Is Pipedream or Relevance AI cheaper?

Pipedream starts at Free (100 credits per month, hard-capped); paid plans from $29 per month billed annually, or $45 month-to-month (free plan available). Relevance AI starts at Free (200 Actions per month); Pro from $19 per month billed annually (free plan available).

Pipedream vs Relevance AI

An independent, review-free comparison compiled by the SaaSTracker editorial team. Both products are profiled in full, and neither can pay for placement here.

The short answer

Editorial assessment

Pipedream compared with Relevance AI

Different layers of the same stack. Relevance AI builds AI agents and agent teams as the primary object, with tools and memory arranged around them. Pipedream builds deterministic event-driven workflows and exposes integrations to agents through MCP. If you want an agent that reasons about a task, Relevance AI is the shorter path; if you want a workflow that runs the same way 40,000 times and occasionally calls a model, Pipedream is. Teams often run both, with Pipedream supplying the authenticated tools.

Choose Pipedream if

Technical GTM and RevOps engineers, and small product teams, who want the connector catalog and hosted auth of an iPaaS but refuse to express real logic as configured boxes. Also strong for SaaS companies that want to ship integrations to their own customers without building each one, via Connect.

Choose Relevance AI if

Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls.

Side by side

13 attributes

Attribute	Pipedream	Relevance AI
Category	GTM Engineering	GTM Engineering
Starting price	Free (100 credits per month, hard-capped); paid plans from $29 per month billed annually, or $45 month-to-month (free plan available)	Free (200 Actions per month); Pro from $19 per month billed annually (free plan available)
Pricing model	Subscription plus usage. Every plan includes a monthly allowance of credits, where one credit is 30 seconds of execution at the default 256 MB of memory, and credit burn scales with the memory you configure. Plans also include AI token allowances for the AI-assisted builder. Tiers gate active workflow count, connected accounts, event history retention, schedule granularity, retries, GitHub sync, and security controls. Prices below are the annual rate; monthly billing costs meaningfully more.	Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time.
Free plan	100 credits per month with a hard usage cap, 3 active workflows, 3 connected accounts, 1M AI tokens, 7-day event history, 5-minute minimum schedule interval, and no auto-retry	200 Actions per month, a one-time grant of 1,000 Vendor Credits, one build user, one project
Free trial	No time-limited trial of paid tiers; the permanent Free plan is the trial. Business plan trials are handled by sales.	No time-limited trial; the free plan serves that purpose
Best for	Technical GTM and RevOps engineers, and small product teams, who want the connector catalog and hosted auth of an iPaaS but refuse to express real logic as configured boxes. Also strong for SaaS companies that want to ship integrations to their own customers without building each one, via Connect.	Revenue and operations teams that want AI agents doing repeatable research, enrichment, outreach, and triage work under supervision, and that have someone willing to treat agent building as an ongoing job rather than a one-week setup. Strongest where the work is judgment-heavy and unstructured, which is exactly where a rules-based automation tool stalls.
Setup time	A first working workflow takes 10 to 20 minutes: sign up, create a trigger, connect one app, add a step. A production-grade workflow with retries, concurrency limits, error alerting, and GitHub sync is a half day. Connect implementations are a genuine engineering project, typically one to two weeks for a first customer-facing integration.	An hour to sign up, clone a marketplace agent, and see it run. A week or two to get a first agent doing real work against real data, most of which is spent writing instructions, connecting integrations, and discovering the edge cases where the agent is confidently wrong.
Learning curve	Low for anyone who writes JavaScript or Python; the platform mostly gets out of the way. The parts that take longer are the ones specific to the model: understanding what a credit actually costs at your memory setting, deciding when to use a data store rather than an external database, and structuring long work as queued events instead of one long execution.	Low to begin, steep in the middle. The visual tool builder is approachable for a non-engineer, but reliable agents require prompt discipline, structured output enforcement, and an appetite for reading traces. Multi-agent Workforces and custom API steps are a genuine technical exercise, not a no-code one.
Platforms	Web application, REST API, CLI, Node.js and Python SDKs for Connect, Hosted and self-deployed MCP servers	Web application, REST API, Model Context Protocol (MCP) server, Slack, Microsoft Teams, Android app, Embeddable tools and agents
Compliance	SOC 2 Type II, GDPR, HIPAA (Business plan)	SOC 2 Type II, GDPR
Founded	2019	2020
Headquarters	San Francisco, California	Sydney, Australia
Ownership	Acquired by Workday (announced November 2025, closed early 2026)	Independent, venture-backed (OnSearch Pty Ltd trading as Relevance AI)

Strengths and limitations

Pipedream

Strengths

Arbitrary Node.js, Python, Go, and Bash inline with configured actions, so no logic problem forces you off the platform.
Managed authentication across 3,000+ apps removes the single most tedious part of building integrations, and code steps get the same credentials as configured ones.
Compute-based pricing is unusually favorable for high-volume, high-filter workloads where most events are discarded early.
Bi-directional GitHub sync with branches and pull requests makes automation reviewable like the rest of the codebase, which very few tools in this category offer.

Limitations

Cold starts of several seconds after roughly five minutes of inactivity make Free-tier workflows unsuitable for interactive triggers such as Slack slash commands; warm workers are a paid-tier feature.
The 750-second execution ceiling (300 on Free) means genuinely long batch jobs must be chunked into queued events rather than run as one pass.
Advanced includes no more credits than Basic, which surprises buyers who upgrade expecting headroom and get feature unlocks instead.
Paid-plan overages are uncapped, so a retry loop against a slow API can burn thousands of credits overnight and appear on the next invoice.

Relevance AI

Strengths

The agent abstraction is genuinely well built: instructions, scoped tools, knowledge, approval gates, and escalation rules are all first-class rather than bolted on.
Tracing and cost visibility are better than most competitors, showing per-step model calls and per-run cost, which turns debugging from guesswork into reading a log.
The two-meter pricing separates platform cost from model cost honestly, and bring-your-own-keys removes the markup that most agent platforms quietly charge.
Approval gates and escalation are the right primitives for letting an agent touch a CRM or an inbox without an incident.

Limitations

Cost is hard to forecast. Actions are charged per tool run including failures and retries, and a multi-agent Workforce fans out into far more runs than a first-time buyer estimates.
The public pricing page shows only Enterprise as of August 2026; Free, Pro, and Team still exist but you have to dig through documentation to find their terms, which is a deliberate move away from self-serve buyers.
The Pro to Team step is punishing, roughly $19 to $234 per month for under three times the Actions, and there is no longer a Business tier in between since it was retired in September 2025.
Governance essentials are Enterprise-gated: evals, agent performance observability, audit logs, SSO, RBAC, and Salesforce, Snowflake, and Zendesk triggers are all out of reach of a self-serve buyer.

Pricing compared

Pipedream

Subscription plus usage. Every plan includes a monthly allowance of credits, where one credit is 30 seconds of execution at the default 256 MB of memory, and credit burn scales with the memory you configure. Plans also include AI token allowances for the AI-assisted builder. Tiers gate active workflow count, connected accounts, event history retention, schedule granularity, retries, GitHub sync, and security controls. Prices below are the annual rate; monthly billing costs meaningfully more.

Free$0
Basic$29
Advanced$49
Connect$99
BusinessCustom

For a technical buyer, Pipedream is one of the better value propositions in this category, because the pricing axis rewards writing efficient code instead of punishing you for having many steps. A team replacing a per-task automation bill that scales with record volume often sees the number fall sharply, since filtering happens inside one credit-cheap step rather than across a dozen billed actions. The caveats are real: the included credit allowance stops improving between Basic and Advanced, overages on paid plans are uncapped, and the cost of a workflow is a function of upstream API latency you do not control. Compared with self-hosting n8n, you are paying roughly $600 a year to not run infrastructure, which is a straightforward trade for most small teams and a bad one for teams that already have a Kubernetes cluster and a spare engineer.

Relevance AI

Two meters plus a plan fee. Actions are consumed one per tool run, including runs that fail or retry. Vendor Credits cover the underlying LLM and third-party model spend, passed through at wholesale with no markup, and can be bypassed entirely on paid plans by connecting your own OpenAI or Anthropic API keys. Plans set the included Actions, included Vendor Credits, number of build users, and number of projects. This structure took effect on 8 September 2025 and replaced the previous single-credit model; the Business plan was discontinued at the same time.

Free$0
Pro$19
Team$234
EnterpriseCustom

Priced against what it replaces, the Pro plan is cheap: $19 a month plus metered model spend for work that would otherwise be a contractor's afternoon. The problem is the shape of the curve above it. Team at $234 a month gives under three times the Actions for more than ten times the price, and the governance features that make agents safe to leave running (evals, observability, audit logs, SSO) are not purchasable at any published price. A small business gets real value from the free and Pro tiers for supervised, human-in-the-loop work. A company that wants agents operating unattended on customer data is buying Enterprise, and should assume a five-figure annual commitment and a procurement cycle.

Editorial verdict on each

Pipedream

Pipedream is the automation platform for people who can write code and are tired of pretending they cannot. The combination of a 3,000-app connector catalog, managed authentication that your own code can use, a hosted runtime with no infrastructure to operate, and GitHub sync with pull requests is not matched by anything else at this price. Compute-based pricing is the right shape for GTM workloads that ingest a lot and keep a little, and the published tiers stay honest all the way to $99 a month before sales gets involved. The reservations are specific rather than generic: cold starts rule out latency-sensitive triggers on lower tiers, the 750-second ceiling forces awkward chunking on batch work, Advanced buys features rather than credits, and uncapped paid-plan overages punish a retry loop. Weigh those against the ownership change, since Workday closed its acquisition in early 2026 and an independent developer platform is now a component of an enterprise agentic strategy. For a technical small team today it remains an excellent buy; keep an eye on the roadmap and the renewal terms.

Read the full Pipedream profile

Relevance AI

Relevance AI is one of the better-engineered agent platforms available to a small company, and the parts that matter most for production use, tracing with per-step cost, approval gates, escalation rules, a job queue with retries, and an honest two-meter bill, are more mature here than in most of its peers. The friction is commercial rather than technical. Pricing jumps from $19 to $234 per month with under three times the Actions, the Business tier that used to bridge that gap was retired in September 2025, and the governance features that make agents safe to leave running unattended (evals, observability, audit logs, SSO) are all locked behind a quote-only Enterprise plan. The public pricing page now advertises nothing else, which tells you where the company's attention has gone. Buy the Free or Pro plan for supervised, human-in-the-loop work where a person reviews output, and expect real value from it. Do not plan an unattended, customer-data-touching deployment on a self-serve plan, because the tools to prove it is behaving are not sold at that price.

Read the full Relevance AI profile

Pipedream profile last reviewed 2026-08-23; Relevance AI last reviewed 2026-08-23. Pricing is compiled from public sources and can change without notice. See our methodology.