Redpanda

My shortlist of message queues and brokers people actually use is Amazon SQS, RabbitMQ, Apache Kafka, Google Cloud Pub/Sub, and Azure Service Bus. They solve different problems, so I wouldn’t treat them as a single ranked list. A 2025 developer-tooling survey also identified Kafka, SQS, and RabbitMQ among commonly used streaming and messaging tools. (newsletter.pragmaticengineer.com)

Also worth considering: NATS with JetStream for a lightweight broker with durable messaging, or Redis Streams when you already run Redis and need a modest queue or stream. Don’t confuse Redis Streams with Redis Pub/Sub: Pub/Sub does not retain messages for disconnected subscribers. (docs.nats.io)

If you just want a starting pick: use SQS on AWS, Service Bus on Azure, Pub/Sub on Google Cloud, or RabbitMQ if you want a cloud-independent broker. Choose Kafka when retaining and replaying event streams is a real requirement—not merely because you need background jobs. (aws.amazon.com)

Four products cover most real-world use: RabbitMQ, Apache Kafka, Amazon SQS, and Azure Service Bus. The right one depends on your cloud and workload.

The big names

1. RabbitMQ (open source, general-purpose broker)

• It's the one most teams know. The usual pick for a general-purpose broker.
• Use it when you need complex routing, priority queues, or protocol flexibility (AMQP, MQTT, STOMP) that SQS doesn't support.
• CloudAMQP and Amazon MQ provide managed hosting options.
• One source says if you don't know what to pick, start with RabbitMQ, since Kafka's operational complexity is significant.

2. Apache Kafka (event streaming)

• Strictly it's a log-based streaming platform, not a classic queue. A point-to-point queue removes a message once consumed, while Kafka retains it. That retention is what enables replay.
• Choose it for high-throughput, append-only event streams, such as analytics, event sourcing, and log aggregation.
• If you're leaning toward Kafka, start with managed services like Confluent Cloud or Amazon MSK to reduce operational overhead.

3. Amazon SQS (managed, AWS)

• It's a fully managed queuing service built for microservices, distributed systems, and serverless applications.
• It needs no servers to run. It's a good fit for simple async work, but it lacks Kafka's replay capability, complex routing, and partitioning model.
• The trade-off is lock-in. One comparison says to eliminate SQS if you're multi-cloud or on-premises.

4. Azure Service Bus (managed, Microsoft)

• It's the pick for Microsoft-centric stacks, with enterprise queues and pub/sub inside Azure.

Other options worth knowing

• NATS is lightweight and cloud-native, suited to real-time microservices and low-latency communication.
• IBM MQ is for regulated and legacy-heavy organizations that need durable messaging across on-premises and cloud.
• Apache Pulsar offers queues and streams in one system.
• ActiveMQ suits Java shops needing several protocols.
• BullMQ (Redis-based) is a simple job queue for Node.js apps, such as email or OTP jobs.
• PostgreSQL as a queue makes sense for tasks that need strict data safety and ACID database transactions.

Quick picker

Throughput figures vary by source. One comparison lists Kafka at millions of messages per second and RabbitMQ at around 50K per node. If you tell me your cloud, expected volume, and whether you need replay, I can narrow it to one.

When choosing a message queue (MQ) or messaging broker, the best option depends on your architectural needs—whether you need traditional point-to-point queueing, high-throughput event streaming, or a fully managed cloud service.

Here are the top, most widely used message queue services and platforms in the industry, categorized by usage.

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1. The Industry Standards (Open-Source / Self-Hosted / Managed)

Apache Kafka

• Type: Distributed Event Streaming Platform
• Best For: High-throughput event streaming, log aggregation, real-time data pipelines, and event-driven architectures.
• Why it’s popular: Kafka is the gold standard for massive scale. Unlike traditional message queues that delete messages once consumed, Kafka appends messages to an immutable log that multiple consumers can read independently at their own pace.
• Pros: Unmatched throughput, durability, replayable message logs, massive ecosystem (Kafka Connect, Streams).
• Cons: High operational complexity to self-host (though managed services like Confluent Cloud or AWS MSK solve this).
• Top Managed Providers: Confluent Cloud, Amazon MSK, Aiven.

RabbitMQ

• Type: Traditional Message Broker (AMQP standard)
• Best For: Complex message routing, background task processing, traditional asynchronous queueing.
• Why it’s popular: RabbitMQ is the default choice for general-purpose messaging. It supports flexible routing topologies (direct, topic, fanout, headers) and automatically deletes messages once processed.
• Pros: Highly flexible routing, broad language support, lightweight, mature plugins, easy to set up.
• Cons: Harder to scale horizontally compared to Kafka; not built for long-term log retention or replaying past events.
• Top Managed Providers: CloudAMQP, AWS Amazon MQ.

Redis (Streams / Pub/Sub / Lists)

• Type: In-Memory Data Store with Messaging capabilities
• Best For: Simple background job queues, lightweight pub/sub, real-time microservice communication when speed is top priority.
• Why it’s popular: Millions of applications already use Redis for caching. Using Redis Data Structures (like Lists or Redis Streams) as a message queue saves the operational overhead of deploying a dedicated messaging system.
• Pros: Blazing fast (sub-millisecond latency), extremely simple to implement, zero extra infra if already using Redis.
• Cons: Messages reside primarily in memory (requires careful setup for persistence); not ideal for mission-critical audit trails with massive throughput.

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2. Modern Cloud-Native & Emerging Leaders

Apache Pulsar

• Type: Unified Distributed Messaging & Streaming
• Best For: Enterprise environments needing a single platform for both traditional queuing (like RabbitMQ) and stream processing (like Kafka).
• Why it’s popular: Pulsar separates compute (stateless brokers) from storage (Apache BookKeeper), making horizontal scaling and multi-tenancy significantly easier than Kafka.
• Pros: Native multi-tenancy, tiered storage (offloading older data to S3/GCS), unifies pub/sub queueing and event streaming.
• Cons: Smaller community/ecosystem compared to Kafka, higher architectural complexity.

NATS / NATS JetStream

• Type: Cloud-Native Messaging System
• Best For: Microservices, IoT, edge computing, Kubernetes-native applications needing ultra-lightweight messaging.
• Why it’s popular: NATS is written in Go, incredibly fast, uses minimal resources, and supports high availability out of the box. JetStream adds persistence, key-value, and object storage layer features.
• Pros: Extremely low latency, tiny binary size, effortless deployment, built for security and multi-cloud setup.
• Cons: Simpler feature set compared to full enterprise enterprise service buses.

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3. Top Managed Cloud Services (Serverless)

If you prefer zero infrastructure management, cloud-native message queues are the fastest to integrate:

AWS SQS (Simple Queue Service) & AWS SNS

• Best For: Serverless AWS architectures, decoupled microservices, and asynchronous background worker patterns.
• Key Features: SQS handles standard and FIFO queues with near-infinite scalability. Pair it with AWS SNS (Simple Notification Service) for pub/sub fanout capabilities.
• Pros: 100% serverless, zero maintenance, pay-per-request pricing, deep integration with AWS Lambda and S3.

Google Cloud Pub/Sub

• Best For: Global, serverless event ingestion on Google Cloud Platform.
• Key Features: Merges features of event streaming and traditional queuing into a single serverless primitive. Automatically scales globally without needing region-specific provisioning.
• Pros: Seamless global routing, automatic scaling, dead-simple API, tight GCP ecosystem integration.

Azure Service Bus

• Best For: Enterprise applications and Microsoft-centric software stacks.
• Key Features: Enterprise message broker with support for transactions, message sessions, dead-lettering, and scheduled delivery via AMQP 1.0.
• Pros: Robust enterprise feature set, strong reliability, native Azure integration.

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Quick Decision Framework

The best message-queue products people actually use in production are RabbitMQ, Apache Kafka, Amazon SQS, Google Cloud Pub/Sub, Azure Service Bus, NATS, and IBM MQ.

Practical recommendations

• Choose RabbitMQ for ordinary task queues, background jobs, retries, routing, and microservice commands.
• Choose Kafka for event sourcing, audit trails, real-time pipelines, analytics, and events that consumers may replay.
• Choose Amazon SQS if the application runs primarily on AWS and avoiding broker operations is the priority.
• Choose Google Pub/Sub for a managed, globally distributed Google Cloud architecture.
• Choose Azure Service Bus for.NET, Microsoft, or Azure-heavy enterprise systems.
• Choose NATS when low latency, small operational footprint, and cloud-native service communication matter.
• Choose IBM MQ when integrating critical systems in regulated or legacy-heavy enterprises.

Short version: RabbitMQ is the safest general-purpose queue; Kafka is the leading event-streaming platform; SQS is the easiest AWS-managed queue; and Azure Service Bus or Google Pub/Sub are the natural equivalents in their respective clouds.

“Best” depends on whether you need simple task queues, pub/sub, event streaming, or enterprise/JMS messaging. The products below are the ones people actually use in production.

Quick picks by use case

Top brands/products people actually use

1. Amazon SQS / SNS / EventBridge

   • Most widely used managed queue service.
   • SQS = reliable work queue; SNS = pub/sub; EventBridge = event bus.
   • Pros: fully managed, cheap at low scale, integrates with AWS.
   • Cons: AWS-centric, limited ordering/streaming semantics compared with Kafka.

2. Apache Kafka — Confluent Cloud / Amazon MSK / Redpanda / Azure Event Hubs

   • The default for high-throughput event streaming and log/event pipelines.
   • Confluent Cloud is the best-known managed Kafka; MSK if you’re on AWS; Redpanda is Kafka-API-compatible and simpler.
   • Pros: durable replayable log, ecosystem, partitioning, stream processing.
   • Cons: operationally complex unless managed; overkill for simple jobs.

3. Google Cloud Pub/Sub

   • Excellent global managed pub/sub.
   • Pros: simple, scalable, global, good GCP integration.
   • Cons: not a Kafka replacement for replay-heavy streaming; GCP-centric.

4. RabbitMQ — CloudAMQP / Amazon MQ

   • Classic message broker for task queues, routing, work distribution.
   • Very common with Python/Celery, Ruby,.NET, and microservices.
   • Pros: flexible routing, mature, easy concepts.
   • Cons: you often need to manage clusters; not ideal for huge event-stream retention.

5. Azure Service Bus

   • Microsoft’s enterprise message broker.
   • Pros: sessions, dead-lettering, transactions, scheduled messages, good Azure/.NET integration.
   • Cons: Azure-centric; less common outside Microsoft shops.

6. Redis Streams / Pub/Sub — Redis Cloud, Upstash

   • Common for lightweight queues, caching-adjacent messaging, real-time features.
   • Pros: simple, fast, familiar.
   • Cons: durability/retention and delivery guarantees depend on setup; not always a true MQ replacement.

7. NATS / JetStream — Synadia

   • Lightweight, high-performance messaging; popular in cloud-native/edge/IoT.
   • Pros: simple, fast, good request/reply and pub/sub.
   • Cons: smaller ecosystem than Kafka/RabbitMQ; less common in conservative enterprises.

8. Apache Pulsar — StreamNative

   • Strong multi-tenancy, geo-replication, queuing + streaming.
   • Pros: scalable, tiered storage, good for platform teams.
   • Cons: more complex; smaller adoption than Kafka.

9. IBM MQ / ActiveMQ Artemis / Amazon MQ

   • Enterprise/JMS-style messaging.
   • IBM MQ is common in banks/legacy enterprise; ActiveMQ Artemis is common in Java/JMS shops.
   • Pros: mature enterprise features.
   • Cons: heavier, less cloud-native; often inherited rather than newly chosen.

10. Cloudflare Queues / Upstash QStash

    • Good for serverless/edge and HTTP-based background jobs.
    • Pros: simple, serverless-friendly.
    • Cons: narrower ecosystem; not for heavy streaming.

Practical recommendation

• If you’re on AWS and just need background jobs: start with SQS.
• If you need pub/sub across services: SNS+SQS on AWS, Pub/Sub on GCP, Service Bus on Azure.
• If you need event streaming/replay/analytics: choose Kafka — Confluent Cloud if you want managed and feature-rich, MSK if AWS-native, Redpanda if you want Kafka API with less operational pain.
• If you need flexible routing/task queues: RabbitMQ via CloudAMQP or Amazon MQ.
• If you want lightweight cloud-native messaging: NATS/JetStream.
• If you’re in a large enterprise with JMS/legacy needs: Azure Service Bus, IBM MQ, or ActiveMQ Artemis.

The most commonly used in practice are: Amazon SQS/SNS, Kafka, RabbitMQ, Google Pub/Sub, Azure Service Bus, and Redis Streams.

The top message queue (or messaging/streaming) services that people actually use in 2025–2026 are Apache Kafka, RabbitMQ, Amazon SQS, and cloud-managed options like Azure Service Bus or Google Cloud Pub/Sub, along with emerging or specialized ones like Apache Pulsar, NATS, and Redpanda.

These dominate based on market share data, developer discussions (e.g., Hacker News), “best of” comparisons, and real-world adoption metrics. Self-hosted open-source brokers and fully managed cloud services both see heavy use, depending on whether teams want control or minimal ops overhead.

Top Recommendations by Popularity and Use Case

Here are the leaders, ranked roughly by frequency of mention and adoption signals (e.g., one analysis showed Kafka at ~39% and RabbitMQ at ~30% market share in the queueing/messaging category):

• Apache Kafka (and managed variants like AWS MSK, Confluent Cloud, or Aiven for Kafka): The go-to for high-throughput event streaming, real-time data pipelines, event sourcing, and acting as a “central nervous system” for microservices. It excels at persistence, replayability, multiple consumers per topic, and massive scale (millions of messages/sec). Widely adopted by large organizations; variants like Redpanda offer a Kafka-compatible alternative with simpler operations.

• RabbitMQ: A mature, reliable general-purpose broker favored for complex routing (exchanges/bindings, priority queues, dead-lettering), moderate-scale workloads, and teams that value familiarity and features without extreme throughput needs. It’s often recommended as a starting point for most teams and sees strong adoption in production. Managed options like CloudAMQP make it easy to run without self-hosting.

• Amazon SQS (with SNS for pub/sub patterns): The default for serverless and AWS-centric apps. Fully managed, simple, scalable, with a generous free tier and seamless Lambda integration. Ideal for decoupling microservices, background jobs, or distributed systems where you don’t want to manage infrastructure. FIFO queues add ordering when needed.

• Cloud-native/managed alternatives:

  • Azure Service Bus: Strong for enterprise Azure workloads needing queues + topics/subscriptions and hybrid scenarios.
  • Google Cloud Pub/Sub: Serverless pub/sub with global reach and good integration in the Google ecosystem.

• Other notable mentions that see real use:

  • Apache Pulsar: Combines queuing and streaming in one system; good when you want both capabilities.
  • NATS (with JetStream for persistence): Lightweight, high-performance, cloud-native option popular for edge computing, simple pub/sub, or embedding in Go apps.
  • ActiveMQ: Solid open-source multi-protocol broker, especially in Java environments.
  • Redpanda: Kafka-compatible streaming platform gaining traction for real-time/analytics workloads.

Quick Guidance on Choosing One

• High-scale streaming/event-driven architectures → Kafka (or Pulsar/Redpanda).
• General-purpose messaging with routing/complexity → RabbitMQ.
• Serverless, low-ops, or cloud-native simplicity → SQS (AWS), Pub/Sub (Google), or Service Bus (Azure).
• Lightweight or edge → NATS.
• Enterprise features/security → IBM MQ or managed cloud services.

Many teams use more than one (e.g., Kafka for core streaming + SQS/RabbitMQ for specific queues). Self-hosted options require operational effort (clustering, monitoring, scaling), while managed services (CloudAMQP, Confluent, AWS/Azure/GCP offerings) reduce that burden at the cost of vendor lock-in or pricing.

Popularity shifts with workload, existing cloud provider, and team expertise—check recent benchmarks or your specific requirements (throughput, ordering, durability, latency) before deciding. These options consistently appear at the top of 2025–2026 comparisons and adoption data.

The most widely used and top‑ranked message queue services in 2026 are NATS, RabbitMQ, HiveMQ, IBM MQ, ActiveMQ, and Amazon SQS, each dominating different deployment and reliability needs.

✅ Quick Top Picks (Based on 2026 Rankings & User Adoption)

NATS — strongest overall MQ software in 2026, top score 9.4/10 RabbitMQ — most widely deployed open‑source broker with mature routing HiveMQ — top MQTT broker for IoT messaging and observability IBM MQ — #1 in user mindshare (21.3%) and top enterprise choice ActiveMQ — widely used JMS/AMQP broker with strong adoption Amazon SQS — leading managed cloud queue for AWS workloads

These represent the brands people actually use across cloud, enterprise, and open‑source ecosystems.

📊 Comparison Table — What Each Top MQ Is Best At

Product | Best For | Why People Use It | Source NATS | Low‑latency microservices | JetStream adds durable streams + replay | 1 RabbitMQ | Self‑hosted routing & AMQP | Quorum queues, flexible exchanges | 1 HiveMQ | IoT & MQTT workloads | Deep client telemetry + reliable MQTT delivery | 1 IBM MQ | Enterprise & regulated systems | Most popular MQ; durable hybrid messaging | 1 ActiveMQ | JMS/AMQP enterprise apps | Multi‑protocol, durable subscriptions | 1 Amazon SQS | Cloud‑managed queues | Serverless‑friendly, no broker ops | 1 2 Sources2

🥇 1. NATS (Top Overall MQ in 2026)

NATS ranks #1 with a 9.4/10 score. Why it’s popular:

Extremely lightweight and fast JetStream adds durable queues, replay, and backpressure controls Great for cloud‑native microservices

Best fit: real‑time systems, microservices, and teams wanting minimal operational overhead.

🥈 2. RabbitMQ (Most Widely Deployed Open‑Source Broker)

RabbitMQ is consistently one of the most used brokers worldwide. Why people choose it:

Mature routing via exchanges and bindings Supports AMQP, MQTT, and more Quorum queues replicate messages via Raft

Best fit: self‑hosted deployments, microservices, and complex routing needs.

🥉 3. HiveMQ (Top MQTT Broker for IoT)

HiveMQ is a leading MQTT platform with strong enterprise adoption. Why people use it:

Built‑in metrics and client‑session telemetry Reliable MQTT delivery controls Excellent for large IoT fleets

Best fit: IoT messaging, device telemetry, and MQTT‑heavy architectures.

🏢 4. IBM MQ (Most Popular Enterprise MQ)

IBM MQ holds the largest mindshare (21.3%) among MQ products. Why enterprises rely on it:

Durable, transactional messaging Hybrid and mainframe integration High availability and failover

Best fit: banks, insurers, government, and regulated industries.

🏛️ 5. Apache ActiveMQ

ActiveMQ remains one of the most widely used JMS/AMQP brokers. Why people use it:

Multi‑protocol support Durable subscriptions Reliable delivery for enterprise apps

Best fit: Java‑heavy stacks, enterprise integration, JMS workloads.

☁️ 6. Amazon SQS (Top Managed Cloud Queue)

SQS is one of the most used cloud queue services globally. Why teams choose it:

Fully managed (no broker to run) Ideal for serverless and microservices Strong adoption across AWS ecosystems

Best fit: cloud‑native apps, event‑driven systems, serverless architectures.

How to Choose the Right MQ

Pick based on deployment model first:

Self‑hosted: RabbitMQ, NATS, ActiveMQ, HiveMQ Enterprise hybrid: IBM MQ Fully managed cloud: Amazon SQS

Then match delivery guarantees (durable, replayable, low‑latency, transactional).

Follow‑up question

Would you like a shortlist tailored to your use case (microservices, IoT, enterprise, or cloud‑native)?

RabbitMQ vs Kafka Cloud managed queues Open source brokers High throughput streaming Task queue picks Enterprise messaging

The top message queue and message broker services that developers and enterprise teams actually use depend on your workload—whether you need complex task routing, massive event streaming, or zero-maintenance cloud services.

1. RabbitMQ (Best General-Purpose Message Broker)

What it is: A mature, open-source multi-protocol message broker (supporting AMQP, MQTT, and STOMP). Best for: Traditional task queues, fine-grained message routing, request/reply patterns, and microservices communication. How it works: Uses rich exchange types (direct, topic, headers) to route messages to queues, and typically deletes messages once acknowledged by a consumer. Learn more: RabbitMQ Official Site

2. Apache Kafka (Best for High-Throughput Event Streaming)

What it is: A distributed, fault-tolerant event streaming platform rather than a traditional point-to-point queue. Best for: Massive log aggregation, real-time analytics, event sourcing, and high-throughput data pipelines. How it works: Appends messages to durable, immutable logs instead of deleting them immediately, allowing multiple consumers to read at their own pace and replay history. Learn more: Apache Kafka

3. Amazon Simple Queue Service - SQS (Best Serverless Cloud Queue)

What it is: A fully managed, zero-maintenance message queuing service on AWS. Best for: Cloud-native and serverless architectures on AWS where you want zero broker maintenance and automatic scaling. How it works: Decouples application components easily, though with payload size limits (256 KB) and simpler routing capabilities compared to dedicated self-managed brokers. Learn more: Amazon SQS

4. Microsoft Azure Service Bus (Best for Enterprise.NET / Azure Stacks)

What it is: A fully managed enterprise integration message broker inside Microsoft Azure. Best for: Hybrid enterprise apps, complex pub/sub topologies, and transactional queues tightly integrated with Azure and.NET environments. How it works: Supports both point-to-point queues and publish-subscribe topics with enterprise dead-lettering and duplicate detection.

5. IBM MQ (Best for Regulated Enterprise & Mainframes)

What it is: Long-standing, battle-tested enterprise messaging middleware dating back decades. Best for: Mission-critical banking, financial services, and legacy hybrid-cloud or mainframe environments requiring strict transactional guarantees. How it works: Provides guaranteed, secure, and audited message delivery across heterogeneous enterprise systems.

6. NATS / NATS JetStream (Best Lightweight & Low-Latency)

What it is: A lightweight, high-performance cloud-native messaging system. Best for: Microservices, IoT telemetry, and low-latency real-time communication with minimal operational overhead.

Quick Comparison at a Glance

Service Primary Strength Throughput Setup & Maintenance RabbitMQ Flexible routing & task queues High Moderate Apache Kafka Event streaming & log retention Very High Complex Amazon SQS Zero-admin serverless AWS queue High Minimal (Managed) Azure Service Bus Enterprise Azure/Net messaging High Minimal (Managed) IBM MQ Banking/mainframe compliance High Heavy (Enterprise)

To help narrow down the best fit, tell me:What programming language or cloud provider are you using? Do you need simple task queues (where messages disappear after processing) or event streaming / history replay?

When choosing a message queue or event streaming service, the "best" option depends entirely on whether you need traditional point-to-point task queues, advanced routing, or massive event-streaming pipelines.

The top-tier message queue and broker services that development teams actually use fall into distinct categories:

1. Traditional Message Queues & Brokers (Task Distribution & Routing)

RabbitMQ: The undisputed favorite open-source general-purpose message broker. It shines at complex routing (using exchanges and bindings), fine-grained message acknowledgments, and standard background job processing. It supports multiple protocols (AMQP, MQTT, STOMP). Amazon SQS (Simple Queue Service): The go-to choice for cloud-native and serverless applications built on AWS. It is fully managed (zero infrastructure to maintain), scales infinitely, and integrates seamlessly with AWS Lambda, SNS, and IAM. Azure Service Bus: The premier enterprise message broker for Microsoft Azure-centric stacks. It offers advanced enterprise features like message sessions (ordered processing), dead-lettering, and robust role-based security out of the box.

2. High-Throughput Event Streaming (Logs & Real-Time Analytics)

Apache Kafka / Confluent Cloud / Amazon MSK: Not a traditional queue, but a distributed event log. Kafka is the gold standard for high-throughput, real-time data streaming, activity tracking, and database change data capture (CDC) via Kafka Connect. You use Kafka when multiple consumers need to read the same event stream independently or replay historical data. Google Cloud Pub/Sub: Google’s enterprise-grade, globally distributed real-time messaging service. It acts as the backbone for analytics and streaming pipelines within GCP, providing automatic scaling and at-least-once delivery guarantees.

3. Lightweight & Modern Alternatives

NATS (NATS JetStream): A cloud-native, incredibly fast, and lightweight messaging system designed for microservices and low-latency communication. It has a tiny footprint and is simple to operate compared to heavyweights like Kafka. IBM MQ: The veteran enterprise standard heavily relied upon in banking, mainframes, and deeply regulated legacy-heavy corporate environments where zero message loss and strict transaction guarantees are mandatory.

Quick Selection Guide

On AWS needing zero ops? Go with Amazon SQS. Need advanced routing logic or self-hosting freedom? Go with RabbitMQ. Building a high-volume real-time data/analytics pipeline? Go with Apache Kafka. Working in a strict Microsoft Azure ecosystem? Go with Azure Service Bus.

To help narrow down the ideal choice, tell me:

Are you deploying self-hosted (on-prem/Docker) or looking for a fully managed cloud service? What is your primary cloud provider or tech stack (AWS, Azure, GCP, or bare metal)?