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ElevenLabs pricing in 2026: what each plan actually buys you for a podcast. ElevenLabs pricing is not one number. It is a subscription tier, a monthly credit allowance, and, since May 7, 2026, a second and separate pay-as-you-go price list for anyone calling the API directly instead of using the app. That split matters if you are trying to figure out whether a plan actually covers what you want to make. Jellypod sits downstream of this exact question a lot: people arrive after pricing out ElevenLabs for a two-host podcast, doing the credit math themselves, and finding out the number on the page and the number they need are not the same thing. To make that concrete, Jellypod, Inc. pulled the median script length across a sample of 2,000 real Jellypod episodes. Stripped of speaker tags and formatting, the median script runs about 6,800 to 7,000 characters of actual spoken dialogue, the exact unit ElevenLabs bills against. That number is what turns "121,000 credits a month" into "about 17 episodes a month," which is the question most people searching ElevenLabs pricing are actually trying to answer. What changed on May 7, 2026. ElevenLabs cut prices across its self-serve API and introduced pay-as-you-go billing for developers who do not want a monthly plan at all: * Text to Speech: up to 55% cheaper. The Flash model on the Creator plan dropped from $0.11 to $0.05 per 1,000 characters processed. * Speech to Text: up to 45% cheaper. Scribe v2 on the Starter plan fell from $0.40 to $0.22 per 1,000 characters. * ElevenAgents: up to 20% cheaper. Starter-plan agent minutes dropped from $0.10 to $0.08 a minute. * Pay-as-you-go, no subscription required. Anyone can now buy credits and use the API without committing to a monthly tier, aimed at teams still prototyping before they commit to production volume. Quality and voice selection did not change. What changed is how much the same generation costs, and who can access API pricing without a subscription first. It's a similar shape to what NotebookLM's own 2026 pricing tiers changed: more capacity for the same money, not a different underlying product. The current plans. | Plan | Monthly price | Annual price | Credits/mo | What it unlocks | | Free | $0 | - | 10,000 | TTS, STT, sound effects, voice design, 3 projects. No commercial license, no voice cloning. | | Starter | $6 | $5/mo | 30,000 | Commercial license, instant voice cloning, 20 projects, dubbing studio. | | Creator | $22 | $18.33/mo | 121,000 | Professional voice cloning, ElevenLabs' most popular tier. | | Pro | $99 | $82.50/mo | 600,000 | 44.1kHz PCM output via API, 192kbps audio quality. | | Scale | $299 | $249.17/mo | 1.8M | 3 workspace seats, team collaboration, 3 professional voice clones. | | Business | $990 | $825/mo | 6M | 10 seats, 10 professional voice clones, TTS as low as 5 cents a minute. | | Enterprise | Custom | Custom | Custom | Negotiated volume and terms. | Annual billing works out to 10 months of fees for 12 months of access on every paid tier. Unused credits roll over, capped at twice the plan's monthly allowance. What a credit actually buys you. ElevenLabs bills close to 1 credit per character on its standard models, which is straightforward until you try to picture what "121,000 credits" means for something you are actually making. Applying the real median script length from Jellypod's own episodes (about 7,000 characters of spoken dialogue per two-host episode) to each plan's monthly allowance gives a much more concrete answer: | Plan | Credits/mo | Roughly this many two-host episodes | | Free | 10,000 | ~1 | | Starter | 30,000 | ~4 | | Creator | 121,000 | ~17 | | Pro | 600,000 | ~85 | | Scale | 1.8M | ~257 | | Business | 6M | ~857 | That estimate assumes a median-length episode on a standard voice model, with no regenerated takes. Longer episodes, professional voice clones, or higher-fidelity models all use more credits per minute, so treat these as a ceiling, not a guarantee. ElevenLabs vs. doing the credit math yourself. ElevenLabs is a voice engine, not a podcast platform. It does not draft a script, assign hosts, or publish an RSS feed, so the credit total above only covers turning finished text into audio. Everything before that (writing the episode, structuring it, editing it after a first pass) is a separate workflow you have to build or buy. Jellypod uses a simpler math on the same underlying problem: credits are only spent when you generate audio, at a flat 60 credits per minute of finished output, regardless of voice or model. Drafting, editing the script, and regenerating lines while you get it right cost nothing. Jellypod's Starter plan, currently $25/mo billed yearly, includes 5,000 credits a month, about 83 minutes of finished audio, or roughly nine episodes at a typical 8 to 10 minute length. You are not estimating character counts against a per-character rate; the platform already did that math for the format it's built around. If your bottleneck is voice quality specifically and you already have a script, editing pipeline, and distribution sorted out, ElevenLabs' per-character pricing is the right tool, and it's worth weighing against other AI voice generators on quality and cost before committing. If you are trying to go from a document or an idea to a published, two-host episode, the character math above is exactly the kind of thing a purpose-built tool absorbs for you. Frequently asked questions. How much does ElevenLabs cost per month? Plans run from Free (10,000 credits, no commercial rights) to $990/mo for Business (6 million credits, 10 seats). Most solo creators land on Starter ($6/mo) or Creator ($22/mo), depending on whether they need professional voice cloning. Enterprise pricing is negotiated directly with ElevenLabs. Did ElevenLabs pricing go down in 2026? Yes. On May 7, 2026, ElevenLabs cut Text to Speech pricing by up to 55%, Speech to Text by up to 45%, and ElevenAgents by up to 20% on its self-serve API, and introduced pay-as-you-go credits for developers who do not want a monthly subscription. How many podcast episodes does an ElevenLabs plan actually cover? It depends on episode length and voice model, but using a real median script length of about 7,000 characters per two-host episode, the Creator plan's 121,000 monthly credits covers roughly 17 episodes, and Pro's 600,000 credits covers roughly 85. Longer episodes or premium voice models use more credits per episode. Is ElevenLabs cheaper than Jellypod? They price different things. ElevenLabs charges for voice synthesis by the character. Jellypod charges 60 credits per minute of finished audio and includes scripting, editing, and publishing in the same credit pool, with drafting and regeneration free on every plan. Which is cheaper depends on whether you're only buying a voice or the entire path from source material to a published episode. Does ElevenLabs have a free plan? Yes, 10,000 credits a month with no commercial usage rights and no voice cloning. It covers text to speech, speech to text, sound effects, and voice design across 3 projects, enough to test quality before paying for commercial rights. The short version. ElevenLabs got meaningfully cheaper in May 2026, up to 55% on the API rates that self-serve developers actually pay, plus a new pay-as-you-go option with no subscription required. The harder question was never the discount, it's translating a credit allowance into something you can plan a show around. Applying real episode-script data puts a number on that: Creator's 121,000 credits is about 17 two-host episodes a month, not an abstract quota. If you'd rather skip that conversion entirely, Jellypod bills by the minute of finished audio and keeps every editing pass free, so the only number you have to track is how many episodes you actually published.

Putting ElevenLabs voice AI into production: an operator's playbook. Every vendor demo of a voice agent sounds flawless. The audio is clean, the model answers instantly, and nobody asks what happens on the four-hundredth call of the day - when a customer switches languages mid-sentence, the CRM lookup times out, and the request is one the script never anticipated. That gap, between a convincing demo and a system that survives a live operation, is where most enterprise voice AI quietly stalls. It is also the gap that a June 2026 partnership between TELUS Digital and ElevenLabs was assembled to close. The headline is a vendor deal; the useful part is the operating model underneath it - a template any company can read for how to take a voice agent from a deployment into something customers actually experience. "Deploying AI agents at enterprise scale is harder than it looks - the technology has to hold up in a live operation with real customers, real complexity, and no margin for a bad experience," said Ashish Uchil, Head of Business Development and Partnerships at ElevenLabs. That sentence, not the partnership, is the thing to internalise. The model: platform, implementation, and the layer in between. The arrangement separates three jobs that companies often wrongly treat as one. Enterprises contract with ElevenLabs directly for ElevenAgents, its AI voice agent platform. A separate implementation partner - here, TELUS Digital - owns deployment, integration, governance and the ongoing operations that keep the thing running after launch. The platform connects to the systems a contact centre already lives in: Genesys, Twilio, Amazon Connect, Zendesk and Salesforce. That separation matters because the platform is rarely what fails. The model can sound natural and still produce a terrible experience if it cannot reach the right record, escalate cleanly, or be supervised in production. The hard, unglamorous work sits in the middle layer - solution architecture, conversation and persona design, systems integration, monitoring, and a responsible-AI review before anyone talks to a customer. It is the same lesson behind why so many enterprise AI programs stall: the demo clears in a week, and then the integration and governance work that no one budgeted for takes the next two quarters. What makes the middle layer credible is operator experience, not slideware. TELUS Digital frames its edge as running customer operations at scale itself, with more than 900 AI engineers and a "forward-deployed" model that embeds those engineers inside client operations to build and refine close to the real work. Whether or not a company uses that particular partner, the structural takeaway holds: buy the platform, but rent - or build - the operating capability to run it. A voice agent that has never been pressure-tested against a real queue, real edge cases, and a real compliance team is a prototype wearing a production badge. What a voice agent actually does on the front line. Framed honestly, a voice agent is capacity. It handles high-volume, routine interactions, and routes complex or sensitive ones to human teams - who, in return, receive better-qualified work. ElevenAgents produces speech across 70+ languages at low latency, and the point is not the voice quality but what happens around it: the agent listens, takes action mid-conversation against back-end systems - updating an account, booking a follow-up - and grounds its answers in the company's own data rather than a generic model's guess. The robustness details are where the demo-to-production gap usually shows. When a customer switches languages mid-call, the agent is meant to follow; when they interrupt or hesitate, the conversation is supposed to keep moving rather than collapse into a re-prompt loop. Those behaviours are cheap to claim and expensive to verify, which is exactly why they belong in a structured evaluation against your own traffic before launch - not in a procurement deck. It can also reach out, not just respond. Proactive contact at the moment of onboarding, before a customer hits a problem, turns out to be one of the higher-value patterns. Designed this way, voice AI is not a replacement for AI customer-support agents staffed by people - it is the thing that lets those people spend their hours on the conversations that genuinely need judgment. The evidence companies should actually weight. Two internal proof points are worth more than the marketing copy, because they are measured outcomes inside a demanding operation rather than projections. The first is training: TELUS Digital runs ElevenLabs inside its own Fuel iX Agent Trainer to generate lifelike voice and chat simulations, letting new hires rehearse everything from routine questions to difficult complaints before taking live calls. After more than 90,000 simulations as of June 2026, onboarding time fell by roughly 20%, with early signs of lower agent turnover. The second is a customer-facing proof-of-concept at TELUS Communications. A voice agent proactively called newly activated home-internet customers during their first 90 days - confirming setup, walking them through their first bill, and answering early questions before they turned into support calls. Account changes, troubleshooting and any request for a person stayed with human agents. Customers who received a welcome call were less than half as likely to cancel within their first 30 days as the average new customer, and rated the calls 8.5 out of 10. That mirrors what Axccelerate Pte. Ltd. saw when Definity rebuilt its contact-centre workflow around AI: the gains come from disciplined scope, not from handing the agent everything at once. Designing for trust before the first call. The TELUS Communications pilot is most instructive for what it refused to automate. Transparency was built into the call flow: the agent identified itself as an AI at the start and before any account detail was discussed, and the call simply ended if a customer declined to continue. Sensitive work was kept on the human side by design. That is the right default, and it generalises. Responsible voice AI means clear disclosure, strict limits on what data the agent can touch, verifying details against live records instead of assuming them, and a full governance and privacy review before launch. The platform layer supports it - ElevenAgents ships with SOC 2, HIPAA and GDPR compliance, plus EU data residency and zero-retention modes for stricter requirements - but compliance features are necessary, not sufficient. Wiring them into a supervised, auditable workflow is an AI automation and infrastructure problem, and in regulated sectors it is the precondition for going live at all, not a finishing touch. Where to start. The use-case map is clearer than the hype suggests. The low-risk, high-return entry points are routine servicing, proactive onboarding, and status-check interactions - high volume, well-bounded, and forgiving. The high-risk zone is sensitive advice, complaint handling, and anything that could create an unsuitable or unrecorded customer communication. Start in the first zone, instrument everything, and expand only once the escalation rules and audit trails have held up under real traffic. The sectors where this pays first are the ones with high-volume, repeatable customer engagement - telecommunications, financial services, utilities and retail - which is precisely where the TELUS Digital and ElevenLabs go-to-market is aimed. The common thread is not the industry but the shape of the work: enough routine contact that containment and proactive outreach move real numbers, and enough regulatory weight that the governance has to be right. Pick the first use case where those two pressures meet, measure containment, resolution and escalation rates honestly, and let the data - not the roadmap - decide what gets automated next. For scale context, ElevenAgents reports 4.8 million agents live and 1.3 million conversations a day across enterprises and governments in 80 countries. The technology is past the question of whether it works. What separates a result from a cautionary tale is the operating discipline around it: the integration, the governance, the human boundary, and the patience to prove each use case before widening it. Voice AI is not a model you buy. It is an operation you run - and that, not the demo, is what companies should be evaluating. Related services How Axccelerate Pte. Ltd. work in this space. More articles.