{"slug":"tdengine","name":"TDengine","domain":"tdengine.com","verdict":"As of 2026-08-06, ChatGPT, Claude, Gemini collectively rank TDengine #2 of 7 for time-series databases for industrial iot telemetry (one of 2 leaderboards it appears on). Source: https://modelsagree.com/product/tdengine (modelsagree.com, CC BY 4.0).","best_rank":2,"categories":2,"brief":{"category":"best-time-series-databases-for-industrial-iot-telemetry","title":"Best time-series databases for industrial IoT telemetry","rank":2,"of":7,"top":"InfluxDB","day":"2026-08-03","why":[{"t":"Designed explicitly for industrial IoT","m":["ChatGPT","Gemini","Claude"],"q":"Designed explicitly for IIoT/IoT with a one-table-per-device model plus supertables that fit industrial fleets naturally"},{"t":"High ingestion rates and compression","m":["ChatGPT","Gemini","Claude"],"q":"extremely high ingestion rates and compression on edge gateways"},{"t":"Stream processing and edge-cloud synchronization","m":["ChatGPT","Claude"],"q":"stream processing, direct OPC UA/MQTT ingestion, edge-cloud synchronization"}],"gap":[{"t":"Broad Telegraf ecosystem with hundreds of plugins","m":["ChatGPT","Claude"],"q":"unusually broad Telegraf ecosystem, including OPC UA, MQTT, Modbus, and hundreds of other plugins"},{"t":"Full SQL query support","m":["Claude","Gemini"],"q":"full SQL query support for large-scale telemetry aggregation."},{"t":"Cheap long telemetry history","m":["Claude"],"q":"object-storage-backed retention makes long telemetry history cheap."}],"fix":[{"t":"Thinner ecosystem and community","m":["ChatGPT","Claude"],"q":"ecosystem and community outside its core markets are thinner"},{"t":"Rigid schema makes joins cumbersome","m":["Claude","Gemini"],"q":"Rigid schema model tied to per-device supertables makes arbitrary cross-table relational joins and non-telemetry query patterns cumbersome."},{"t":"Best features require enterprise tiers","m":["ChatGPT","Claude"],"q":"best features often live in the enterprise/cloud tier."}]},"entries":[{"slug":"best-time-series-databases-for-industrial-iot-telemetry","title":"Best time-series databases for industrial IoT telemetry","rank":2,"of":7,"score":11,"appearances":3,"modelRanks":{"ChatGPT":1,"Claude":4,"Gemini":2},"reason":"Purpose-built for high-cardinality device telemetry, with strong compression, high ingest, stream processing, direct OPC UA/MQTT ingestion, edge-cloud synchronization, and Raft-based HA; a near-tie with InfluxDB, ranked first assuming multi-site industrial operations matter most","reasons":[{"model":"ChatGPT","reason":"Purpose-built for high-cardinality device telemetry, with strong compression, high ingest, stream processing, direct OPC UA/MQTT ingestion, edge-cloud synchronization, and Raft-based HA; a near-tie with InfluxDB, ranked first assuming multi-site industrial operations matter most"},{"model":"Gemini","reason":"Purpose-built for industrial IoT with a single-table-per-device supertable architecture that delivers extremely high ingestion rates and compression on edge gateways, built-in caching, low resource overhead, and native handling of out-of-order sensor backfilling."},{"model":"Claude","reason":"Designed explicitly for IIoT/IoT with a one-table-per-device model plus supertables that fit industrial fleets naturally, strong compression, built-in caching, stream processing and data subscription, and native edge-to-cloud sync — a genuinely industrial-first design with commercial support."}],"fixes":[{"model":"ChatGPT","fix":"Key industrial connectors and advanced deployment capabilities require Enterprise, and its ecosystem is smaller than InfluxDB’s"},{"model":"Claude","fix":"Its device-centric data model and SQL dialect are opinionated and less familiar; ecosystem and community outside its core markets are thinner, and best features often live in the enterprise/cloud tier."},{"model":"Gemini","fix":"Rigid schema model tied to per-device supertables makes arbitrary cross-table relational joins and non-telemetry query patterns cumbersome."}],"updated":"2026-08-06","api":"https://modelsagree.com/api/v1/best/best-time-series-databases-for-industrial-iot-telemetry.json"},{"slug":"best-time-series-database","title":"Best time-series database","rank":6,"of":6,"score":1,"appearances":1,"modelRanks":{"Grok":5},"reason":"Strong ingestion/compression/query performance in IoT-focused benchmarks, built-in clustering/HA in OSS, efficient for high-volume device/sensor data with SQL-like querying.","reasons":[{"model":"Grok","reason":"Strong ingestion/compression/query performance in IoT-focused benchmarks, built-in clustering/HA in OSS, efficient for high-volume device/sensor data with SQL-like querying."}],"fixes":[{"model":"Grok","fix":"Smaller overall adoption/ecosystem and less flexible for non-IoT general time-series or complex analytical joins compared to top options."}],"updated":"2026-07-15","rank_history":{"days":["2026-06-29","2026-06-30","2026-07-08","2026-07-09","2026-07-10","2026-07-14","2026-07-15"],"ranks":[null,null,null,null,null,6,null]},"api":"https://modelsagree.com/api/v1/best/best-time-series-database.json"}],"page":"https://modelsagree.com/product/tdengine","check":"https://modelsagree.com/check?q=TDengine","updated":"2026-08-10T18:18:45.051Z","attribution":"modelsagree.com, CC BY 4.0"}