Using ElevenLabs for Multilingual Voice Agents: Limitations to Know

A voice agent handling insurance claims in Spanish mispronounces a policy number because the TTS defaults entity pronunciation to English. The caller repeats themselves twice, then asks for a human agent. At $8.00 per live agent escalation versus $0.10 for successful automation, each pronunciation failure costs $7.90 in lost efficiency. This scenario plays out across contact centers deploying multilingual voice automation without understanding their TTS provider's constraints. ElevenLabs (https://elevenlabs.io/) multilingual TTS failures create more than awkward moments; they directly erode the ROI that justified the voice agent deployment.

The conversational AI market (https://www.marketsandmarkets.com/Market-Reports/conversational-ai-market-49043506.html) is projected to grow from $2.4 billion to $47.5 billion by 2034, with enterprise voice agents (https://deepgram.com/product/voice-agent-api) driving 20-30% cost reductions through automation. Teams that deploy multilingual TTS without understanding provider constraints leave these savings unrealized.

This article evaluates specific limitations teams encounter when using ElevenLabs multilingual models for production voice agents, and where alternatives with consistent cross-language behavior provide a better fit.

Engineering teams evaluating ElevenLabs for multilingual voice agents should understand these core constraints before committing to production deployments:

• Entity pronunciation (phone numbers, addresses, policy IDs) requires manual text normalization preprocessing in non-English languages
• Language is fixed per API call for TTS synthesis, preventing native code-switching for bilingual callers
• Multilingual v2 costs 2x more than Flash/Turbo v2.5 while imposing 4x API call overhead due to 10,000 character generation limit (vs 40,000 for Flash/Turbo)
• Independent concurrent load benchmarks don't exist; all published latency data uses sequential API calls
• Pronunciation instability occurs after 1,000-2,500 words, requiring 2-3x credit consumption due to regenerations

Production voice agents require consistent pronunciation, latency, and entity handling across all supported languages—not just language count. Teams choosing TTS providers based on language count alone discover this gap after deployment.

Voice agents serving bilingual customers need three capabilities that marketing language obscures. First, entity pronunciation must work correctly in each language: phone numbers, addresses, and alphanumeric identifiers require language-specific pronunciation rules and manual text normalization for non-English languages. Second, latency must remain consistent across languages; sequential latency data shows Flash v2.5 at 350ms (US East) to 527ms (India) end-to-end. Third, the system must handle callers who switch languages mid-conversation, which ElevenLabs TTS doesn't support natively since language is fixed per API call.

ElevenLabs advertises support for 30+ languages across its model lineup. This number obscures critical factors like pronunciation quality for specific entities, latency consistency across languages, and code-switching support. Turbo v2.5 supports 32 languages, yet language count is a feature checkbox, not a production readiness indicator.

Voice agents operate within tight latency budgets. ElevenLabs publishes (https://elevenlabs.io/docs/overview/models) Flash v2.5 at ~75ms inference time, though actual end-to-end latency varies with geographic location, network conditions, and API processing overhead. The vendor documentation notes this excludes application and network latency.

No independent benchmarks have tested either Flash or Multilingual v2 under concurrent API requests. All available latency data represents best-case sequential performance from vendor documentation. For comparison, Deepgram's speech-to-text (https://deepgram.com/product/speech-to-text) achieves sub-300ms latency with support for 140,000+ concurrent API calls.

ElevenLabs multilingual capabilities depend heavily on which model tier you select, how you configure language parameters, and which voices you pair with which languages.

ElevenLabs offers three relevant model tiers. Flash v2.5 prioritizes speed with measured latency around 350ms. Turbo v2.5 offers 3x faster synthesis than Multilingual v2 while supporting multiple languages at the same per-character pricing as Flash. Multilingual v2 provides the highest quality output but operates at latencies substantially above the 100ms threshold documented in academic analysis (https://arxiv.org/abs/2410.10759).

ElevenLabs requires explicit language specification through the language_code parameter in API calls. When this parameter is omitted, the system defaults to English pronunciation rules regardless of the text content. This constraint means you must implement language detection logic on the client side before making TTS requests, an engineering overhead that compounds with each supported language. For teams seeking simpler multilingual implementation, Deepgram's documentation (https://developers.deepgram.com/docs/getting-started-with-pre-recorded-audio) outlines automatic language detection approaches.

ElevenLabs voices perform inconsistently across different languages. ElevenLabs documentation (https://help.elevenlabs.io/hc/en-us/articles/14888917355409-Why-are-numbers-dates-symbols-and-acronyms-not-properly-pronounced-or-spoken-in-the-correct-language) confirms the system struggles with numbers, dates, symbols, and acronyms because different languages have distinct pronunciation rules, with the model often defaulting to English pronunciations. You should implement manual text preprocessing, use the language_code parameter, and consider creating custom pronunciation dictionaries for domain-specific terminology.

Production deployments encounter five specific constraints that require engineering workarounds or alternative provider selection.

ElevenLabs official documentation (https://help.elevenlabs.io/hc/en-us/articles/14888917355409-Why-are-numbers-dates-symbols-and-acronyms-not-properly-pronounced-or-spoken-in-the-correct-language) confirms the system may incorrectly default to English pronunciations when language context isn't explicitly specified. A GitHub issue (https://github.com/elevenlabs/elevenlabs-python/issues/253) from German developers documents specific cases where numbers and dates are pronounced in English within German text. Production deployments require manual text preprocessing: entities must be written out in the target language before API calls.

Language is fixed per API call for TTS synthesis, preventing native code-switching. ElevenLabs multi-voice documentation (https://elevenlabs.io/docs/speech-synthesis/prompting#multiple-voices) describes a workaround: pre-configure multiple voices and switch using XML-style markup tags. This requires language detection logic, pre-configured voice pairs, and state management between segments.

Deepgram Nova-3's multilingual code-switching detects language switches at the word level, while AWS Polly (https://aws.amazon.com/polly/)'s bilingual voices synthesize mid-sentence language switches natively. Both operate without requiring manual voice switching.

Flash v2.5's measured end-to-end latency is 4.7x higher than vendor-published model inference time (~75ms). This gap represents real-world overhead including network latency and API processing. Multilingual v2 operates at even higher latencies. You must decide whether quality gains justify latency increases that may break conversational flow.

ElevenLabs documentation (https://elevenlabs.io/docs/product/troubleshooting/overview) acknowledges pronunciation instability including abrupt language transitions mid-sentence and regional accent shifts. Quality degradation occurs after approximately 1,000-2,500 words. Independent testing (https://qcall.ai/elevenlabs-review) from QCall.ai measured 23% volume fluctuations and required 347 regenerations for a 50,000-word project.

Multilingual v2 has a 10,000 character generation limit per API call versus 40,000 characters for Flash/Turbo v2.5. This creates a 4x increase in required API calls for equivalent content volumes, compounding both latency and cost at scale.

Understanding the business impact helps justify provider evaluation time and potential migration costs.

When a voice agent mispronounces a policy number in Spanish, callers repeat themselves or request human agents. Deepgram's implementation with Five9 (https://deepgram.com/customers/five9) demonstrated $7.90 cost savings per successful self-service interaction ($0.10 automated vs $8.00 live agent). Pronunciation failures that trigger escalations multiply across every bilingual caller.

The Five9 deployment also showed 2-4x higher accuracy than alternative speech-to-text (https://deepgram.com/product/speech-to-text) solutions for alphanumeric inputs like account numbers, order numbers, and policy IDs, with doubled user authentication rates compared to previous ASR implementations.

ElevenLabs' 10,000 character limit per Multilingual v2 generation versus 40,000 characters for Flash/Turbo v2.5 creates a 4x increase in required API calls. Documented pronunciation instability results in 2-3x credit consumption due to quality control regenerations. Multilingual v2 pricing at $0.24 per 1,000 characters versus $0.12 for Flash/Turbo v2.5 compounds these constraints.

At scale, these factors multiply: a 100M character monthly workload on Multilingual v2 costs $24,000 baseline before regenerations, compared to $12,000 for Turbo v2.5. Adding the 2-3x regeneration multiplier pushes actual costs to $48,000-$72,000 monthly for Multilingual v2 versus $24,000-$36,000 for Turbo v2.5.

Before committing to any multilingual TTS provider, test these four areas using your actual production requirements.

Create test cases with phone numbers, addresses, and policy IDs in each target language. Test without preprocessing to establish baseline behavior, then test with recommended normalization approaches. For ElevenLabs, manual text normalization is required for non-English languages.

Standard API tier limits (5-15 concurrent requests) prevent independent researchers from testing realistic concurrent load. Request evaluation access at 10, 25, 50, and 100 simultaneous API requests, measuring p50, p95, and p99 latency percentiles from your actual deployment regions.

Test bilingual conversation scenarios where callers switch languages naturally. ElevenLabs TTS fixes language per API call, requiring manual voice switching workarounds. AWS Polly supports fully bilingual voices capable of switching languages mid-sentence without manual intervention.

ElevenLabs offers on-premises deployment with air-gapped environment support and three isolated data residency regions. Zero Retention Mode must be explicitly enabled to ensure processing remains within selected regions.

The right provider depends on whether your voice agents serve monolingual or bilingual callers.

ElevenLabs works well for single-language deployments where entity preprocessing is acceptable and sequential latency budgets accommodate 350ms+ response times.

Consider alternatives when voice agents serve bilingual callers who switch languages mid-conversation, entity pronunciation accuracy directly affects call completion rates, or latency budgets require consistent sub-400ms response times under concurrent load. Deepgram achieves sub-300ms latency with over 90% accuracy, supporting 140,000+ concurrent API calls. Deepgram Nova-3 with native code-switching detection combined with Deepgram Aura-2 (https://deepgram.com/product/text-to-speech) or AWS Polly's bilingual voice synthesis addresses these requirements.

Try it yourself: grab $200 in free credits (https://console.deepgram.com/signup) and test multilingual voice agents against your production requirements.

ElevenLabs fixes language per API call for TTS synthesis, preventing native code-switching. The workaround requires pre-configuring multiple voices and switching using XML-style markup tags. For production bilingual voice agents requiring automatic code-switching, consider Deepgram Nova-3 combined with AWS Polly's bilingual voices.

The system may default to English pronunciation rules regardless of the specified language. German developers documented cases where numbers were pronounced in English within German text. Production deployments require manual text preprocessing where entities are written out in the target language before API submission.

Flash v2.5's measured end-to-end latency from US East is 4.7x higher than vendor-published inference times. Multilingual v2 operates at substantially higher latencies. Geographic location adds significant variance; benchmarked figures show a 50% increase from US East to India for identical API calls.

Yes, with proper configuration. ElevenLabs offers HIPAA-eligible services with Business Associate Agreements, on-premises deployment with air-gapped support, and Zero Retention Mode. SOC 2 Type 2, PCI DSS v4.0.1, and ISO/IEC 27001 certifications are available.

Multilingual v2 costs approximately $0.24 per 1,000 characters at Business tier compared to $0.12 for Flash/Turbo v2.5. Multilingual v2 also has a 10,000 character generation limit versus 40,000 for Flash/Turbo v2.5, requiring 4x more API calls for equivalent content volumes.