Top 10 IoT platforms you should use in 2025

Top 10 IoT platforms you should use in 2025: the essentials, the trade‑offs, and how to choose

The best IoT platforms do more than connect devices—they give you a secure, scalable IoT architecture, production-grade device management, and analytics-driven workflows that feed business outcomes. In 2025, IoT buyers face a crowded market of cloud hyperscalers, industrial IIoT suites, connectivity-first offerings, and end‑to‑end vendors that bundle hardware, connectivity, and software. This guide narrows the field to ten widely adopted options and offers an IoT platform comparison grounded in real deployment needs.

Why this matters now:

• Scale and reliability: Your device fleet can grow 10–100x faster than your software can—unless the platform’s ingestion, identity, and observability layers are ready on day one.
• Security and governance: From hardware root of trust to zero‑trust APIs and fleet compliance, IoT security is a continuous program, not a checkbox.
• Integration and analytics: The value is in the data. Strong IoT backend services integrate with streams, data lakes/warehouses, digital twins, and ML to convert telemetry into action.
• Total cost and time‑to‑value: Managed services, low‑code tools, and prebuilt apps reduce risk and accelerate deployment.

What you’ll find below:

• A practical overview of the ten best IoT platforms to consider for 2025
• Where each platform shines, trade‑offs to consider, and typical use cases
• A concise "aws iot core vs azure iot" comparison you can use in executive conversations
• A checklist for designing a scalable IoT architecture that won’t paint you into a corner

Evaluation criteria you should apply to any IoT platform:

• Scalability: Elastic ingestion (MQTT/HTTP), multi‑region, multi‑tenant, and event processing that keeps up at peak.
• Security: Mutual TLS, fine‑grained authZ, HSM-backed keys, fleet posture management, SBOM/OTA signing, and compliance (ISO, SOC, GDPR, HIPAA, etc.).
• Integration: Native connectors to streams, data lakes, digital twins, ERP/CRM/SCM, and modern event-driven tools.
• Edge capabilities: Local processing, offline resilience, and policy-based synchronization.
• Device lifecycle: Provisioning at scale, certificate rotation, OTA updates, digital twins/shadows, and bulk operations.
• Analytics/AI: Time-series storage, rules engines, ML integration, and business-friendly dashboards.
• Cost and support: Transparent pricing, support SLAs, and a healthy ecosystem of partners.

Below, we examine AWS IoT Core, Microsoft Azure IoT Hub, Google Cloud for IoT workloads, IBM Watson IoT, Oracle IoT Cloud, Particle, ThingWorx IIoT, Cisco IoT Cloud Connect, Salesforce IoT Cloud, and Cumulocity IoT. Use the closing IoT platform comparison section to map these options to your business goals.

AWS IoT Core: serverless scale and deep cloud integration

AWS IoT Core is a fully managed, serverless service that lets you securely connect millions of devices using MQTT, WebSockets, or HTTP with mutual TLS. It’s often the baseline in an IoT platform comparison thanks to mature device identity, the Rules Engine for routing data, and tight integration with AWS analytics, storage, and AI.

Standout capabilities:

• Device Shadow and Fleet Indexing: Keep digital twins of device state; search fleets by attributes and status.
• IoT Rules Engine: Transform and route messages to 20+ AWS services (S3, Kinesis, Lambda, Timestream, DynamoDB, OpenSearch, and more) with SQL-like filters.
• Provisioning at scale: Just‑in‑time provisioning (JITP/JITR) and Fleet Provisioning for secure onboarding.
• Security and fleet posture: AWS IoT Device Defender for audits, metrics, and anomaly detection; fine‑grained IAM policies.
• Edge: AWS IoT Greengrass for local processing and connectors; SiteWise for industrial telemetry; TwinMaker for rich digital twins.

Best for:

• Teams that want cloud-native IoT backend services with minimal ops.
• Scenarios spanning consumer, industrial, and automotive where analytics/ML (SageMaker), data lakes (S3/Lake Formation), or time-series (Timestream) are critical.

Considerations:

• Pricing complexity: Messaging, rules, device management, and Defender each add cost—model end‑to‑end.
• Multi‑cloud: AWS-first design; multi‑cloud adds architectural overhead.

Quick architecture pattern:

• Ingest via MQTT to IoT Core -> route through Rules Engine to Kinesis/Data Streams for real-time, S3 for cold storage, Timestream for time-series -> analytics with Athena/OpenSearch/SageMaker -> manage fleet with Device Management and Defender.

Bottom line: For many, AWS IoT Core sets the pace among the best IoT platforms due to serverless scalability, abundant integrations, and a broad ecosystem.

Microsoft Azure IoT Hub: enterprise-grade control and digital twins

Azure IoT Hub provides secure bi‑directional communication between devices and the cloud with per‑device identities, D2C/C2D messaging, and device twins. It pairs well with Azure Digital Twins, Azure IoT Edge, and Azure’s data/AI stack for end‑to‑end solutions inside the Microsoft ecosystem.

Standout capabilities:

• Device twins and desired vs. reported properties for robust state management.
• DPS (Device Provisioning Service) for at‑scale, zero‑touch onboarding.
• IoT Edge for offline processing, modules, and GPU/AI workloads at the edge.
• Deep integrations: Event Hubs/Stream Analytics/Synapse, Azure Data Explorer (ADX) for time‑series, Power BI for dashboards, and Azure Machine Learning for AI.

Best for:

• Enterprises invested in Microsoft 365, Dynamics 365, and Azure-native security/governance.
• Industrial digital twin scenarios linking operations to business processes.

Considerations:

• Cost and SKUs: Model message quotas, twin operations, and routing carefully.
• Service sprawl: Powerful but expansive; standardize patterns early.

AWS IoT Core vs Azure IoT (quick take):

• Identity and twins: Parity on fundamentals; Azure’s twin model and Digital Twins service are exceptionally mature.
• Edge: Azure IoT Edge is a strength; AWS Greengrass is also strong—choice often depends on preferred toolchains.
• Analytics: AWS + Timestream/OpenSearch vs. Azure + ADX/Stream Analytics; both are excellent—pick based on your team’s familiarity.

Bottom line: Azure IoT Hub is a top contender in any IoT platform comparison—particularly compelling when you want to align device data with Microsoft’s data/AI and business app stack.

Google Cloud for IoT: real-time analytics with a composable stack

While Google’s managed IoT Core device service was retired, Google Cloud remains a strong platform for IoT workloads thanks to a composable architecture centered on Pub/Sub, Dataflow, BigQuery, and Vertex AI. For many data-intensive use cases, this stack provides exceptional ingestion throughput and low-latency analytics.

Composable reference pattern:

• Ingest: MQTT/HTTP via proxies or lightweight brokers (e.g., Cloud Run/Functions bridging MQTT to Pub/Sub) or partner gateways.
• Stream processing: Dataflow (Apache Beam) for enrichment, windowing, and anomaly detection.
• Storage/analytics: BigQuery for near‑real‑time SQL analytics and federated queries (including BigLake), Cloud Storage for raw dumps, and Looker for BI.
• ML/AI: Vertex AI for model training, feature stores, and online predictions.
• Device management: Use open-source brokers (EMQX, Mosquitto) on GKE/Compute Engine or partner solutions; store digital twins in Firestore/Cloud SQL.

Best for:

• Organizations prioritizing large‑scale analytics, streaming, and ML with robust cost controls.
• Teams comfortable assembling modular IoT backend services in a cloud‑native way.

Considerations:

• DIY device layer: You’ll stitch together device identity, provisioning, and OTA with open‑source or partner services.
• Operations: Powerful but requires platform engineering discipline to standardize patterns.

Why it makes the list of best IoT platforms:

• If your competitive edge is analytics speed and ML accuracy, few clouds rival BigQuery + Dataflow throughput and developer velocity.

Bottom line: Google Cloud’s composable approach enables a scalable IoT architecture for analytics-heavy workloads—ideal when you want to roll your own device tier and maximize streaming + ML performance.

IBM Watson IoT: AI-infused operations and industrial reliability

IBM’s IoT capabilities focus on industrial automation, asset performance, and predictive maintenance. Combining IoT telemetry with AI-driven insights, IBM supports complex operational scenarios in manufacturing, automotive, energy, and critical infrastructure.

Standout capabilities:

• Predictive maintenance: Model degradation and failure patterns from sensor and maintenance data.
• Asset optimization: Integrations with enterprise asset management and maintenance workflows.
• Edge and hybrid: Options for local inference, industrial gateways, and hybrid deployments.
• Governance and security: Emphasis on data lineage, auditability, and enterprise controls.

Best for:

• Industrial organizations that need strong asset-centric workflows and rigorous governance.
• Teams aligning IoT data with quality, reliability, and safety programs.

Considerations:

• Vendor fit: Ensure alignment with your existing EAM/CMMS and industrial data systems.
• Time to value: Complex operations may require a structured rollout with domain experts.

Bottom line: IBM’s combination of AI and industrial know‑how makes it a proven option where uptime and regulatory rigor dominate the requirements.

Oracle IoT Cloud: connect telemetry directly to ERP and field service

Oracle IoT Cloud shines when you want IoT data to drive business processes in ERP, SCM, and field service. Prebuilt applications for asset monitoring, production, and fleet operations shorten time‑to‑value and close the loop between events and work orders.

Standout capabilities:

• Real-time event processing and rules: Detect anomalies and trigger business workflows.
• Deep back-office integration: Connect telemetry to Oracle Fusion apps for maintenance, inventory, and service.
• Diagnostics and asset tracking: Device health, location, and usage patterns.
• Hybrid deployment options and security that align with enterprise governance.

Best for:

• Manufacturers, logistics, and service organizations standardized on Oracle’s business suite.
• Teams that want out‑of‑the‑box IoT apps rather than building from scratch.

Considerations:

• Ecosystem alignment: Best value if your core systems are already on Oracle.
• Extensibility: Validate APIs/SDKs for custom analytics and multi‑cloud needs.

Bottom line: If your ROI depends on turning sensor events into ERP actions and field resolutions, Oracle IoT Cloud offers one of the most direct paths among the best IoT platforms.

Particle: hardware, connectivity, and cloud in one developer-friendly stack

Particle delivers an end‑to‑end platform that bundles embedded hardware, secure connectivity (cellular/Wi‑Fi), OTA firmware management, and a managed device cloud. It’s purpose‑built for teams that want to move from prototype to production without assembling a dozen vendors.

Standout capabilities:

• Integrated device modules and gateway options with secure boot and OTA updates.
• Device OS and SDKs that simplify low‑power telemetry, command/control, and fleet management.
• Managed connectivity: Global cellular options, SIM management, and fleet diagnostics.
• Cloud APIs and integrations with popular data lakes, analytics, and third‑party services.

Best for:

• Startups and product teams that value speed, predictable costs, and reduced supply‑chain complexity.
• Applications where a single vendor for device + cloud accelerates launch and simplifies support.

Considerations:

• Platform lock‑in: Evaluate long‑term BOM and data egress patterns.
• Advanced analytics: You’ll likely pair Particle with external analytics/ML services.

Bottom line: Particle is a standout when you want practical IoT backend services paired with production-ready hardware and connectivity—particularly for connected products and distributed assets.

ThingWorx IIoT Platform: industrial modeling, apps, and AR

PTC’s ThingWorx targets industrial IoT with model‑based application development, device connectivity (often via Kepware), and strong tooling for building operator-facing apps. Its Vuforia integration brings augmented reality to maintenance and training scenarios.

Standout capabilities:

• Model-driven development: Define assets, properties, and services to accelerate app development.
• Industrial connectors: Broad PLC/protocol support through Kepware and partner ecosystem.
• Analytics and alerts: Build rules, KPIs, and dashboards for OT stakeholders.
• AR experiences: Author maintenance and training workflows that overlay digital instructions on physical equipment.

Best for:

• Manufacturers and energy/utilities with rich OT systems and the need for operator apps.
• Organizations that want to standardize asset models across plants and lines.

Considerations:

• Integration architecture: Plan how data flows to data lakes and enterprise apps.
• Governance: Establish dev standards early for multi‑site rollouts.

Bottom line: ThingWorx is a proven IIoT platform when you need industrial connectivity, model-centric apps, and AR-enabled workflows under one roof.

Cisco IoT Cloud Connect: secure connectivity for distributed fleets

Cisco focuses on secure, reliable device connectivity at massive scale. With network-centric IoT services, industrial gateways, private networks, and connectivity management, it’s a strong fit for transportation, utilities, and public-sector deployments where uptime and security on the wire are paramount.

Standout capabilities:

• Secure device connectivity and policy-based segmentation across cellular and IP networks.
• Real-time telemetry and QoS for mission-critical applications (e.g., fleet and grid operations).
• Edge compute: Cisco IOx-capable gateways for local processing and resilience.
• Connectivity management: Tools for SIM/eSIM, APN security, and carrier integrations.

Best for:

• Highly distributed fleets and infrastructure with stringent connectivity/security needs.
• Teams that want to anchor IoT in network policy and observability.

Considerations:

• Application layer: Pair with analytics, twin, and business apps to complete the stack.
• Multi‑vendor operations: Align IT/OT security and lifecycle processes.

Bottom line: Cisco’s strength in secure networking makes it a solid choice when connectivity is your primary risk and success factor.

Salesforce IoT Cloud: customer-centric IoT for service and personalization

Salesforce IoT Cloud brings device and event data into the CRM to power proactive service, personalized experiences, and revenue workflows. If your competitive edge is customer engagement and service excellence, tying telemetry to cases, entitlements, and journeys can be compelling.

Standout capabilities:

• Event ingestion and orchestration: Turn device signals into CRM actions using flows and rules.
• 360° customer view: Combine telemetry with account, asset, and service history.
• Field service alignment: Trigger work orders, parts requests, and technician scheduling.
• Ecosystem: Mulesoft for integration, AppExchange for extensions.

Best for:

• Connected product companies focused on service differentiation and upsell/cross‑sell.
• Subscription/aftermarket models where usage data drives outcomes.

Considerations:

• Data strategy: Decide what lives in Salesforce vs. data lake/warehouse.
• Event scale and cost: Model ingestion volumes and orchestration triggers carefully.

Bottom line: A strong fit for customer-facing IoT where telemetry should flow directly into service and lifecycle experiences.

Cumulocity IoT: open, multi-deployment IIoT with strong device management

Cumulocity IoT (SaaS, on‑premises, edge, or hybrid) emphasizes rapid device onboarding, standards-based connectivity, and low‑code analytics. It’s used by manufacturers, telcos, and system integrators who need flexible deployment and robust remote/bulk device management.

Standout capabilities:

• Protocol breadth: MQTT, CoAP, LwM2M, REST; device agent SDKs and edge options.
• Device operations: Bulk registration, remote configuration, firmware/OTA updates, command/control.
• Low-code analytics: Build rules, KPIs, and dashboards without heavy custom code.
• Extensibility: Microservices, APIs, and integration frameworks for enterprise systems.

Best for:

• Organizations seeking a vendor-neutral IIoT platform deployable across cloud and on‑prem.
• Telcos and OEMs that need multi-tenant management and white-label options.

Considerations:

• Advanced data science: Pair with external lakes/warehouses and ML stacks for deeper analytics.
• Governance: Define tenancy, access control, and lifecycle processes for scale.

Bottom line: Cumulocity is a versatile choice when open protocols, deployment flexibility, and strong fleet management are top priorities.

IoT platform comparison: AWS IoT Core vs Azure IoT and a scalable architecture checklist

No single platform wins every scenario. Use this decision guide to align the best IoT platforms with your goals, budget, and constraints.

AWS IoT Core vs Azure IoT (executive quick read):

• Cloud gravity: Choose AWS if your data/ML stack (S3, Lake Formation, SageMaker) is primary; choose Azure if ADX/Synapse/Power BI/Dynamics 365 are central.
• Digital twins: Both support twins; Azure’s Digital Twins service offers rich graph modeling and spatial relationships; AWS’s TwinMaker integrates well with existing AWS data.
• Edge: Azure IoT Edge brings modular, offline-ready containers tightly integrated with IoT Hub. AWS Greengrass is similarly capable; selection often hinges on your CI/CD and container strategy.
• Time-series analytics: AWS Timestream + OpenSearch vs. Azure Data Explorer (ADX). ADX excels at high‑cardinality, ad‑hoc interrogations; Timestream is tightly integrated with AWS services and serverless ops.
• Security/governance: Parity on fundamentals. Let your existing cloud controls, IAM, and compliance posture guide the choice.

When to consider Google Cloud for IoT workloads:

• Your differentiator is real-time analytics at scale and ML productivity. Pub/Sub + Dataflow + BigQuery + Vertex AI is hard to beat for streaming analytics.

When to favor industrial IIoT platforms (ThingWorx, Cumulocity, IBM, Oracle):

• You need device/PLC connectivity out of the box, asset-centric modeling, and prebuilt apps for maintenance, quality, and production. Look at IIoT suites first, then attach a lake/warehouse.

When connectivity is king (Cisco, Particle):

• Cisco for network resilience, segmentation, and industrial gateways at fleet scale.
• Particle for integrated hardware + connectivity + cloud that compresses time‑to‑market.

Scalable IoT architecture checklist:

• Device identity and trust: Per‑device certificates, hardware root of trust where possible, automated rotation.
• Protocols and ingestion: MQTT 3.1.1/5.0 with backpressure strategies; HTTP for bursts; WebSockets for interactive control.
• Message routing: Serverless rules/streams, dead‑letter queues, and schema/versioning governance.
• Storage tiers: Hot path (in‑memory/stream), warm path (time‑series/ADX/Timestream), cold path (S3/Cloud Storage/data lake) with lifecycle policies.
• Digital twins/shadows: Standardize twin schemas; treat desired/reported as contract; apply RBAC and audit trails.
• Edge computing: Local filtering, ML inference, and command buffering for offline resilience; policy-based synchronization.
• Observability and SRE: Fleet metrics, per‑device logs, synthetic devices for canary testing, fleet posture dashboards (cert expiry, firmware drift).
• OTA and firmware lifecycle: Signed updates, staged rollouts, rollback plans, SBOM tracking, CVE scanning.
• Security baseline: Mutual TLS, least-privilege policies, secure boot, encrypted at rest/in transit, anomaly detection.
• Cost governance: Simulate full end‑to‑end cost (messages, rules, storage, queries, egress). Set budgets and alerts; compress payloads and batch where safe.

Practical next steps:

• Start with a thin slice—1 critical use case, 1 region, staged rollout—then scale.
• Pick the platform that minimizes unknowns in your top two risks (e.g., analytics throughput and OTA safety).
• Document your IoT platform comparison assumptions and revisit quarterly as scale and requirements evolve.

Final word: The best IoT platforms are the ones that align to your target outcomes and your team’s strengths. Use the guidance above to choose a platform, design a scalable IoT architecture, and deliver results you can measure—faster.