AI voice agents are software that answers and makes phone calls for a business, holding natural conversations with callers. They can handle routine requests like booking appointments or updating customer records while the caller is still on the line, which frees your team to focus on calls that need a person.
In this article, we review 15 of the best AI voice agents for business in 2026, covering each platform’s features and pricing. We also explain how monday agents in the monday AI Workspace help teams act on call outcomes after the conversation ends.
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Key takeaways
- AI voice agents hold natural phone conversations with callers and resolve routine requests in real time.
- Informational agents answer common questions, while transactional agents complete tasks like bookings during the call.
- Testing a platform on your own calls tells you more than any vendor demo.
- monday agents help teams act on call outcomes by qualifying leads and routing support tickets in the monday AI Workspace.
What are AI voice agents?
AI voice agents are AI systems that hold spoken conversations with callers and resolve their requests in real time. Unlike traditional phone menus, they understand natural speech, so callers can explain what they need in their own words.
Each call runs on two layers working together. Speech recognition and voice synthesis handle the conversation itself, while a large language model works out what the caller wants and decides how to respond. Most AI voice agents also connect to business systems such as CRMs and scheduling tools, so they can look up account details or book an appointment during the call.
AI voice agents work alongside human teams, taking on high-volume, routine questions and passing complex or sensitive calls to a person with a summary of the conversation. Guidelines you define keep each agent on brand and within your processes.
How voice AI differs from interactive voice response and chatbots
Customer support tech has come in three waves, and each one handles the work differently. Side by side, it’s easy to see where older systems create friction and voice agents take admin off your plate.
Informational vs. transactional agents
The comparison above shows AI voice agents can complete workflows and write data back to business systems. How much a specific agent can do depends on its type, and most fall into one of two.
Informational agents answer common questions using approved content from your knowledge base, such as opening hours or order status. Transactional agents go a step further and complete the task during the call, whether this means booking an appointment or taking a payment.
15 best AI voice agent platforms for business
AI voice agent platforms are built for different jobs. Some focus on outbound sales calls or contact center volume, while others give developers the infrastructure to build custom voice applications.
We reviewed 15 of the leading AI voice agents to help you find the right fit for your team and technical resources. Each review covers the platform’s use case, key features, pricing, and considerations.
1. monday agents
Best for: Organizations that want AI agents connected directly to everyday workflows, projects, and cross-team data.
monday agents are AI agents built into the monday AI Workspace. They work on the same boards and workflows your teams already use to manage projects and customer records. For teams comparing AI voice agents, their role starts once a conversation ends: an agent can take a call summary, update the matching customer record and assign follow-up tasks to the right owner.
Because the agents share context across departments, a sales call can trigger next steps for the delivery team, or a support call can flag an at-risk account to its account manager. Your team sets the permissions and approval rules, so agents handle the follow-up while people keep control of decisions.
Use case:
Revenue teams, service leaders, operations managers, and recruiting teams that need voice interactions to trigger assigned workflows, updated records, and cross-department follow-up without manual handoffs.
Common examples include:
- a sales call that should generate a summary, assign next steps, and update lead priority
- a support conversation that should classify the request, set urgency, and route it to the right owner
- a recruiting reference call that should capture feedback and centralize candidate scoring
- a project check-in that should surface delivery risk and notify stakeholders right away
Key features:
monday agents work inside the monday AI Workspace, on the same boards your teams use to manage tickets and leads.
- Ticket triage: The Ticket Management Agent classifies and routes incoming support tickets, then sets SLAs and resolves common issues using your knowledge base.
- Lead qualification: The Lead Qualifier scores each new lead out of 100 using web data like company size and funding, then sends top prospects to the right salesperson.
- Customer feedback analysis: The Voice of Customer agent finds recurring themes in customer feedback and ranks pain points by business impact.
- Support escalation: The Support Signal Agent monitors tickets for a product area and escalates critical issues, such as churn language, as soon as they appear.
- Project risk detection: The Dependency and Risk Mapper traces dependency chains across a board and flags at-risk work with suggested next steps.
- Custom agents: Teams can start from ready-made agents or build their own for workflows the templates don’t cover.
- Guardrails and oversight: Teams set permissions and keep human approval in place for sensitive actions.
Pricing:
- Starting price: AI agent capabilities are available on monday.com plans starting at $12/seat/month (billed annually); some agent features continue to roll out across specific products and editions
- AI features operate on a transparent credit-based model
- Enterprise pricing is available for high-volume deployments
- View full pricing details on monday.com
Why it stands out:
If your team already lives in the monday AI Workspace, monday agents gives AI voice agents somewhere to act with real context. It cuts handoffs, speeds up response times, and keeps everything within your rules.
- Work continues after the call: Instead of stopping at notes, monday agents can update pipelines, create items, assign owners, trigger follow-up sequences, and notify the right team members.
- People and agents work as one team: You set the direction, approval rules, and boundaries. AI agents handle the repetitive execution around the clock, which helps teams scale output without adding manual coordination.
- Context reaches across departments: A voice-driven workflow can connect sales activity, service requests, project status, and recruiting processes on the same platform, so the next step reflects the full picture.
- Trust is built into the experience: Permissions, transparency into agent actions, audit trails, and enterprise-grade security controls help teams adopt AI with confidence, especially in regulated or high-accountability environments.
- Adoption feels natural: Because agents operate where team members already manage work, teams can start with ready-made agents, tailor them to their processes, and expand usage without introducing a separate AI system.
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2. Retell AI
Retell AI is built for engineering teams that need a voice platform ready for real traffic, not just demos. It specializes in low-latency voice orchestration and gives you fine control over conversation design, voice behavior, and integrations.
Use case:
Engineering teams building custom voice applications who need precise control over conversation flow, voice synthesis, and backend integrations at production scale.
Key features:
- Sub-second voice orchestration: Retell’s proprietary voice engine targets end-to-end latency under 1.5 seconds, with full-duplex conversations, natural turn-taking, interruption handling, and backchanneling that make interactions feel human rather than scripted.
- Flexible conversation design: Teams can build Single-Prompt, Multi-Prompt, or Conversation Flow agents, with “Flex Mode” for switching between rigid and adaptive behavior, plus reusable Flow Components for consistent agent logic across deployments.
- Built-in testing and quality assurance: Simulation and batch testing suites, AI QA with weighted scorecards, and per-call analytics give teams the visibility to measure latency, hallucination rates, and tool accuracy before and after going live.
Pricing:
- Pay-as-you-go: Starts free with $10 in credits; voice agents priced at approximately $0.07 – $0.31/minute depending on model, voice provider, and add-ons selected
- Enterprise: Custom pricing with volume discounts, higher concurrency limits, RBAC/SSO, dedicated support, and custom legal terms
- Add-ons include Knowledge Base, Advanced Denoising, Safety Guardrails, PII Removal, and AI Quality Assurance, each billed separately
- HIPAA BAA is available self-serve on any plan; enterprise unlocks uncapped concurrency and additional compliance terms
Considerations:
- Retell AI requires dedicated engineering resources to implement, configure, and maintain — teams without in-house technical capacity may find the platform’s flexibility more of a challenge than an advantage.
- Some advanced features, such as agentic warm transfer, are only available on specific agent types, which can limit flexibility depending on how an organization has structured its voice workflows.
3. ElevenLabs
ElevenLabs focuses on making voice AI sound convincingly human, giving organizations the audio quality needed to keep customer interactions natural and on-brand. The company serves enterprise teams in telecom, healthcare, retail, and financial services that need voice agents ready for production.
Use case:
Organizations that need enterprise-ready AI voice agents with natural-sounding speech, omnichannel deployment, and strong compliance controls across regulated industries.
Key features:
- Ultra-low latency voice synthesis: The v4 Turbo model targets approximately 100 ms median inference latency, keeping conversations responsive and natural without the awkward pauses that erode caller trust.
- Omnichannel agent deployment: Deploy the same agent across phone, web, WhatsApp, and SMS using native connectors for Twilio, Genesys, Amazon Connect, and SIP trunking, so teams reach customers wherever they are without rebuilding logic for each channel.
- Enterprise-grade compliance and data governance: SOC 2 Type II, ISO 27001/27701/42001, and PCI DSS Level 1 certifications, alongside HIPAA eligibility with BAA support and regional data residency across the US, EU, India, and Singapore, give compliance and legal teams the controls they need to approve deployment.
Pricing:
- Free: $0/month, includes basic access and a limited number of call minutes
- Starter: $6/month (first month at $1), billed monthly
- Creator: $22/month (first month at $11), billed monthly
- Pro: $99/month, billed monthly
- Scale: $299/month, billed monthly, includes 3 seats
- Business: $990/month, billed monthly, includes 10 seats
- Enterprise: custom pricing for high-volume deployments with SSO, SLAs, HIPAA BAAs, and dedicated support
- Additional call minutes are billed at $0.08/minute; text messages at $0.003 each
- LLM usage and external telephony provider costs (e.g., Twilio) are billed separately
- Annual billing provides two months free across eligible plans
Considerations:
- Costs can be hard to forecast at scale. Per-minute billing, separate LLM charges, and 2× burst pricing when you exceed concurrency limits all add up, so you’ll want usage monitoring in place.
- Enabling retrieval-augmented generation for knowledge grounding adds approximately 250 ms of latency, and knowledge base size limits vary by plan, which may affect teams that rely on large internal document libraries for agent responses.
4. PolyAI
PolyAI is built specifically for enterprise contact centers that manage complex, high-stakes customer conversations at volume. It is aimed at large organizations in hospitality, healthcare, and financial services, where both conversational quality and compliance requirements are critical.
Use case:
Large enterprises with high-volume contact center operations that require sophisticated conversation handling, deep system integrations, and industry-specific compliance.
Key features:
- Audio-native dialog model: PolyAI’s Dialog-RSN-1 model is built directly for live voice, not adapted from chat transcripts. It reliably processes accents, noisy environments, and choppy phone connections with ~280ms p50 response latency, keeping conversations fluid with natural turn-taking.
- Continuous self-improvement loop: Wren, PolyAI’s built-in optimization agent, reviews production calls daily, flags gaps, drafts fixes, tests them, and queues changes for human approval — keeping agents aligned with containment and CSAT targets without constant manual tuning.
- Deep enterprise integrations: With 130+ connectors spanning major CCaaS platforms (Five9, Genesys, Amazon Connect), CRMs (Salesforce, Zendesk), EHRs (Epic, Cerner), and PCI-compliant payment systems, PolyAI agents can resolve transactions end-to-end, not just answer FAQs.
Pricing:
- Usage-based pricing: Billed per minute; includes proactive performance improvements, maintenance, 24/7 support, monitoring, upgrades, and a 99.9% phone-line SLA.
- Studio access: Available to builders with a free entry point for agent creation and iteration.
- Enterprise deployments: Custom quotes based on call volume, integration requirements, and implementation scope.
- Segment-specific offers: Packaged monthly pricing is available for select verticals (for example, a restaurant partner rate of $299/month on an annual plan); multi-site pricing available on request.
Considerations:
- While PolyAI highlights a typical three-week deployment window from kickoff to launch for standard rollouts, highly customized architectures with complex legacy integrations can stretch closer to 3–6 months.
- Standard per-minute rates are not publicly listed, so precise budget modeling requires a sales conversation before committing.
5. Synthflow
Synthflow combines a visual no-code builder with enterprise-oriented telephony infrastructure. It is built for contact centers, BPOs, and operations teams that want production-ready voice automation without a long development cycle. Early versions of Synthflow leaned toward self-serve SMB use.
Use case:
Growing businesses and enterprise operations running high-volume inbound or outbound calling that want visual, no-code setup paired with strict security standards and scalable telephony controls.
Key features:
- No-code agent builder and templates: Teams can assemble voice agents visually using pre-configured industry templates, CRM integrations, and webhooks to get up and running without custom code.
- Telephony-native deployment: Supports native phone numbers, SIP trunking, and Twilio integration, complete with warm and cold call transfers, human detection, and whisper summaries for seamless handoffs to live agents.
- Structured deployment lifecycle: The BELL framework (Build, Evaluate, Launch, Learn) guides teams through simulation testing and staged rollouts so conversations run smoothly before going live.
- Real-time analytics and monitoring: Hourly-updated dashboards track call volumes, sentiment, and conversion rates, while live listen-in tools and unified transcript logs give managers complete call visibility.
Pricing:
- Enterprise contracts: Starting at $30,000 annually, with pricing tailored to call volume, concurrency requirements, telephony setup, and launch support (reflecting the platform’s move toward enterprise-led packaging over legacy self-serve models)
- Per-minute overage rates apply to usage beyond committed plan allowances, managed via the Billing Portal
- Concurrency upgrades and custom integrations are available as add-ons
Considerations:
- The sales-led $30,000 annual minimum commitment means Synthflow is no longer built for budget-conscious teams or casual experimentation.
- Key automation tools, including the visual Workflows builder and batch outbound Campaigns, are reserved for Enterprise plan agreements.
6. Bland.ai
Bland.ai gives dev teams a direct path from prototype to live voice agent, running on its own models end to end. It handles more than 1 million calls a day across regulated industries, so enterprises get reliable scale without stitching together third-party AI vendors. With SOC 2 Type II, HIPAA, GDPR, and PCI DSS v4.0 compliance built in, it is clearly aimed at organizations where governance is a hard requirement.
Use case:
Development teams and enterprise organizations that need to deploy reliable, compliant AI voice agents at high call volumes — without relying on third-party AI in the call path.
Key features:
- Full-stack proprietary models: Bland.ai runs its own STT, LLM, and TTS on dedicated infrastructure, targeting approximately 200 ms end-to-end response times and eliminating upstream vendor dependencies that can introduce latency or compliance risk.
- Enterprise-grade governance and testing: Guard Rails, Evals, and Scenarios give teams configurable policies, automated end-to-end agent testing, and canary releases on live traffic — so quality is validated before agents reach production.
- Flexible deployment options: Organizations can deploy on Bland’s managed cloud, a customer VPC, on-premises, or fully air-gapped environments, with per-call regional pinning and a single-vendor data processing agreement.
Pricing:
- Start: $0.14/min (AI talk time), no platform fee; includes 10 concurrent calls and 100 calls/day
- Build: $0.12/min + $299/month platform fee; includes 50 concurrent calls and 2,000 calls/day
- Enterprise: custom pricing with dedicated infrastructure, VPC/on-premises options, BAA, SSO, and 99.99% SLA
- Agent Phone Plan: $14.99 for the first month, then $29.99/month; covers unlimited US/Canada voice and SMS on one number
- AI minute pricing includes LLM, STT, and TTS costs with no separate token charges; telephony is billed separately
Considerations:
- Bland.ai runs exclusively on its own proprietary models, so teams that require the flexibility to select their own LLM or TTS vendor may find the architecture less adaptable than orchestration-layer alternatives.
- Start and Build tiers include per-day call caps and concurrency limits, which organizations should validate against expected call volumes before committing to a plan.
7. Sierra
Sierra is positioned for enterprises that need voice agents capable of protecting brand reputation while resolving customer issues from start to finish. It serves large organizations in regulated industries such as healthcare and financial services, as well as retail and telecom, where consistency matters in every interaction. Its Tandem Voice architecture separates fast conversational response from deeper reasoning so the agent can stay responsive without becoming shallow.
Use case:
Enterprise organizations that need brand-consistent, compliant AI voice agents capable of handling complex customer interactions across multiple languages and channels.
Key features:
- Tandem Voice architecture: Splits each interaction into a fast conversation layer and a deeper reasoning layer, enabling agents to respond in real time while working through multi-step problems—without the awkward pauses that make automated calls feel robotic.
- Voice Personas: Brand-specific voice identities that can be tested, sealed, and deployed consistently across locales, with one subscription business reporting a 50%+ resolution lift after rolling them out.
- Built-in release governance: Pre-deployment voice simulations, staged rollouts, and supervisor models reduce the risk of regressions in live customer experiences—a critical safeguard when small changes can affect millions of interactions.
Pricing:
- Enterprise pricing: Custom quotes based on deployment scope; contact Sierra directly for details.
- Pricing model: Primarily outcome-based—often tied to resolved conversations, retention events, or sales conversions, though agreements can also incorporate consumption-style metrics depending on scope.
Considerations:
- Outcome-based pricing structures require agreeing on precise metric definitions and tracking upfront, which can add complexity to procurement before deployment begins.
- Language coverage figures vary across Sierra’s product pages, so prospective customers should confirm current voice language availability for their specific channel mix before committing.
8. Cognigy
Cognigy brings together enterprise AI voice agents, agentic reasoning, and carrier-grade telephony infrastructure for high-scale inbound and outbound interactions. The platform is aimed at large contact centers in regulated industries and takes a vendor-neutral approach, integrating into existing CCaaS and telephony stacks rather than forcing a full replacement.
Use case:
Large enterprises that need unified AI voice agents across contact center environments, with strict compliance requirements and high call volumes that demand resilience, multilingual support, and seamless human handoff.
Key features:
- Carrier-grade Voice Gateway: Handles inbound and outbound call automation with native barge-in, continuous speech recognition, DTMF support, answering machine detection, and warm handoffs that transfer full conversation context to live agents.
- Composite AI for governed conversations: Blends LLM reasoning with deterministic workflow controls, so organizations maintain oversight and consistency even when AI handles complex, open-ended interactions.
- Vendor-neutral model orchestration: Connects to leading speech and language providers — including Deepgram, OpenAI, Anthropic, Google, and AWS — with automatic failover between providers to protect uptime and manage cost.
Pricing:
- Quote-based pricing: Cognigy does not publish public pricing tiers; all plans are scoped based on concurrent Voice Gateway sessions, usage volume, and deployment model.
- Knowledge AI requires a separate license with usage-based quotas billed per query.
- Support is available across Standard, Extended, and Premium tiers, with costs varying by service level.
Considerations:
- The platform’s depth means teams should expect an enablement investment; Cognigy itself notes it has “countless bells and whistles” and is “not simple to master” for sophisticated deployments.
- Some operational features, including the AI Ops Center, are available only on SaaS deployments, which may limit visibility for organizations running fully on-premises environments.
9. Vapi
Vapi provides voice AI infrastructure as a service, giving technical teams a production-ready base for custom voice applications without requiring them to manage telephony from the ground up. It is best suited for builders who want provider-agnostic orchestration across speech recognition, LLMs, and text-to-speech, all optimized for sub-500 ms response times.
Use case:
Technical teams building custom voice AI applications who need reliable, provider-agnostic infrastructure without managing telephony, transcription, or synthesis directly.
Key features:
- Provider-agnostic orchestration: Vapi connects to dozens of speech-to-text, LLM, and text-to-speech providers, letting teams bring their own API keys and swap components without rebuilding their stack — keeping costs at pass-through rates.
- Production-grade observability: Built-in simulations, evaluations, monitoring dashboards, and A/B traffic splitting give engineering teams the visibility to test, measure, and improve conversational performance continuously.
- Flexible telephony management: Handles phone number provisioning, call routing, inbound and outbound calling, warm transfers, and voicemail — so teams can focus on business logic rather than carrier infrastructure.
Pricing:
- Free: $0 to start, includes $5 credits, one free Vapi number, four concurrent calls, and 14-day data retention
- Core: $29/month, includes five Vapi numbers, 10 concurrent calls, and 30-day data retention
- Telephony transport fees are charged by the telephony provider, not Vapi
