In today’s day and age, voice agents have evolved from being voice agents which can assist you with daily tasks to real-time, always-on systems embedded in enterprise workflows. Today, the question isn’t whether your business should adopt Voice AI- but whether the platform you choose can stand up to production pressure.

When companies are evaluating enterprise readiness, they look at two players- who have built voice from the ground up- Smallest.ai and Retell. 

But once you move beyond the surface, their architectural bets and enterprise readiness begin to diverge.

This blog compares Smallest and Retell through the lens of scalability, observability, latency control, compliance, and integration- the things that actually matter when you’re not running a demo, but a system where your business is handling hundreds of calls a day. 

Retell: Real-Time Voice, Built with API’s

Retell is a compelling option in the modern voice AI stack. It gives developers the ability to deploy full-duplex conversational agents quickly, with latency-focused streaming and natural barge-in capabilities.

Its architecture prioritizes real-time audio streaming and modular flexibility: developers can bring their own LLM, STT, or TTS components, or use Retell’s preferred defaults. For early-stage teams building voice-first interfaces, this plug-and-play architecture is powerful.

However, modularity introduces trade-offs in production environments:

• Inference loop latency is variable when scaling because each external component (LLM, STT, TTS) adds hop delays.
  
• Visibility is limited- since Retell doesn’t own the models, tracing issues through the stack becomes harder.
  
• Deployment is cloud-only- suitable for prototyping, but limiting for data-sensitive or compliance-heavy sectors such as banking and healthcare. 

Smallest.ai: An Integrated Voice Infrastructure, 

Smallest.ai takes a more vertically integrated approach with their technology. It owns and operates the entire voice inference loop—including:

• Electron V2: a compact LLM purpose-built for spoken language understanding, instruction following, and hallucination control
  
• Lightning V2: an ultra-low-latency TTS engine that generates 10s of natural speech in ~100ms
  
• Native STT: tightly coupled for streaming token-by-token input with minimal processing delay
  
  

This full-stack approach results in:

• True token-level barge-in
  
• Lower failure rates from model coordination mishaps
  
• Better cost optimization at scale
  
  

In high-throughput environments- like sales centers, compliance-sensitive support desks, or inbound call orchestration- predictability and control matter more than raw flexibility.

Observability and Debugging

Now, let’s look into how Smallest performs against Retell, when it comes to observability and debugging. One of the biggest differences that pops up between Smallest and Retell lies in visibility.

Retell gives developers useful logs at the session level, but the granularity required to debug token delays or TTS anomalies in-flight is minimal.

Smallest, in contrast, offers first-class observability- and that is down to per-token tracking, model stall detection, and barge-in latency benchmarks. This makes it far more amenable to performance tuning, A/B testing, and post-incident tracing in live production environments.

Custom Training on Private Enterprise Data

Another architectural divergence between both is : model ownership and training.

Retell’s API model assumes that the developer brings, or selects third-party models. While flexible, this means:

• No fine-tuning on internal enterprise data without external dependencies
  
• No alignment with domain-specific knowledge unless explicitly integrated
  

Smallest, on the other hand, offers custom training of its Electron V2 model on private datasets, making it particularly well-suited for:

• Domain-specific instruction alignment (e.g., banking, legal, medical)
  
• Vocabulary tuning based on historical tickets, transcripts, or CRM data
  
• Last-mile accuracy guarantees, which are difficult with general-purpose models

Deployment and Compliance

In enterprise environments, deployment flexibility is not a bonus—it’s a prerequisite.

Retell, while cloud-capable and performant, is currently constrained to public infrastructure. That makes it a challenge for customers operating in regulated sectors or requiring data residency guarantees.

Smallest meets organizations where they are, whether that's in a cloud VPC, on-prem, or air-gapped for maximum security.

Head-to-Head: What the Metrics Say

Conclusion

Retell is one of the most capable real-time voice platforms available for developers who want to build fast, experiment often, and own orchestration logic.

But if your voice stack is moving from MVP to mission-critical, and you care about:

• Predictable latency at scale
• Custom tuning on internal data
• Human-in-the-loop orchestration
• Deployment across secure environments
• Deep observability across the full inference pipeline
  
  

Then Smallest.ai offers the infrastructure-level depth you need.