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What you are comparing
A hosted API gives your application access to speech capabilities through a provider-managed service. The provider sets the available models, regions, usage meters, quotas, and service terms. Self-hosting runs inference in infrastructure you control, whether in your own cloud environment or on premises. That can offer more control over where audio is processed, but it also makes deployment and operations part of your system.
| Decision area | Hosted API | Self-hosted models |
|---|---|---|
| Inference infrastructure | Provider operates the inference service. | Your team or infrastructure provider operates the deployment. |
| Cost basis | Provider-defined usage meters, which may differ by speech task. | Compute and other infrastructure costs, plus engineering and operational work. |
| Capacity and scaling | Subject to the service’s documented quotas, regions, and scaling behavior. | Depends on deployment capacity, scaling design, licensing, and support terms. |
| Privacy and geography | Depends on the chosen service, configuration, and contract. | Can provide more control over deployment location and audio handling; actual control depends on the architecture. |
| Operational responsibility | The provider maintains the inference service; your team still owns integration and application behavior. | Your team owns or arranges deployment, capacity planning, monitoring, updates, and incident response. |
How to compare cost without misleading yourself
Start by writing down the workload rather than comparing a per-minute API rate with a server’s hourly price. Record input and output audio minutes, transcription hours, generated text characters, language-model usage, session length, peak concurrency, and how much capacity must stay available during quiet periods. Then include redundancy, monitoring, engineering, and support in the self-hosted estimate.
Hosted prices use different meters
xAI’s official voice overview, accessed October 4, 2026, lists speech-to-speech audio at $0.08 per minute, text-to-speech at $15 per million characters, batch speech-to-text at $0.10 per hour, and streaming speech-to-text at $0.20 per hour. These are xAI’s documented prices, not market-wide rates. Its speech-to-speech documentation, last updated September 22, 2026, adds a $0.004 charge per text-input event. For default server-VAD sessions, xAI says billing covers session duration; for push-to-talk sessions, it covers audio sent and received. The billing mode therefore matters as much as the headline rate when estimating an interactive session.
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Google Cloud Text-to-Speech describes character-based pricing, streaming and long-audio synthesis, and free monthly allowances for some voice families. Rates vary by voice family, so use Google’s live price schedule when estimating a specific configuration rather than applying one rate to every voice.
Self-hosted compute is only one part of the bill
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Voice.ai’s May 2026 report describes a 112-million-parameter TTS Lite checkpoint and one CPU example on an m6a.large instance. Voice.ai reported a 0.31–0.37× real-time factor and under-200-ms first audio chunk for that setup, with instance prices of approximately $0.086 per hour on demand or $0.057 per hour reserved. Those are the vendor’s figures for that instance and TTS workload, not a cost estimate for a complete voice agent or a comparison with API billing. The report said the GitHub release was forthcoming at the time, so do not assume the checkpoint is currently available.
Use a workload-specific worksheet
- Define the same workload: write down audio minutes, text volume, languages, typical and maximum call length, and expected peak concurrent sessions.
- Map each task to its meter: separate speech-to-speech, transcription, synthesis, and language-model usage. Apply each candidate provider’s current billing rules to those quantities.
- Size self-hosted capacity: estimate compute for the target model and peak concurrency, then include idle headroom and redundancy needed for the required service level.
- Add operating costs: include deployment, monitoring, updates, incident response, and support in addition to infrastructure.
- Run both estimates against the same assumptions: compare equivalent quality, availability, geographic placement, and traffic patterns. Recheck vendor rates and quotas before using the result for a budget decision.
How to compare latency fairly
Do not compare a model’s inference latency with an end user’s wait for a spoken response. Conversational time-to-first-audio includes the network, speech recognition, language generation, speech synthesis, and turn-taking behavior. Streaming and pipelining can let later stages begin before earlier stages finish, so a benchmark that changes streaming behavior or endpoint geography is not an apples-to-apples comparison.
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- Use the same audio, application endpoint geography, concurrency, and turn-taking policy for each candidate.
- Record median and tail time-to-first-audio, not just the fastest observed result.
- Measure interruptions and end-of-turn behavior alongside response time; a fast first chunk may not mean a smooth conversation.
- Test representative languages, accents, audio conditions, and task flows because recognition and completion quality affect perceived speed.
The authors of the 2026 technical tutorial “Building Enterprise Realtime Voice Agents from Scratch” reported 947 ms P50 time-to-first-audio and a 729 ms best case for their described cascaded streaming STT → LLM → TTS implementation. Those numbers characterize that implementation; they are not a universal target or a direct comparison between an API and a self-hosted stack.
Read latency claims in context
Deepgram’s self-hosting product page claims under 200 ms real-time inference latency when the deployment is co-located with the application. That is a vendor claim about inference latency, not an independently established end-to-end conversational result. It should not be compared directly with a different system’s time-to-first-audio without matching test conditions.
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Scaling, limits, and reliability
Scaling characteristics depend on the particular service or deployment rather than on the words “API” and “self-hosted” alone. Check the actual capacity limits and operational behavior before committing to either architecture.
Hosted-service limits
xAI’s speech-to-speech documentation, last updated September 22, 2026, lists 10 concurrent sessions per team and a maximum session length of 120 minutes. It describes real-time conversations over WebSocket with function calling and tool access, and lists a US East region. These details are specific to that documented service and may change; validate current limits and geographic availability against the service you plan to use.
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Self-hosted capacity
Deepgram markets autoscaling for its self-hosted offering and says it can run in a customer’s cloud or on premises. Marketing statements do not replace deployment-specific sizing: confirm actual capacity, licensing, topology, scaling behavior, and support terms with the vendor for your intended workload.
Reliability is an architecture requirement
- For an API, establish the applicable quotas, availability commitments, failover options, and behavior when the provider is unavailable.
- For self-hosting, plan for capacity loss, model or host failures, deployment rollbacks, and how the service behaves while new capacity warms up.
- For either option, determine who responds to incidents and what fallback—if any—keeps a conversation usable.
Privacy, control, and engineering ownership
Self-hosting can put inference in infrastructure controlled by the operator, which may help meet deployment-location or data-handling requirements. Deepgram promotes privacy, regulatory and data-residency control, and on-premises or customer-cloud deployments. Those are vendor-described capabilities, not a substitute for checking the chosen configuration, contract, data flows, and retention terms.
A hosted API can reduce the work of operating inference, but the application’s integration, monitoring, and handling of audio remain your responsibility. With self-hosting, add model updates, capacity planning, operational monitoring, and incident response to the team’s workload. Include those responsibilities in the decision even if they do not appear as a per-minute line item.
When each option is a better fit
Consider a hosted API when
- You want to integrate speech capabilities without building and operating an inference deployment.
- The service’s supported languages, regions, quality, quotas, and data terms meet your requirements.
- Your traffic and product needs fit its billing model, including any session-duration or concurrency limits.
Consider self-hosting when
- You have a concrete requirement for control over deployment location or inference infrastructure.
- Your team can operate the deployment and validate its capacity, quality, latency, and resilience.
- A workload-specific estimate—including operations and redundancy—supports the choice against available hosted options.
For a mixed system, evaluate each speech task separately: the best deployment for transcription need not be the best one for synthesis or a complete speech-to-speech conversation. Choose based on measured quality and the full cost and operational profile of each part.
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What would establish a break-even point?
No general break-even volume follows from the available examples. Establishing one requires the same workload and quality target on both sides, plus actual traffic volume, concurrency, model selection, hardware utilization, redundancy and availability assumptions, engineering effort, and current API terms. Benchmark candidate configurations and calculate total cost under those assumptions before claiming that one architecture becomes cheaper at a particular number of calls or minutes.
Quick Recap
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