DataDack
Summary
Most automation platforms collapse the moment you need IoT telemetry, LLM inference, and multi-step agent logic to run inside a single data boundary — you end up stitching three vendors together with glue code that breaks on the first traffic spike. DataDack is built as a single architecture that covers all three.
The platform runs visual workflow orchestration, AI agents with RAG memory, and IoT telemetry ingestion under one roof, deployed on AWS Mumbai and Hyderabad for teams that cannot let data cross Indian borders under DPDP. The vendor states 10ms node latency and a 99.9% uptime target at 10k+ RPS — claims that hold architectural credibility given the Go and Node.js core, but production verification at your specific load profile is still your job. The agent builder and RAG memory features are paid-only. Teams on the free tier get workflow automation and gateway access, but the autonomous swarms stay behind a paywall.
Bottom line: Pick DataDack when your production requirement is India-data-residency plus IoT-plus-LLM in one pipeline; look elsewhere when your workflow branching grows past what a visual canvas can express and you need code-first agent logic without a visual layer in the way.
Pricing Plans
Subscription- Free Tier
- 1,000 workflow runs / month, shared AI Gateway pool, 1-week audit logs (50 MB), community + email support
Free
1,000 workflow runs/month, shared AI Gateway, community support
- Visual no-code builder
- AI Agents community templates
- IoT MQTT/OPC-UA/Modbus
- 1-week audit logs
Starter
10,000 workflow runs/month, custom agents, unlimited IoT
- Visual builder + custom code
- AI Agents custom logic
- AI Gateway credits
- 3-month audit logs
Pro
500,000 workflow runs/month, RAG memory, high-frequency IoT
- Subflows, branching, error handling
- RAG / multi-step chains
- India data residency
- 6-month audit logs
Enterprise
Unlimited runs, private infrastructure, custom SLAs
- Private model endpoints
- Private MQTT broker + dedicated infra
- SOC 2 + DPDP + HIPAA
- Unlimited audit logs
View full pricing on datadack.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- India-first data residency with AWS Mumbai and Hyderabad nodes and zero cross-border data exits, which means DPDP-compliant deployments skip the legal review that kills timelines for India-based fintech and enterprise teams.
- Single architecture covering workflow automation, AI agent chains with RAG memory, and IoT telemetry ingestion, so you are not stitching three separate vendors together with fragile connectors that drift out of sync.
- AI Gateway with mTLS encryption and zero-log mode that routes prompts straight to VRAM, which means prompt data never lands in a third-party database — a hard requirement for applications processing regulated or confidential inputs.
- 100+ native connectors including Kafka, MQTT, InfluxDB, and gRPC alongside the standard SaaS stack, so IoT-to-cloud pipelines connect without a custom middleware layer sitting between the hardware and the agent.
Cons
Sign in to edit- RAG memory, multi-step agent chains, and the full agent builder are paid-only features — teams that start on the free tier to prototype will hit the paywall before they can test the core agent capabilities the platform is marketed around.
- The visual canvas for workflow orchestration reaches a practical ceiling when conditional branching grows complex — pipelines that branch on agent output, rejoin, and branch again require workarounds that the vendor's documentation does not describe. Teams with deeply conditional logic either flatten their design to fit the canvas or add a code layer alongside it, which splits the system in two.
- No self-hosted option is available. For regulated enterprises that require the orchestration engine itself to run inside their own infrastructure — not just data routed through regional proxies — this is a hard stop, and those teams move to open-source alternatives like Temporal or n8n self-hosted instead.
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About
- API Available
- Yes
- Self-Hosted
- No
- Last Updated
- 2026-06-18T05:44:06.292Z
Best For
Who it's for
- Teams building production AI workflows
- IoT device integration and telemetry
- Users needing no-code automation plus agents
- India-based deployments with data residency
What it does well
- Automate workflows with 100+ integrations
- Build custom AI agents with memory and chains
- Connect and ingest data from IoT devices
- Proxy LLM calls via AI Gateway with credits
Integrations
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Frequently Asked Questions
- Is DataDack free?
- DataDack is a paid tool. No permanent free tier is offered.
- Is DataDack open source?
- No — DataDack is a closed-source tool. Source code is not publicly available.
- Does DataDack have an API?
- Yes. DataDack exposes a developer API. See the official documentation at https://datadack.com for details.
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Curated lists that include this category
Consumer automation tools treat LLM calls, API webhooks, and hardware telemetry as three separate problems. DataDack treats them as one. The core workflow is a visual graph editor where you drag AI agents, REST endpoints, database connectors, and MQTT IoT streams onto a canvas and wire them together — the engine executes each step deterministically, logs every result, and the vendor describes execution as cryptographically signed. Supported connectors include OpenAI, Anthropic, Kafka, PostgreSQL, Redis, MongoDB, Stripe, Salesforce, Datadog, InfluxDB, Twilio, Slack, AWS Lambda, and gRPC, among others.
The clearest differentiator is the AI Gateway with regional routing. DataDack proxies all LLM inference through local nodes — Mumbai and Hyderabad for India, EU nodes for GDPR compliance — with mTLS encryption across every boundary and a zero-log mode that routes prompts straight to VRAM without touching the vendor’s database. For teams operating under India’s DPDP framework or building supply-chain and fintech applications where prompts contain sensitive data, this is the architecture decision that removes the compliance conversation entirely.
The platform fits teams that need IoT telemetry feeding directly into agent logic — factory vibration sensors triggering repair tickets, freight disruption detected and rerouted by agent swarms, compliance PDFs reconciled against live Stripe ledgers. The visual canvas works well for linear and moderately branched pipelines. When your workflow logic becomes deeply conditional — branching on what an agent returned three steps back, with parallel agent paths that rejoin — the canvas model reaches a ceiling that the vendor’s documentation does not fully address. Teams hitting that ceiling add custom logic layers, at which point they are effectively maintaining two systems.