Orkes vs Cadence: what are the key differences today and what does migration look like?

Most engineering teams looking at Orkes vs Cadence aren’t asking “which workflow engine is better?” in the abstract. You’re trying to answer two practical questions: how do these platforms differ operationally right now, and what does it actually take to migrate real workloads off Cadence without blowing up SLAs?

Quick Answer: Orkes Conductor and Cadence are both durable workflow engines, but Orkes is a managed, enterprise-grade orchestration platform with built-in UI, governance, observability, and AI/agent features, while Cadence is an open-source workflow engine you operate yourself. Migration typically means mapping Cadence workflows/activities to Orkes workflows/workers, wiring SDKs to Orkes APIs, and incrementally shifting traffic with versioning and parallel runs.

---

Frequently Asked Questions

How is Orkes different from Cadence at a high level?

Short Answer: Cadence is a workflow engine you host and operate; Orkes is a fully managed orchestration platform (built on Conductor) with UI, governance, observability, and agentic features designed to run AI agents, microservices, and human workflows at enterprise scale.

Expanded Explanation:
Cadence gives you a durable workflow runtime with strong guarantees, but leaves infrastructure, scaling, upgrades, monitoring, and governance to your team. That usually works for early-stage adoption, but it becomes painful when many product teams depend on the cluster, on-call load grows, and you need centralized controls over who can deploy what.

Orkes Conductor, by contrast, is delivered as an enterprise-grade platform: hosted or customer-managed, with up to 99.99% availability SLAs, SOC 2 Type II posture, and battle-tested scale (1B+ workflows/day, 1,200+ companies). On top of the engine, it adds a rich UI, JSON/SDK workflow definitions, polyglot workers, real-time monitoring, Git-like versioning, RBAC, audit logs, Human Tasks, and an MCP Gateway for turning internal APIs into safe tools for AI agents.

Key Takeaways:

• Choose Cadence if you want to fully own and operate the workflow stack; choose Orkes if you want a governed, enterprise-grade orchestration layer with SLAs and a UI.
• Orkes focuses heavily on closing the POC-to-production gap for agents and distributed systems with observability, guardrails, and governance built-in.

---

What does a typical migration from Cadence to Orkes look like?

Short Answer: Migration usually follows a phased path: inventory Cadence workflows, map them to Orkes workflow definitions and workers, integrate via Orkes SDKs/APIs, then cut over gradually with side-by-side runs and workflow versioning.

Expanded Explanation:
You don’t need to shut down Cadence and “big bang” everything into Orkes. A safer approach is to treat this like any critical platform migration: identify high-value workflows, replicate them in Orkes, run them in parallel for a subset of traffic, compare results and metrics, then route more traffic once you’re confident in behavior and performance.

Structurally, you’ll translate Cadence constructs (workflows, activities, task queues) into Orkes primitives (workflows, tasks, workers), wire up your existing services as workers, and replace Cadence client calls with calls to https://api.orkes.io (or your Orkes endpoint) via SDKs or direct HTTP/gRPC. Because Orkes supports versioned workflows and rollback, you can iterate safely without locking your teams into a risky migration window.

Steps:

1. Inventory and classify your Cadence workflows by business criticality, SLA, and external dependencies (upstreams/downstreams).
2. Model equivalent Orkes workflows in the UI or JSON (including retries, timeouts, and compensation), and implement workers in your existing services.
3. Run side-by-side and gradually shift traffic using feature flags or routing logic, monitoring Orkes executions and metrics until you can fully decommission the Cadence flow.

---

How do Orkes and Cadence differ in features and architecture?

Short Answer: Both provide durable workflows, but Orkes layers on UI-driven modeling, agentic workflows, Human Tasks, MCP Gateway, enterprise governance, and analytics, while Cadence is primarily a programmable engine without those platform surfaces out of the box.

Expanded Explanation:
From a core engine standpoint, both systems manage workflow state, retries, and timeouts. The divergence is in how much platform you get around those capabilities, and how opinionated each is about AI and human-in-the-loop automation.

Orkes is explicitly designed as the “missing layer” between agents/humans/services and your infrastructure. It runs event-driven, long-running, and low-latency workflows on the same platform, with visual design, JSON definitions, SDKs, and a CLI. It exposes workflows as APIs or MCP tools so you can plug them directly into LLM-based agents, with guardrails enforced by the orchestration layer.

Cadence exposes powerful primitives through code-first APIs. You write workflows and activities in code, host and scale the cluster, and build your own story for observability and governance using external tools and homegrown conventions. That’s flexible, but you’re on the hook for shaping it into something production-safe across many teams.

Comparison Snapshot:

• Option A: Cadence
  • Code-first workflow definitions, hosted by you.
  • Strong workflow guarantees but limited built-in UI, governance, and agent-native features.
• Option B: Orkes
  • Visual/JSON/SDK workflow definitions plus managed runtime, monitoring, and governance.
  • Native agentic workflows, Human Tasks, MCP Gateway, and enterprise operations (SLAs, RBAC, audit logs).
• Best for:
  • Use Cadence if you want raw workflow primitives and are comfortable owning infra and governance.
  • Use Orkes if you want a shared orchestration platform for AI agents, services, and humans, with traceability and guardrails built in.

---

How do I actually implement my Cadence workflows on Orkes?

Short Answer: Recreate your Cadence workflow logic as Orkes workflows, re-expose your business logic as Orkes workers, and switch your applications to trigger Orkes executions via its APIs or SDKs.

Expanded Explanation:
On Cadence, your workflows and activities live as code; orchestration is implicit in how you write and chain functions. On Orkes, orchestration is a first-class, inspectable object: a workflow definition that calls tasks, which are backed by workers you implement in whatever languages you already use (Java, Python, Go, C#, JavaScript, TypeScript).

The practical implementation path is to extract the orchestration logic from Cadence code into Orkes workflow definitions (using the UI or JSON), then keep your existing activity logic in your microservices, exposing them via HTTP/gRPC or native workers. Your applications will start and monitor workflows via Orkes APIs, rather than embedding orchestration logic directly in application code.

What You Need:

• Workflow definitions: Modeled in Orkes (UI/JSON/SDK), capturing your control flow, retries, timeouts, and compensation steps.
• Workers and integrations: Service code wired as Orkes workers (via SDKs or HTTP/gRPC) plus connectivity to your existing systems (APIs, events, queues).

---

Strategically, when does it make sense to move from Cadence to Orkes?

Short Answer: It makes sense when Cadence starts to strain under multi-team, production use—on-call fatigue, uneven observability, governance gaps—and when you need a governed orchestration layer to move AI agents and critical workflows beyond POC into reliable, auditable operations.

Expanded Explanation:
Cadence is rarely the first thing to fail; it’s everything around it. As more teams adopt the cluster, you end up with a tangle of workflows that are hard to trace end-to-end, inconsistent retry behavior, unclear ownership, and “please don’t touch that” fear around upgrades and schema changes. When agents enter the picture—issuing calls that change real systems—the lack of guardrails, auditability, and easy human approvals becomes a real risk.

Orkes targets exactly this inflection point. It gives platform teams a governed orchestration layer for AI agents + humans + services, with SOC 2 Type II controls, fine-grained RBAC, audit logs, workflow versioning, analytics, and up to 99.99% SLAs. Instead of each team building their own glue on top of Cadence, they get a shared surface for designing, deploying, observing, and debugging workflows—plus MCP-native tooling to plug those orchestrations into LLM agents safely.

Why It Matters:

• Reduced operational risk: Fewer custom scripts and ad-hoc conventions; more standardized retries, timeouts, and governance across teams.
• Faster POC-to-production for agents and workflows: AI agents, human approvals, and system integrations run in a single, observable, auditable platform instead of scattered infrastructure.

---

Quick Recap

Orkes and Cadence both offer durable workflow execution, but they sit at different layers. Cadence is a powerful engine you operate; Orkes is a managed orchestration platform with UI, governance, analytics, and agent-focused features designed to bring AI agents and distributed workflows into real production. Migration is not an all-or-nothing rewrite—you can map Cadence workflows to Orkes definitions, reuse your existing services as workers, and cut over gradually with side-by-side runs and versioned rollouts.

Next Step

Get Started