Best Voice AI for Developers (2026)

For engineering teams needing custom pipeline control, LiveKit Agents wins decisively. It is fully API-first with no no-code builder, signaling it is designed for developers who wire pipelines themselves. It passes all LLM and TTS costs through at cost and lets teams bring their own LLM and voice, giving full stack control. Vapi also supports BYO-LLM but bundles a no-code builder and abstracts pipeline decisions away from the engineer. LiveKit's SOC 2 Type II certification adds enterprise credibility that Vapi lacks. The base concurrency of 5 versus Vapi's 10 is a minor trade-off that does not offset the pipeline control advantage.

For engineering teams needing custom pipeline control, LiveKit Agents is API-first and passes through LLM and TTS costs at cost, giving engineers direct control over every component without markup or abstraction layers. Retell AI marks up LLM and TTS costs rather than passing them through, limiting transparency and control. LiveKit Agents has no no-code builder, reinforcing its code-first, engineer-oriented design. The real production cost floor is lower for LiveKit at 0.0435 USD/min versus 0.085 USD/min for Retell, meaning custom pipelines stay cheaper at scale.

For engineering teams needing custom pipeline control, LiveKit Agents supports bring-your-own LLM and passes through LLM and TTS costs at cost, giving engineers full control over model selection and cost transparency. Bland AI bundles these costs with no passthrough, limiting pipeline customization. Both are API-first, but LiveKit's open framework design suits teams building bespoke pipelines more directly. The margin is narrow because Bland AI is also API-first and highly capable, but LiveKit wins on composability.

For engineering teams needing custom pipeline control, Vapi's API-first architecture, native SIP trunking, DTMF/IVR support, bring-your-own LLM, bring-your-own TTS, and LLM/TTS cost passthrough at cost give it a more complete low-level control surface. Vapi also offers 10 base concurrent calls versus Voiceflow's 5, and a $0/mo platform fee versus Voiceflow's estimated $60/mo, lowering the barrier to custom builds. Both platforms are API-first, but Vapi's native SIP trunking and richer telephony integrations, including Twilio and Telnyx, more directly support pipeline customization for engineering teams.

For engineering teams needing custom pipeline control, Vapi wins on several developer-facing dimensions. Vapi supports bring-your-own LLM while Ultravox does not (9576ca60-2b27-4467-b17e-ec0d4fee0e2a), which is critical for custom pipelines. Vapi also includes a simulation and testing suite versus Ultravox which does not, enabling pipeline validation. Both are API-first, but Vapi offers broader native integrations including Twilio, Zapier, and Make and a higher base concurrency of 10 calls versus Ultravox's 5 (3eb460c0 vs f004cce1). BYO LLM alone is decisive for custom pipeline engineering use cases.

For engineering teams needing custom pipeline control, Vapi is clearly superior. It is API-first, meaning the full agent lifecycle can be managed programmatically, while Thoughtly explicitly is not API-first. Vapi also offers self-serve signup with a $0/mo platform fee, lowering friction for engineering experimentation. LLM and TTS costs are passed through at cost, giving teams full transparency and control over the pipeline stack. These facts decisively favor Vapi for a developer-driven custom pipeline use case.

For engineering teams needing custom pipeline control, Vapi wins on multiple technical dimensions. It supports bring-your-own LLM (versus Ringly.io, which does not), bring-your-own TTS voice, and LLM/TTS cost passthrough at cost, giving engineers full model flexibility. Vapi also offers 10 concurrent calls on its base plan versus Ringly.io's 1 line, a no-code plus API-first builder, DTMF/IVR navigation, and integrations with GHL, Twilio, Telnyx, Zapier, and Make, compared to Ringly.io's narrower helpdesk-focused set. The $0/mo platform fee versus $349/mo also removes friction for iterative engineering workflows.

For engineering teams needing custom pipeline control, Vapi edges ahead on two key dimensions. First, Vapi passes LLM and TTS costs through at cost and supports bring-your-own LLM, giving engineers direct control over every component in the inference pipeline. Second, Vapi's production cost range of 0.05-0.17 USD/min is significantly lower than Phonely's 0.2-0.35 USD/min, reducing friction for teams running high-volume custom workflows. Both platforms are API-first, but Vapi's white-label and partial sub-account support also aids multi-tenant pipeline setups.

Both tools are API-first with BYO-LLM and BYO-voice support, so core pipeline flexibility is equal. Vapi edges ahead for engineering teams through its simulation and testing suite, broader native integrations including GHL, Twilio, Telnyx, Zapier, Make, and Slack, and a $50M Series B suggesting deeper engineering investment. Millis AI lacks a documented testing suite and has fewer integration touchpoints. These advantages matter for teams building and validating complex custom pipelines.

Both tools are API-first (facts aa673710 and 96c19528), but Vapi offers bring-your-own LLM, bring-your-own TTS voice, and LLM/TTS cost passthrough, giving engineering teams direct control over every pipeline component. Vapi also includes a simulation and testing suite and native SIP trunking, both of which matter for custom pipeline construction. Dasha bundles LLM and TTS without passthrough, limiting component-level substitution. Vapi's higher base concurrency of 10 calls versus Dasha's 1 further supports pipeline scale testing.

Both tools are API-first and support BYO-LLM and BYO-voice, but Vapi passes LLM and TTS costs through at cost while Retell bundles them into a fixed rate, giving engineering teams more granular cost control and flexibility. Vapi also has a lower real production cost ceiling at 0.05-0.17 USD/min versus Retell's 0.085-0.415 USD/min, meaning custom pipelines with variable model usage stay cheaper. The $50M Series B signals stronger infrastructure investment supporting custom engineering workflows.

Both tools are API-first, but ElevenLabs Agents adds a no-code agent builder while Ultravox does not, giving engineering teams more flexibility to prototype alongside custom pipeline work. ElevenLabs also reports a 75 ms median e2e latency, which matters for real-time pipeline control. Ultravox lacks barge-in support and a testing suite, limiting pipeline iteration options. ElevenLabs also supports 70 languages vs Ultravox's 26, broadening pipeline applicability. The margin is narrow because Ultravox offers native SIP trunking and BYO TTS, which some engineering pipelines need.

ElevenLabs Agents is API-first, giving engineering teams full programmatic control over the agent lifecycle, while Thoughtly is explicitly not API-first. For custom pipeline control, that programmatic access at every stage is essential. ElevenLabs also offers self-serve signup, letting engineers onboard and iterate immediately without a sales call, whereas Thoughtly requires one. These two factors decisively favor ElevenLabs Agents for engineering-led custom pipeline use cases.

Engineering teams building custom pipelines need API-first access, low latency, bring-your-own-LLM flexibility, and low iteration cost. ElevenLabs Agents is API-first, supports BYO-LLM, delivers 75 ms median end-to-end latency, and charges a $0/mo platform fee. Ringly.io charges $349/mo, explicitly does not support BYO-LLM, and offers no API advantage over ElevenLabs for custom pipeline work. The combination of zero platform fee and BYO-LLM support makes ElevenLabs decisively better for engineering-driven custom pipelines.

Both tools offer API-first access, but ElevenLabs Agents has a 75 ms median e2e latency which is critical for engineering teams building custom pipelines where responsiveness matters. ElevenLabs also supports 70 languages vs Phonely's 50 (fact 7340f19b vs 55d0ac30), giving more headroom for custom integrations. Both are API-first, so this is not a differentiator there, but ElevenLabs's latency advantage and broader language support tip the balance for engineering-focused pipeline control.

Both tools offer API-first design and no-code builders. ElevenLabs Agents has a median latency of 75 ms versus Millis AI at 600 ms, a difference that matters significantly for engineering teams building tightly controlled pipelines where timing and responsiveness affect pipeline logic. ElevenLabs also carries a Series D funding signal suggesting stronger infrastructure investment. Millis AI offers BYO-LLM and BYO-voice flexibility, which is relevant for custom pipelines, but the 8x latency disadvantage is a concrete engineering constraint that tips the decision.

Both tools are API-first, support BYO LLM and BYO voice, and offer native SIP trunking, making them closely matched for engineering pipeline control. However, Vocode has a $0.00/min base price versus Vapi at $0.05/min rising to $0.17/min real cost, which lowers friction for experimentation in custom pipelines. Vocode also lacks a no-code builder, signaling a purely code-first orientation that aligns with engineering teams building custom pipelines rather than a hybrid product.

For engineering teams needing custom pipeline control, code-level flexibility and open architecture matter most. Vocode has a $0.00/min base price (estimated), making it cost-free to experiment, and its API-first design matches developer workflows. Critically, Vocode lacks a no-code builder, which signals orientation toward code-driven customization rather than abstracted interfaces, giving engineers direct pipeline control. Retell AI has a no-code builder and bundles more managed abstractions, which can limit low-level pipeline access. Both are API-first, but Vocode's absence of a no-code layer and zero base cost lean it toward engineering-first custom pipeline use.

For engineering teams needing custom pipeline control, the key differentiator is how much the platform stays out of the way. Vocode supports bring-your-own LLM, giving engineers full control over the inference pipeline, and its $0/min base price makes it practical to absorb custom infrastructure costs. Bland AI does not surface BYO-LLM support in the facts, which limits pipeline customization. Both platforms are API-first, but Vocode's open flexibility at the LLM layer is the decisive edge for custom pipeline engineering work.

Both tools are API-first, but Bland AI adds native SIP trunking, a $0/mo platform fee (versus Voiceflow's estimated $60/mo), a 99.9% uptime SLA on all plans, and telephony at $0.00/min passthrough. For engineering teams building custom pipelines, Bland AI's lower cost floor and SIP trunking provide more infrastructure control. Voiceflow offers BYO LLM and a testing suite, which are engineering-friendly features, but the cost and telephony flexibility tip the balance toward Bland AI.

Both tools are API-first and self-serve, but Bland AI adds a no-code agent builder while Ultravox does not, giving engineering teams more flexibility to prototype and iterate pipelines without extra tooling. Bland AI also includes barge-in interruption handling whereas Ultravox does not, which matters for pipeline control over conversational flow. Ultravox wins on per-minute cost at $0.05 versus $0.14, but for pipeline control flexibility Bland AI edges ahead.

For engineering teams needing custom pipeline control, Bland AI is API-first, meaning the full lifecycle can be managed programmatically, while Thoughtly is explicitly not API-first. Bland AI also offers self-serve signup with a $0/mo platform fee, letting engineers iterate immediately, whereas Thoughtly requires sales contact and charges a $500/mo platform fee (facts aa3ef37d, 09f3d97a). These two factors together make Bland AI decisively better for engineering-driven custom pipeline workflows.

Bland AI offers an API-first platform with a no-code builder, native outbound campaigns, DTMF/IVR navigation, barge-in handling, bring-your-own TTS voice, and a $0/mo platform fee compared to Ringly.io's $349/mo. For engineering teams building custom pipelines, Bland AI holds a decisive edge through full API lifecycle control, 10-call base concurrency versus Ringly.io's 1 line, and no platform fee. Ringly.io lacks a no-code builder, outbound campaigns, DTMF/IVR, and barge-in support, all of which are critical for custom pipeline orchestration.

Bland AI is explicitly API-first, covering the full lifecycle with native Twilio and SIP trunk integrations, giving engineering teams granular pipeline control. Its production cost of 0.11 to 0.14 USD per minute is lower than Phonely's estimated 0.20 to 0.35 USD per minute, reducing cost risk for high-volume custom pipelines. Phonely offers an API and a testing suite, but its integration story is less specific. Bland AI's Y Combinator backing and 99.9% uptime SLA across all plans add further credibility for engineering deployments.

For engineering teams needing custom pipeline control, Retell AI offers native SIP trunking, DTMF/IVR navigation, bring-your-own LLM, bring-your-own TTS voice, and a $0/mo platform fee with no annual contract required. The $0 platform fee compares favorably to Voiceflow's $60/mo, while both platforms remain API-first. Retell's base concurrency of 20 calls versus Voiceflow's 5 provides more headroom for complex pipelines out of the box. Native SIP trunking support further enables the deep telephony pipeline customization that engineering teams typically require.

For custom pipeline control by engineering teams, Retell AI supports bring-your-own LLM, giving teams direct model control, while Ultravox does not. Retell AI also includes a simulation and testing suite, which Ultravox lacks, and this is critical for iterating on custom pipelines. Both platforms are API-first, but Retell's bring-your-own LLM support and testing tooling give engineering teams meaningfully more pipeline control.

For engineering teams needing custom pipeline control, Retell AI offers bring-your-own LLM, bring-your-own TTS voice, a full API-first lifecycle, a no-code builder alongside code access, native SIP trunking, DTMF/IVR navigation, and a $0/mo platform fee with $0.07/min base pricing. Ringly.io, by contrast, charges a $349/mo platform fee and lacks BYO-LLM support. The absence of BYO-LLM on Ringly.io and its lack of a no-code agent builder both reduce pipeline customization depth. Retell AI's extensive integration set, including Twilio and n8n, further supports custom engineering workflows.

For engineering teams needing custom pipeline control, Retell AI offers a bring-your-own LLM option, letting teams plug in their own model and fully control the inference pipeline. Both tools are API-first, but Retell also supports passthrough cost transparency and named integrations including Twilio, Vonage, Make, and n8n, giving engineers more low-level plumbing options. Phonely does not list a bring-your-own LLM capability, which is a meaningful gap for teams wanting full control over the AI backbone.

Both tools are API-first and support BYO LLM and BYO TTS voice, so core pipeline flexibility is comparable. Retell AI edges ahead for engineering teams because it includes a simulation and testing suite for agents, which is critical for validating custom pipelines before production. Retell also integrates natively with a broader set of developer-relevant connectors, including Twilio, Vonage, Go High Level, n8n, and HubSpot, versus Millis AI's smaller integration list. The testing suite alone is a meaningful differentiator for engineering-focused pipeline work.

Both tools are API-first and support full lifecycle control. Retell AI adds concrete engineering capabilities on top: a simulation and testing suite, bring-your-own LLM, bring-your-own TTS voice, native SIP trunking, and a base concurrency of 20 calls versus Dasha's 1 call. For teams building custom pipelines, the higher base concurrency (20 vs 1) and the testing suite provide materially more pipeline control without negotiating upgrades. Dasha also charges $0.08 extra per minute for telephony versus Retell's $0.01, adding meaningful cost friction at scale.

Both tools are API-first and support full lifecycle management via API. However, Dasha has no no-code abstraction layer listed and is positioned as a developer-centric platform with API-first architecture and self-serve signup, making it more suitable for engineering teams building custom pipelines. ElevenLabs Agents also offers API-first capability, but its no-code builder suggests a broader, less pipeline-focused audience. Dasha's bundled pricing with no LLM/TTS passthrough gives engineering teams predictable cost control. The edge is narrow because both share API-first support (facts aa673710, b0b135f2).

Both tools are API-first and support full lifecycle control via API. However, Dasha's real production cost is a flat $0.08/min versus Bland AI's $0.11-0.14/min range, meaning engineering teams running high-volume custom pipelines pay materially less with Dasha. Dasha also charges $0/mo for extra concurrent lines beyond its base, removing a scaling constraint that matters when teams spin up parallel pipeline branches. Bland AI's 10-call base concurrency is higher out of the box, but Dasha's zero marginal concurrency cost better fits programmable, burst-heavy custom pipelines.

For engineering teams needing custom pipeline control, API-first access is decisive. Retell AI is explicitly API-first, while Thoughtly is not. Retell AI also carries no monthly platform fee ($0/mo) versus Thoughtly's $500/mo, lowering the barrier for custom integrations and iterative development. Retell AI further supports bring-your-own LLM and bring-your-own TTS, giving engineers fine-grained control over every pipeline component.

Both tools are API-first and support BYO voice, but Millis AI adds bring-your-own LLM support, giving engineering teams direct control over the model layer, a key requirement for custom pipelines. Millis also shows lower median latency at 600ms versus Bland AI's 1000ms, which matters when orchestrating multi-step pipelines. Millis integrates with Make.com, OpenAI, ElevenLabs, PlayHT, and Cartesia, offering more flexible composability for custom stacks. Bland AI lacks documented white-label and BYO-LLM support. The win is narrow because Bland AI also has strong API coverage and SOC2 certification.

Both tools are API-first and offer no-code builders, but Voiceflow adds capabilities specifically useful for engineering teams building custom pipelines: bring-your-own LLM support lets teams plug in their own models, a simulation and testing suite enables pipeline validation, and white-label sub-accounts support multi-tenant architectures. ElevenLabs Agents has a strong 75ms latency advantage but lacks confirmed BYO-LLM or testing suite support. Voiceflow's $60/mo platform fee is a cost tradeoff, but its pipeline control feature set edges it ahead for engineering teams.

SMB AI receptionist. No won verdicts for this use case yet; it ranks on ties and near-misses.

Ecommerce phone agents. No won verdicts for this use case yet; it ranks on ties and near-misses.

Realtime speech model + platform. No won verdicts for this use case yet; it ranks on ties and near-misses.