Vapi vs Retell:what breaks on each
We grade production calls on both platforms, so this isn't a feature-page recap. Here's how Vapi and Retell differ for the team building on them — and what actually fails on each once real callers show up.
Vapi vs Retell, side by side
Both are production-grade voice agent platforms. They differ in how much of the stack you own. Features as of October 2026 — check each vendor's docs before you commit.
| Vapi | Retell | |
|---|---|---|
| Building model | API-first. Assistants (one prompt plus tools) and Squads (several assistants with context-preserving transfers), configured through the API, SDKs, CLI, or dashboard. | Single-prompt and multi-prompt agents, plus a visual Conversation Flow builder where each node has its own instructions, transitions, and functions. |
| Model stack | You pick each component — transcriber, LLM, and voice — from dozens of providers, or bring your own keys and models. | Choose the LLM and voice from Retell's supported options, or connect your own LLM over a WebSocket. More of the pipeline, including turn-taking, is managed for you. |
| Pricing shape | A per-minute platform fee, with transcription, LLM, voice, and telephony billed on top at provider rates. | A per-minute rate assembled from voice infrastructure plus the LLM, voice, and telephony you select. Flow agents are billed by time spent in each node. |
| Pre-launch testing | Evals and test suites: mock conversations that check assistant behavior, flow, and tool usage before you ship. | Simulation and batch testing: define caller personas and success criteria, then run many simulated conversations against an agent. |
| Production QA | Per-call analysis you configure up front. Finding patterns across thousands of calls is left to you. | Post-call extraction and analysis you configure up front. Finding patterns across thousands of calls is left to you. |
| Best fit | Engineering teams that want to own every component and orchestrate agents in code. | Teams that want a managed stack and flows that non-engineers can read and edit. |
What breaks on each in production
The platform shapes the failure. These are the patterns that show up most when you grade every call instead of sampling a few.
Vapi: tool calls that fail silently
A webhook times out mid-call and the assistant talks right past it. The caller hears confidence; your CRM never gets the update. With every provider swappable, these failures also shift whenever you change the transcriber, model, or voice.
Vapi: Squad handoffs that drop context
Calls get transferred to the wrong assistant, or the next assistant re-asks what the caller already said. Latency dashboards stay green — the failure is only visible in what was said.
Retell: callers falling off the flow
Flows encode the happy path. Real callers interrupt, backtrack, and answer questions no node anticipated. The agent stalls, loops, or improvises on a node that was never meant to handle it.
Retell: flow edits that regress old paths
Every node edit is a deploy. Fixing Tuesday's transition quietly breaks Monday's working branch, and nothing flags it until a customer complains — weeks later.
Whichever you pick, grade the real calls
Step 01
Connect the platform you're on
Paste an API key, or send calls through one REST call (calls.ingest). Zelto ingests every completed call — recording, transcript, tool calls, metadata. No changes to your agent.
Step 02
Every call graded
Zelto learns what a good call looks like from your own traffic and grades every production call against it. No scorecard to write, no scenarios to maintain.
Step 03
Findings with the fix drafted
Calls that fail for the same reason become one Finding, ranked by volume, with example calls, a drafted prompt or flow fix, Slack alerts, and an optional Linear ticket.
Vapi vs Retell: common questions
What teams ask us when they're choosing — or switching — platforms.