Pipecat Alternatives – Top 12 Competitors Compared

Building real-time voice and multimodal AI agents requires tools that can manage streaming audio, low-latency responses, and orchestration across speech recognition, language models, and text-to-speech.

Pipecat is a popular open-source framework for this use case, giving developers direct control over real-time conversational pipelines.

It’s not the only option, though. Some teams prefer framework-level control, while others choose managed platforms or lower-level APIs depending on how much orchestration they want to own.

In this guide, you’ll find a comparison of Pipecat alternatives, grouped by how they approach real-time voice and multimodal AI:

• Framework-level tools that expose the full agent loop
• Platform-level solutions that abstract orchestration behind managed services
• Low-level real-time APIs that provide core building blocks

Pipecat Overview

Pipecat is an open-source Python framework for building real-time voice and multimodal conversational agents. It’s designed to help developers orchestrate speech recognition, large language models, and text-to-speech into streaming pipelines that support natural, interactive conversations.

Rather than abstracting agent behavior behind a managed service, Pipecat exposes the full conversational loop in code, giving teams fine-grained control over how agents process input, respond, and handle real-time events like interruptions.

Main Features

• Pipeline-based orchestration: Build real-time voice agents as a sequence of modular processing steps, such as audio input, speech recognition, language model inference, and text-to-speech output.
• Frame-based processing model: Handles audio, text, and control signals as structured frames, enabling streaming responses and support for interruptions.
• Pluggable, reusable components: Swap speech recognition, language models, and text-to-speech providers without changing the overall pipeline design.
• Transport modularity: Keep agent logic independent of how users connect, whether over WebRTC, WebSockets, or other real-time transports.
• Voice activity detection support: Integrates voice activity detection to manage turn-taking and speech boundaries in live conversations.
• Structured conversation tooling: Supports defining conversational states and transitions for more controlled or guided interactions.
• Ecosystem tooling: Includes SDKs, command-line tools, and debugging utilities to support local development and production deployments.

Primary Use Cases

Pipecat is best suited for teams building custom, real-time conversational experiences where control over agent behavior and latency is important.

Common use cases include:

• Voice assistants and AI agents: Build interactive agents that respond in real time to spoken input, with fine-grained control over speech, timing, and responses.
• Customer support and service bots: Power voice-based support experiences that integrate with internal tools, knowledge bases, or workflows.
• Multimodal conversational applications: Combine voice with text or other modalities to create richer, context-aware agent interactions.
• Prototyping and research: Experiment with real-time conversational pipelines, model combinations, and interaction patterns in a flexible, open-source framework.
• Embedded voice experiences: Add real-time voice interaction to apps, devices, or web experiences without relying on a fully managed platform.

Advantages of Pipecat

• Full control over agent orchestration: Developers own the real-time conversational loop, including streaming behavior, turn-taking, and how different AI services are combined.
• Framework-level flexibility: Pipecat acts as a composable foundation rather than a fixed product, making it easier to adapt agent behavior to specific use cases.
• Provider-agnostic design: Supports integrating different speech recognition, language model, and text-to-speech services without locking teams into a single vendor.
• Built for real-time interaction: Designed around streaming inputs and outputs, enabling natural voice conversations and interruption handling.
• Open-source and extensible: Source availability allows teams to inspect, modify, and extend the framework to meet their needs.
• Good fit for experimentation: Useful for prototyping new conversational patterns, testing model combinations, or building custom agents without platform constraints.

Drawbacks of Pipecat

• Requires engineering effort: Teams need to design, deploy, and maintain their own real-time pipelines rather than relying on a managed service.
• No built-in hosting or scaling: Infrastructure, monitoring, and reliability are the developer’s responsibility unless paired with external services.
• Limited out-of-the-box features: Compared to platform-level solutions, Pipecat provides fewer preconfigured tools for analytics, monitoring, or agent management.
• Learning curve for real-time systems: Working with streaming audio, concurrency, and low-latency workflows can be complex for teams new to real-time applications.

Pipecat Pricing

Pipecat is an open-source framework and is free to use. There are no licensing or usage fees associated with the framework itself.

However, teams should account for the operational costs of running Pipecat in production, including:

• Infrastructure for hosting and scaling real-time services
• Usage-based fees from third-party providers (speech recognition, LLMs, text-to-speech)
• Real-time transport or media services, if used

As a result, the total cost depends on your architecture, traffic volume, and choice of AI providers.

What to Consider: Pipecat Versus a Competitor

When comparing Pipecat to other voice and multimodal AI tools, the decision often comes down to control, complexity, and ownership.

Do you need full control over the agent loop?
Pipecat gives developers direct control over the real-time agent loop, including orchestration, streaming behavior, and provider selection.

How much setup and complexity can you handle?
Framework-level tools require more engineering effort, while platform-level alternatives offer faster setup with fewer configuration options.

Who owns infrastructure and reliability?
With Pipecat, teams manage hosting, scaling, monitoring, and reliability themselves.

How much customization do you need?
Pipecat works best when you need to customize agent behavior beyond what a managed service allows.

How quickly do you need to ship?
If speed and simplicity are priorities, a managed platform may be easier to deploy than a framework.

Pipecat Versus the Top 12 Alternatives

Framework Level

Framework-level tools give developers direct control over the real-time agent loop. They expose how audio, models, and responses are orchestrated, allowing teams to build custom voice or multimodal agents without relying on a fully managed service.

Pipecat vs. Vision Agents

Vision AI Agents by Stream is a framework-level solution for building real-time, multimodal AI agents that can operate inside live audio and video experiences. It’s designed for developers who want direct control over agent behavior while supporting voice, video, and vision-based interactions.

Both Vision Agents and Pipecat give developers ownership of the real-time agent loop rather than abstracting it behind a managed service. However, Pipecat is primarily voice-focused, while Vision Agents are built to support multimodal, real-time experiences from the ground up.

✅ Pipecat Advantages:

• Pipecat is an open-source Python framework with explicit, pipeline-based orchestration for real-time voice agents.
• It places a strong emphasis on conversational control and real-time voice interactions.
• It supports provider-agnostic integration with speech recognition, language models, and text-to-speech services.
• The framework is well-suited for voice-first agents and experimentation with conversational flows.

☑️ Vision Agents Advantages:

• Vision Agents provides framework-level control with native support for voice, video, and vision models.
• It’s built on Stream’s real-time infrastructure for live audio and video interactions.
• It’s designed for multimodal agents that can see, hear, and respond in real time.
• The framework integrates naturally with Stream’s broader Video and Audio APIs.

💲 Stream Vision Agents Pricing

Stream Vision Agents itself is free to use. There are no licensing fees for the framework.

Costs depend on the underlying Stream real-time products used to run agents in production, such as Video or Audio, and are billed based on usage. Stream offers usage-based pricing with $100 in free monthly credits, making it easy to prototype and scale without an upfront commitment.

Pipecat vs. LiveKit Agents

LiveKit Agents is a framework-level solution for building real-time voice and AI-driven interactions on top of LiveKit’s open-source WebRTC infrastructure. It’s designed for developers who want to embed agents directly into live audio or video rooms with tight control over media routing and session state.

While Pipecat is optimized for voice-first conversational pipelines, LiveKit Agents are tightly integrated with live rooms, participants, and media streams.

✅ Pipecat Advantages:

• Pipecat is optimized for standalone, voice-first conversational agents rather than room-based interactions.
• Agent logic is decoupled from a specific real-time media provider, offering greater transport flexibility.
• Its pipeline-based design makes speech-to-response flows easier to reason about and customize.
• Pipecat works well for lightweight agent deployments that don’t require video or multi-participant context.

☑️ LiveKit Agents Advantages:

• Agents run as native participants inside LiveKit rooms with access to real-time room and participant state.
• Built directly on LiveKit’s WebRTC stack for low-latency audio and video delivery.
• Supports multi-user and group interactions where an agent responds to multiple participants.
• Integrates closely with LiveKit’s open-source real-time media ecosystem.

💲 LiveKit Agents Pricing:

LiveKit Agents are open source and free to use. Costs depend on how LiveKit is deployed, whether self-hosted or via LiveKit Cloud, with pricing based on real-time usage such as minutes of audio and video. Agent sessions start at $0.01/ per minute.

Pipecat vs. Rasa Voice

Rasa Voice extends the Rasa conversational AI framework to support voice interactions, enabling teams to add speech input and output to assistants built around intent classification, dialogue management, and state machines.

Both Pipecat and Rasa Voice can be used to build conversational agents, but they differ in approach: Pipecat is designed for real-time, streaming voice agents, while Rasa Voice builds on text-first conversational logic and dialogue flows.

✅ Pipecat Advantages

• Pipecat is designed around real-time audio streaming rather than turn-based, text-first conversations.
• Its pipeline model is better suited for low-latency voice interactions and interruption handling.
• Pipecat does not require intent schemas or predefined dialogue graphs to function.
• The framework is optimized for natural, continuous voice conversations instead of command-style interactions.

☑️ Rasa Voice Advantages

• Rasa provides a mature dialogue management system with explicit state tracking and conversation rules.
• It is well-suited for assistants that rely on structured intents, forms, and deterministic flows.
• Rasa offers extensive tooling for training, testing, and evaluating conversational models.
• The platform supports both self-hosted and enterprise deployments with strong control over data and models.

💲 Rasa Voice Pricing

Rasa Voice is built on Rasa Open Source, which is free to use. Enterprise features, including advanced tooling and support, are available through Rasa’s paid plans. Teams must contact sales for a quote.

Pipecat vs. Agora Conversational AI

Agora Conversational AI provides SDKs and tooling for building real-time voice interactions on top of Agora’s global real-time engagement network, with a strong focus on low-latency audio delivery at scale.

Both Pipecat and Agora Conversational AI operate at the framework level, but they emphasize different layers of the stack. Pipecat focuses on agent orchestration and conversational pipelines, while Agora centers on real-time voice transport and global media infrastructure.

✅ Pipecat Advantages

• Pipecat provides a higher-level framework for orchestrating full conversational pipelines rather than focusing primarily on media transport.
• Agent logic is independent of a specific real-time network provider, offering greater architectural flexibility.
• The pipeline-based design makes it easier to customize how speech, language models, and responses interact.
• Pipecat is well-suited for teams prioritizing conversational behavior over global media distribution concerns.

☑️ Agora Conversational AI Advantages

• Built on Agora’s global real-time engagement network optimized for low-latency voice delivery.
• Strong support for large-scale, geographically distributed voice applications.
• Tight integration with Agora’s audio infrastructure and SDKs.
• Well-suited for voice experiences where network performance and reach are primary requirements.

💲 Agora Conversational AI Pricing

Agora Conversational AI pricing depends on usage of Agora’s real-time voice infrastructure and is billed based on factors such as audio minutes and regional distribution. Pricing details are available through Agora’s usage-based plans.

• Conversational AI Engine Audio Basic Task: ~$0.0099 per minute
• Adaptive Recognition Engine for Speech (ARES): ~ $0.0166 per minute when enabled
• First 300 minutes are free each month.

Pipecat vs. Vocode

Vocode is an open-source framework for building voice AI applications, with a strong focus on telephony and phone-based voice agents that integrate speech recognition, language models, and text-to-speech.

While Pipecat is designed for general-purpose, real-time conversational pipelines, Vocode is more opinionated toward phone and call-based voice workflows.

✅ Pipecat Advantages

• Pipecat is designed for a broader range of real-time voice and multimodal agent use cases beyond telephony.
• Its pipeline abstraction makes it easier to customize complex conversational flows and processing stages.
• Pipecat is transport-agnostic and not centered around a specific call or phone model.
• The framework is well-suited for experimenting with new interaction patterns and agent behaviors.

☑️ Vocode Advantages

• Vocode offers strong built-in support for telephony and phone-based voice agents.
• The framework provides pre-integrated components for common call workflows.
• It simplifies building outbound and inbound voice agents for customer support or sales use cases.
• Vocode is a good fit for teams focused specifically on voice agents over phone systems.

💲 Vocode Pricing

Vocode is open source and free to use. Operational costs depend on the telephony providers, AI services, and infrastructure used to run voice agents in production.

Platform Level

Platform-level solutions provide managed environments for running voice AI agents, handling orchestration, infrastructure, and scaling behind the scenes.

These tools prioritize faster setup and operational simplicity, trading off some flexibility and control compared to framework-level approaches.

Pipecat vs. Vapi

Vapi is a platform-level solution for building and deploying voice AI agents, offering a managed runtime that handles real-time orchestration, infrastructure, and scaling behind a hosted service.

Vapi’s main focus is abstracting the logic behind a managed platform to simplify deployment. This differs from Pipecat, which exposes the full conversational pipeline in code and gives developers direct control over how agents process input and generate responses.

✅ Pipecat Advantages

• Pipecat gives developers full visibility into and control over the real-time conversational loop.
• Agent behavior can be customized at a deeper level than what a managed platform typically allows.
• The framework is provider-agnostic and not tied to a single hosted runtime.
• Pipecat is well-suited for teams that want to own infrastructure and long-term architecture decisions.

☑️ Vapi Advantages

• Vapi offers a faster path to production by handling orchestration and infrastructure automatically.
• The platform reduces operational complexity by managing scaling and reliability.
• It is designed for teams that want to deploy voice agents without building a custom runtime.
• Vapi works well for productionizing voice agents quickly with minimal setup.

💲 Vapi Pricing

Vapi uses a usage-based pricing model that varies based on call volume and features. The base hosting cost is roughly $0.05 per minute for calls.

In addition to the core platform fee, total costs depend on telephony providers (e.g., Twilio or SIP trunks) used to connect calls / speech-to-text, language model, and text-to-speech services / and add-ons such as extra concurrent call capacity or advanced compliance features.

Pipecat vs. Retell AI

Retell AI is a platform-level solution for building and deploying voice AI agents, with a focus on phone-based conversations and managed call handling.

Compared to Pipecat’s framework-level approach, Retell AI removes much of the implementation detail around real-time pipelines. This makes it easier to launch voice agents quickly, but limits how deeply developers can customize conversational behavior or underlying architecture.

✅ Pipecat Advantages

• Pipecat gives developers full control over the real-time agent loop and conversational logic.
• Agent behavior can be customized beyond predefined call flows or platform constraints.
• The framework is provider-agnostic and can be adapted to different transports and use cases.
• Pipecat is better suited for teams that want to own their voice agent architecture.

☑️ Retell AI Advantages

• Retell AI provides built-in support for phone-based voice agents and telephony workflows.
• The platform reduces operational overhead by managing infrastructure and scaling.
• It offers tools designed specifically for inbound and outbound voice calls.
• Retell AI works well for teams focused on deploying voice agents quickly for call-centric use cases.

💲 Retell AI Pricing

Retell AI uses a usage-based pricing model with no fixed platform fee and options for both pay-as-you-go and enterprise plans.

• Pay-as-you-go: Voice agent usage starts at about $0.07+ per minute
• Free trial credits: $10 of initial free credits to explore and test voice agents
• Enterprise plans: Custom pricing for larger deployments

Pipecat vs. Ultravox

Ultravox is a platform-level solution focused on delivering low-latency, speech-to-speech voice AI through a hosted runtime. It emphasizes real-time responsiveness and simplified deployment for teams building production voice agents without managing the full orchestration stack.

It differs from Pipecat in its focus on providing a fully managed, speech-to-speech runtime rather than a developer-defined orchestration framework. Ultravox prioritizes performance and ease of use by handling much of the underlying complexity internally.

✅ Pipecat Advantages

• Pipecat gives developers direct control over agent orchestration and conversational logic.
• The framework supports deeper customization of pipelines and interaction patterns.
• Pipecat is provider-agnostic and not tied to a single hosted runtime.
• It is well-suited for teams experimenting with custom or evolving agent architectures.

☑️ Ultravox Advantages

• Ultravox is optimized for low-latency, real-time speech-to-speech interactions.
• The platform abstracts infrastructure and orchestration to reduce setup complexity.
• It provides a streamlined path to deploying production-ready voice agents.
• Ultravox works well for teams prioritizing responsiveness over customization.

💲 Ultravox Pricing

Ultravox offers a tiered, usage-based pricing structure:

• Pay As You Go: First 30 minutes free, then $0.05 per minute for real-time voice interactions
• Pro: $100/month. Includes everything in the pay as you go plan, plus 5 custom voices and 20 corpora for RAG
• Enterprise: Custom pricing

Pipecat vs. Bland AI

Bland AI is a platform-level solution focused on deploying voice agents for phone-based use cases such as outbound calling, inbound support, and sales automation. It emphasizes rapid setup and operational simplicity by packaging telephony, orchestration, and runtime management into a single service.

Compared to Pipecat’s framework-level approach, Bland AI trades flexibility for speed. Pipecat exposes the full conversational pipeline and allows developers to define how agents process speech and generate responses, while Bland AI abstracts most of that logic to enable faster deployment with fewer configuration steps.

✅ Pipecat Advantages

• Pipecat is designed for building custom conversational agents rather than configuring predefined call automation workflows.
• It supports voice interactions beyond phone calls, like app-embedded and real-time web experiences.
• Pipecat makes it easier to model complex or non-linear conversations that don’t fit standard inbound or outbound call flows.
• It is better suited for teams building bespoke or evolving voice agent architectures.

☑️ Bland AI Advantages

• Bland AI is designed for fast deployment of phone-based voice agents, with workflows optimized for inbound and outbound calling use cases.
• The platform handles telephony, scaling, and runtime management automatically.
• It minimizes the engineering effort required to launch voice agents by abstracting real-time orchestration and call handling behind a managed service.
• Bland AI works well for teams prioritizing speed to production over deep customization.

💲 Bland AI Pricing

Bland AI does not publicly list pricing. Costs are usage-based and depend on factors such as call volume, call duration, and enabled features.

Low-Level Real-Time APIs

Low-level real-time APIs provide the building blocks for voice and multimodal AI, such as streaming speech, audio input and output, and real-time model inference. Unlike frameworks or platforms, these APIs don’t manage agent behavior or conversational flow, leaving orchestration and state management to the developer.

Pipecat vs. OpenAI Realtime API

OpenAI Realtime API provides low-level, streaming access to speech-to-speech and multimodal model interactions, allowing developers to send and receive audio and text in real time directly from OpenAI’s models.

It differs from Pipecat in abstraction level. Pipecat is a framework for orchestrating full conversational pipelines across multiple providers, while the OpenAI Realtime API supplies core real-time model primitives that developers must assemble into an agent themselves.

✅ Pipecat Advantages

• Pipecat keeps the conversational session and agent lifecycle in your application, rather than inside a provider-managed real-time session.
• It allows you to design agent behavior independently of OpenAI-specific tools, message formats, or session semantics.
• It makes it easier to evolve your architecture over time, such as swapping real-time model providers without rewriting the interaction layer.
• Pipecat is better suited for building complex or multi-provider voice agent architectures.

☑️ OpenAI Realtime API Advantages

• Provides direct, low-latency access to OpenAI’s real-time voice and multimodal models.
• Eliminates the need to manage model hosting or inference infrastructure.
• Enables speech-to-speech interactions with minimal setup.
• Works well as a building block within custom agent frameworks.

💲 OpenAI Realtime API Pricing

OpenAI Realtime API uses usage-based pricing, with costs based on real-time audio and text usage.

• Text (gpt-realtime): $4.00 per 1M input tokens and $16.00 per 1M output tokens
• Audio (gpt-realtime): $32.00 per 1M input tokens and $64.00 per 1M output tokens
• Image (gpt-realtime): $5.00 per 1M input tokens

Pipecat vs. Google Gemini Live

Google Gemini Live provides real-time, streaming access to Google’s multimodal models, enabling voice and multimodal interactions through managed APIs that handle model inference and scaling.

While Pipecat is a framework for orchestrating full conversational pipelines across services, Gemini Live focuses on delivering real-time model capabilities that developers integrate as part of a broader agent stack.

✅ Pipecat Advantages

• Pipecat makes it easier to insert custom processing steps (such as VAD, guardrails, or business logic) before and after model calls.
• It keeps your agent loop and transports under your control, which is useful when you need the agent to run inside your own app sessions.
• The framework exposes agent behavior and pipeline logic directly in code rather than behind a managed API.
• Pipecat is better suited for building custom, multi-component voice agent systems.

☑️ Google Gemini Live Advantages

• Provides native access to Google’s real-time, multimodal models.
• Handles model hosting, scaling, and inference automatically.
• Supports voice and multimodal interactions with minimal infrastructure setup.
• Works well as a foundational API for teams building on Google’s AI platform.

💲 Google Gemini Live Pricing

Google Gemini Live uses usage-based pricing tied to model inference and real-time interaction volume. Pricing varies depending on the specific Gemini model used, the modalities involved (text, audio, or multimodal), and total token usage during live sessions.

Google publicly lists pricing for all supported Gemini models in its Gemini API documentation.

Pipecat vs. Deepgram Voice Agent API

Deepgram Voice Agent API provides real-time speech recognition and voice agent primitives optimized for low-latency audio processing. It focuses on streaming speech-to-text and related voice capabilities that developers can use as part of a larger conversational system.

The difference from Pipecat is the focus on individual speech capabilities. Pipecat is a framework that orchestrates complete conversational pipelines. Deepgram Voice Agent API, on the other hand, supplies specialized voice components that must be combined with other services to form a full agent.

✅ Pipecat Advantages

• Pipecat manages the full conversational pipeline rather than individual voice components.
• The framework supports coordinating multiple providers across speech, language models, and responses.
• It handles agent-level concerns such as flow control and interaction timing.
• Pipecat is better suited for building end-to-end voice or multimodal agents.

☑️ Deepgram Voice Agent API Advantages

• Deepgram is optimized for fast, accurate, real-time speech recognition.
• The API is designed for streaming use cases with low-latency requirements.
• It integrates easily into custom agent stacks as a speech layer.
• Deepgram works well for teams that want best-in-class ASR without building their own models.

💲 Deepgram Voice Agent API Pricing

Deepgram’s Voice Agent API uses per-minute, usage-based pricing. Public rates include:

• Standard: $0.08/min (Pay As You Go), $0.07/min (Growth)
• Standard with BYO TTS: $0.06/min (Pay As You Go), $0.05/min (Growth)
• Custom with BYO LLM + TTS: $0.05/min (Pay As You Go), $0.04/min (Growth)

Alternatives Comparison Chart

Is Pipecat Right For You?

Pipecat is a great fit for teams that want framework-level control over real-time voice or multimodal agent behavior. It works well when you need to define conversational pipelines in code, integrate multiple AI providers, or customize how agents handle streaming input and responses.

That level of control comes with more responsibility. Teams using Pipecat manage their own infrastructure, scaling, and operational complexity. For some use cases, this flexibility is essential.

If you want similar control with a different focus (such as multimodal experiences, live audio/video integration, or vision-based agents), other framework-level alternatives may be a better fit. Platform-level solutions can reduce setup and operational overhead if speed to production is the priority.

Many teams experiment with multiple approaches before committing. Frameworks like Vision AI Agents are free and open source, making it easy to prototype real-time, multimodal agents and evaluate tradeoffs before choosing a long-term solution.