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Riverside.fm vs Speech to Speech

Bottom line

Riverside.fm has a free plan plus paid upgrades, while Speech to Speech is free. Speech to Speech is open source and Riverside.fm is not. You can run Speech to Speech on your own machine or server; Riverside.fm is hosted by the vendor.

Which one should you try?

Riverside.fm

Freemium

Best for Podcasters conducting remote interviews

Visit Riverside.fm See full review

Speech to Speech

Free

Best for Developers building local voice agents

Visit Speech to Speech See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Riverside.fm

Riverside.fm

The local-first architecture is the load-bearing wall of the whole platform: each speaker's video and audio are captured at the source — up to 4K video and uncompressed WAV — so a bad internet connection degrades the preview stream, not the final file. From there, a text-based editor lets you cut by editing the transcript rather than scrubbing a timeline, which collapses post-production time for interview-heavy formats. AI tools handle noise removal, filler-word stripping, eye-contact correction, and clip generation without leaving the platform. The wall appears when your workflow demands fine-grained color grading, complex multi-cam switching, or the kind of layered audio mixing a DAW handles — at that point editors export tracks and finish elsewhere. Teams running high-volume enterprise webinar programs also hit limits around audience scale and CRM integration depth that push them toward dedicated webinar infrastructure.

Speech to Speech

Speech to Speech

The pipeline chains VAD → STT → LLM → TTS into a single installable Python package, with every slot independently swappable. The LLM layer speaks OpenAI-compatible protocols, so you can point it at a hosted provider or redirect it to a local vLLM or llama.cpp server without touching the rest of the stack. It exposes an OpenAI Realtime-compatible WebSocket API, which means clients built against that spec drop in without rewrites. The ceiling appears when you push toward production-grade reliability: 77 open issues in the repo signal active rough edges, and teams requiring guaranteed latency SLAs or enterprise support find precious little to stand on here.

AttributeRiverside.fmSpeech to Speech
PricingFreemiumFree
Price$24/mo—
Pricing checkedJun 11, 2026Not checked yet
Free trial14 daysNot stated
Open sourceNoYes
Has APIYesYes
Self-hosted optionNoYes
PlatformsWeb (browser-based Chrome), iOS app, Android app, Mac appPython 3.10+
Released2020-03—
Pros
  • Local recording on every participant's device captures uncompressed audio and up to 4K video regardless of connection quality, so a guest's unstable Wi-Fi degrades the preview — not the file you actually edit.
  • Separate track download for each speaker means you can fix crosstalk, swap layouts, and mix audio independently, which eliminates the destructive editing problem that comes with merged recordings.
  • Text-based editing lets you cut by deleting transcript words rather than frame-scrubbing, so a 60-minute interview can be roughed out in minutes without timeline experience.
  • AI clip generation and the 'Co-Creator' asset suite produce social cuts, captions, thumbnails, and show notes from a single session, so a solo creator ships a week of content from one recording without a separate editing pass.
  • Direct publishing to YouTube, Spotify, and Apple Podcasts is built in, so you avoid the manual upload loop and metadata re-entry that breaks distribution cadence for high-frequency publishers.
  • Every pipeline stage — VAD, STT, LLM, TTS — is independently swappable via config, so you can replace the speech recognition model for a specialized domain without rebuilding the surrounding system.
  • OpenAI Realtime-compatible WebSocket API, which means client code written against that spec works without modification — avoiding a rewrite when dropping this backend into an existing product.
  • CLI flags redirect the LLM slot to a local vLLM or llama.cpp server, so fully offline operation is a configuration change rather than a separate codebase branch.
  • Apache-2.0 license, so you can modify, extend, and ship the pipeline in commercial products without licensing friction or usage-based fees.
  • ARM64 Dockerfile ships in the repo, so deploying to embedded or robotics hardware does not require a separate porting effort.
Cons
  • The text-based editor handles cuts and transcript corrections but stops short of color grading, advanced audio mixing, and layered motion graphics — teams with a post-production specialist on staff export tracks to Premiere, DaVinci Resolve, or a DAW before the job is done, splitting the workflow the platform was supposed to consolidate.
  • AI voice and lip-sync correction for 'said the wrong thing' edits is a paid-only feature, so teams on the free tier who discover the capability in a demo cannot use it in production without upgrading.
  • Webinar functionality covers recording, streaming, and basic lead capture, but teams running demand-generation programs that need audience segmentation triggers, CRM field mapping, or post-event automation report switching to dedicated webinar platforms — the webinar feature is sufficient for internal communications but not for high-volume marketing programs where those integrations are the product.
  • There is no self-hosted option, so teams operating under data residency requirements or strict enterprise security policies that prohibit cloud-only storage have no path to compliance and must evaluate alternatives from the start.
  • Latency compounds across four sequential stages — VAD, STT, LLM, TTS each add processing time — and there is no managed infrastructure absorbing load spikes. Teams targeting sub-200ms response for consumer voice products hit this ceiling and instrument each stage independently, often replacing the slowest component with a purpose-built alternative.
  • 77 open issues in the public repo indicate active instability in edge cases. Teams with zero-downtime requirements end up maintaining their own fork to patch blockers ahead of upstream merges, which means they are now responsible for keeping that fork current.
  • There is no commercial support tier, no SLA, and no escalation path beyond GitHub issues and community forums. When a production outage traces to a pipeline bug at 2am, there is no vendor to call — teams with enterprise reliability requirements switch to a managed voice API provider and accept the vendor lock-in they were originally trying to avoid.

Made up your mind?

Riverside.fm

Freemium

Best for Podcasters conducting remote interviews

Visit Riverside.fm See full review

Speech to Speech

Free

Best for Developers building local voice agents

Visit Speech to Speech See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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