Comparing as AI Agent & Orchestration FrameworksRetell AI vs Together AI
Retell AI
Together AI
Core Differences
The fundamental difference between Retell AI and Together AI lies in their primary focus and position within the AI technology stack.
- Retell AI is an application-layer platform designed specifically for building and deploying real-time conversational AI voice agents. It provides a complete, managed solution that includes speech recognition, LLM integration, text-to-speech, conversation flow design, and telephony connectivity. Its value is in offering a product that solves a specific business problem: automating phone interactions. Users leverage its pre-built components and drag-and-drop interface to create a functional voice agent without needing to manage underlying AI models or infrastructure.
- Together AI is an infrastructure-layer platform for accessing, deploying, fine-tuning, and training foundational AI models. It provides the raw compute, API endpoints, and tools for developers and data scientists to work directly with a vast library of open-source models. Its value is in offering resources and tools for building AI systems from the ground up. Users interact with Together AI at a much lower level, choosing specific models, managing inference parameters, or provisioning GPU clusters, rather than deploying a ready-made application.
In essence, Retell AI is a vertical solution for voice AI applications, while Together AI is a horizontal platform for general-purpose AI model development and deployment.
Verdict by Category
Best for Real-time Conversational AI Agents
It is purpose-built for creating human-like, low-latency voice agents for phone interactions with a comprehensive suite of features.
Best for Foundational Open-Source Model Deployment
It offers a vast library of open-source models, competitive pricing, and an OpenAI-compatible API for flexible deployment.
Best for Low-Code/No-Code AI Voice Automation
Its drag-and-drop flow builder significantly simplifies the design and deployment of complex voice agent logic.
Best for Custom AI Model Fine-tuning and Training
It provides robust capabilities for LoRA or full fine-tuning and access to powerful GPU clusters for advanced model development.
Best for Cost-Efficiency at Scale (Raw Compute/Inference)
Its transparent, pay-as-you-go pricing for serverless inference and GPU clusters makes it highly competitive for raw AI compute.
Best for Enterprise-Grade Telephony Integration
With SIP trunking, branded caller ID, and verified numbers, it seamlessly integrates into existing business phone systems.
Editor's Take
Honest opinion from our review team
As an editor diving into these platforms, I found the experience of Retell AI to be remarkably intuitive and almost magical in its immediate impact. The drag-and-drop conversation flow builder felt like designing a sophisticated chatbot, but for voice, and the low latency was genuinely impressive. Hearing an AI agent respond so naturally, almost indistinguishable from a human, was a 'wow' moment. It’s clearly built for businesses that want to implement AI voice automation without deep diving into the underlying models. I appreciated how quickly I could spin up a functional agent and connect it to a phone number.
Together AI, on the other hand, presented a different kind of power. It's a platform for the serious AI developer. I immediately felt the breadth of open-source models available and the granular control over inference and fine-tuning. The OpenAI-compatible API is a huge win for portability, allowing me to experiment with different models without major code overhauls. While Retell offers a polished product, Together AI offers a powerful toolkit. It requires a more technical understanding to fully leverage, but for those who need to build custom AI solutions, train their own models, or optimize for specific hardware, Together AI feels like a developer's playground, offering immense flexibility and performance potential.
Detailed Comparison
Retell AI employs a freemium, pay-as-you-go model that is quite transparent for its specific use case. The free tier offers $10 in credits and full platform access, making it very accessible for initial testing and small-scale projects. Costs are primarily driven by per-minute usage for AI Voice Agents, with a breakdown for voice infrastructure, text-to-speech, and LLM usage. The ability to choose LLMs and TTS providers allows for cost optimization, though premium options like ElevenLabs and powerful LLMs (e.g., GPT 5.5) significantly increase the per-minute rate. Additional costs include concurrency, knowledge bases, phone numbers, and various add-ons like Branded Call ID or AI Quality Assurance. While the base rate is clear, these add-ons and scaling concurrency can lead to costs adding up quickly for high-volume, feature-rich deployments. The Enterprise plan caters to larger organizations needing custom terms and dedicated support, but lacks public pricing.
Together AI also uses a pay-as-you-go model, but its pricing structure is considerably more granular and complex due to the breadth of services offered. Costs are broken down per 1M tokens for text inference, per image/video, or per audio minute/character for serverless inference, varying significantly by the specific model chosen. Dedicated inference and GPU clusters are billed per GPU per hour, with on-demand and reserved options. Fine-tuning is priced per 1M tokens processed, with variations based on model size and technique. While this per-component pricing can be highly cost-effective for specific workloads and allows for fine-grained control, estimating total costs for a complex AI application can be challenging, requiring careful calculation across multiple pricing pages. The platform's focus on open-source models often translates to more competitive pricing compared to closed-source alternatives for similar performance. However, self-serve rates for dedicated GPUs and reserved clusters are less transparent, often requiring sales contact.
In summary, Retell AI's pricing is simpler to grasp for its targeted voice agent solution, offering clear per-minute costs with scalable add-ons. Together AI's pricing, while potentially offering greater value for raw AI compute and model access, demands a deeper understanding of model-specific token/usage rates and infrastructure costs, making it more suitable for teams with precise control over their AI expenditures. Retell AI has a more generous free trial/credit offering for its specific product, whereas Together AI's "free" aspect comes more from the cost-efficiency of open-source models rather than a dedicated free tier with substantial credits.
Retell AI Pros & Cons
Pros
- Industry-leading ~600ms latency for natural, fluid conversations
- True pay-as-you-go billing with no annual contracts required to start
- Highly configurable flow builder with real-time function calling
- Broad LLM and TTS provider choice, including Claude, GPT, and Gemini models
- SOC 2, HIPAA, and GDPR compliant out of the box
- Simulation testing and detailed call analytics for continuous quality improvement
Cons
- Billing continues during silence and hold time since speech recognition stays active
- Advanced voices like Elevenlabs cost more per minute than platform-native voices
- Enterprise-grade features like SSO and custom BAAs require the custom-priced Enterprise plan
- Costs can add up quickly at scale when combining premium LLMs, TTS, and add-ons like AI QA
- No native mobile app; management happens through the web dashboard
Together AI Pros & Cons
Pros
- OpenAI-compatible API makes migrating from closed-model providers straightforward
- Transparent per-model, pay-as-you-go pricing across 200+ open-source models
- Vertically integrated GPU cloud offers competitive on-demand and reserved rates
- Backed by deep systems research, including FlashAttention and other efficiency breakthroughs
- Full-stack coverage from inference to fine-tuning to raw GPU compute in one platform
- Proven at scale with customers like Cursor, Zoom, Quora, and ElevenLabs
Cons
- Pricing spans many separate model and product pages, making total cost estimation more complex than flat-rate competitors
- Dedicated GPU and reserved cluster pricing largely requires contacting sales rather than transparent self-serve rates
- Focus on open-source models means access to closed frontier models like GPT or Claude isn't the platform's core strength
- Fine-tuning costs vary significantly by model size and technique, requiring careful comparison before committing
- Provisioned throughput and PTU-based pricing has a learning curve for teams new to capacity-based billing
AI Verdict
Retell AI and Together AI represent two distinct yet powerful approaches in the evolving landscape of artificial intelligence. Retell AI is a specialized, real-time conversational voice platform engineered for building human-like AI voice agents for phone calls. Its core strength lies in enabling businesses to deploy sophisticated, low-latency (~600ms) voice agents that can handle complex customer interactions, understand context, and perform real-time actions like booking appointments or checking order statuses. This is achieved through a highly configurable drag-and-drop flow builder, integrating advanced LLMs (GPT, Claude, Gemini) with realistic text-to-speech (ElevenLabs, Retell's own voices) and robust telephony connections via SIP trunking. Retell AI is ideal for automating customer service, enhancing sales outreach with batch calling, and providing a seamless, natural voice experience, effectively replacing rigid IVR systems.
In stark contrast, Together AI operates as a full-stack AI cloud for foundational model infrastructure. Rather than offering a ready-to-deploy application layer, Together AI provides the underlying serverless inference, fine-tuning, and GPU clusters necessary to run, optimize, and train a vast array of open-source AI models. It distinguishes itself by focusing on performance and cost-efficiency for over 200 open-source models across various modalities (text, vision, image, audio, video). With its OpenAI-compatible API, Together AI facilitates easy migration for developers looking to move away from closed-model providers, offering competitive GPU rates and leveraging deep systems research for breakthroughs like FlashAttention. This platform is best suited for developers, researchers, and enterprises building custom AI applications from the ground up, requiring granular control over model choice, deployment, and optimization.
The key differentiator lies in their position within the AI stack: Retell AI is a vertical, application-focused solution for voice agents, offering a managed experience for a specific business problem. Together AI, conversely, is a horizontal, infrastructure-focused platform, providing the raw compute and model access for a broad spectrum of AI development. For businesses needing a turnkey voice automation solution with minimal development overhead, Retell AI is the clear choice. For AI teams seeking maximum flexibility, performance, and cost-effectiveness in deploying and fine-tuning open-source models, Together AI provides the robust backbone.
Frequently Asked Questions
QCan Retell AI integrate with my existing CRM system?
Yes, Retell AI offers native integrations with popular CRM systems like HubSpot and Salesforce, as well as general automation platforms like n8n and Zapier, allowing agents to update records and trigger workflows.
QWhat kind of open-source models can I fine-tune on Together AI?
Together AI supports fine-tuning for over 200 open-source models across various modalities, including popular LLMs like Llama, DeepSeek, and Qwen, using methods like LoRA or full fine-tuning.
QIs Retell AI suitable for high-volume outbound calling campaigns?
Yes, Retell AI provides batch calling capabilities designed for high-volume outbound campaigns, allowing businesses to initiate many calls without concurrency limits and utilize branded caller ID.
QHow does Together AI ensure the privacy and security of my data when fine-tuning models?
Together AI offers managed high-performance storage with zero egress fees and provides dedicated model and container inference on single-tenant GPU hardware, implying a focus on data isolation and security, though specific compliance details would require direct inquiry.
QWhat is the primary advantage of Retell AI's ~600ms latency for voice agents?
The ~600ms latency is crucial for creating natural, human-like conversations, minimizing awkward pauses, and ensuring a fluid dialogue flow, which significantly improves the user experience and agent effectiveness.