That Talks to Your Documents Using Python (Gemma + Qdrant + Docling)
    ๐Ÿ“ฆ Qdrant for fast vector search and retrieval. - Source: dev.to / 11 months ago
View more

TmpState.dev mentions (0)

We have not tracked any mentions of TmpState.dev yet. Tracking of TmpState.dev recommendations started around Jul 2026.

What are some alternatives?

When comparing Qdrant and TmpState.dev, you can also consider the following products

Weaviate - Welcome to Weaviate

Supabase - An open source Firebase alternative

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Upstash - Upstash provides Serverless Redis and Kafka as a service.

Vespa.ai - Store, search, rank and organize big data

Firebase - Firebase is a cloud service designed to power real-time, collaborative applications for mobile and web.