AI · head to head
Deepgram vs Langwatch

Deepgram
AI
Voice AI API platform for speech-to-text, text-to-speech, and voice agents
- From
- Free
- Rated
- -

Langwatch
Machine Learning
LLM engineering platform for testing and evaluating AI agents in production
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Deepgram pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.; Langwatch free plan limited to 50k events per month, restricting larger deployments
- They diverge on capability: Deepgram covers Flux speech-to-text, Langwatch covers Agent simulation testing.
Where they differ
Only the attributes on which Deepgram and Langwatch actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Deepgram
- Flux speech-to-text
- Flux text-to-speech
- Voice Agent API
- Real-time and batch processing
- Self-hosted deployment
- Audio intelligence
Only in Langwatch
- Agent simulation testing
- LLM evaluation
- OpenTelemetry tracing
- Langy AI Engineer
- Governance controls
- Multiple deployment options
- Framework support
What people use each for
The jobs each tool is most often brought in to do.
Deepgram
- Building real-time voice agents for customer supportnot Langwatch
- Transcribing recorded audio at scale via batch STTnot Langwatch
- Adding conversational text-to-speech to voice applicationsnot Langwatch
- Self-hosting speech models for data residency requirementsnot Langwatch
Langwatch
- Continuous testing of AI agents before production deploymentnot Deepgram
- Automated test creation from product requirementsnot Deepgram
- LLM response quality evaluation and scoringnot Deepgram
- Production agent monitoring and cost trackingnot Deepgram
- Governance and access control for AI systemsnot Deepgram
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Deepgram
- Pricing is entirely usage-based, so total cost can be harder to predict than flat subscription tools.
- The Growth plan requires a minimum $4K/year commitment to unlock discounted rates.
- Enterprise features and custom SLAs require a direct sales conversation rather than self-serve signup.
- Some promotional per-minute rates are time-limited, meaning long-term pricing may differ from current promotional rates.
Langwatch
- Free plan limited to 50k events per month, restricting larger deployments
- Pricing in EUR may complicate budgeting for US-based teams
- Usage-based overage model can create unpredictable costs
- Self-hosted option requires DevOps expertise
Pricing, plan by plan
Deepgram
Free- Pay As You Go$undefined/mo
- $200 free credit to start
- No minimums or expiration
- No credit card required to start
- Growth$undefined/mo
- Save up to 20% with annual pre-paid credits
- Minimum $4K/year commitment
- Credits applied against actual usage
- Enterprise$undefined/mo
- Custom pricing for large-scale deployments
- Dedicated support and contracts
Langwatch
Free- DeveloperFree
- 50k events per month
- 14-day data access
- 2 users
- Growth$29/month
- 200k events per month included
- 5 EUR per 100k additional events
- 30-day data retention
- Enterprise$undefined/custom
- Custom event limits
- Hybrid, self-hosted or on-premises deployment
- Custom SSO and RBAC
Which should you pick?
Choose Deepgram if
- You need flux speech-to-text.
- You want to start without paying.
- You work on web, api.
- You also want flux text-to-speech.
Choose Langwatch if
- You need agent simulation testing.
- You want to start without paying.
- You work on Web, Docker, Kubernetes.
- You also want llm evaluation.
Questions people ask
- Is Deepgram or Langwatch better?
- Neither clearly leads. Deepgram starts at Free and Langwatch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Deepgram or Langwatch?
- Deepgram starts at Free and Langwatch at Free.
- Does Deepgram or Langwatch run on more platforms?
- Deepgram runs on web, api. Langwatch runs on Web, Docker, Kubernetes.
- Can I use Deepgram for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Deepgram best used for?
- Deepgram is most often used for building real-time voice agents for customer support, transcribing recorded audio at scale via batch stt, adding conversational text-to-speech to voice applications, self-hosting speech models for data residency requirements. Of those, building real-time voice agents for customer support and transcribing recorded audio at scale via batch stt are not what Langwatch is typically brought in for.
- What can Deepgram do that Langwatch cannot?
- Deepgram covers Flux speech-to-text, Flux text-to-speech, Voice Agent API, Real-time and batch processing. Langwatch covers Agent simulation testing, LLM evaluation, OpenTelemetry tracing, Langy AI Engineer.
Answered from the vendors’ own pages
Deepgram: What does Deepgram cost?
Deepgram uses usage-based pricing starting with $200 in free credit, pay-as-you-go rates per minute or per character, a Growth plan with annual pre-paid credits requiring a $4K/year minimum, and custom Enterprise pricing.
SourceLangwatch: Is there a permanent free tier?
Yes, Langwatch's Developer plan is free forever with 50k events per month, 14-day data access, 2 users, and no credit card required. It is specifically designed for individual developers prototyping AI applications.
SourceDeepgram: Is there a free plan, and what are its limits?
New users get $200 of free credit with no credit card required, which can be applied to speech-to-text, text-to-speech, or voice agent usage before any payment is needed.
SourceLangwatch: What is Langy and how does it save time?
Langy is an AI-powered tool that automates test creation. It converts product requirements into test scenarios, runs simulations, scores results, and generates pull requests with fixes in a median of 14 minutes.
SourceDeepgram: How is usage metered?
Usage is metered per minute of audio for speech-to-text and voice agent calls, and per 1,000 characters for text-to-speech, with add-ons like redaction and entity detection billed separately per minute.
SourceLangwatch: What frameworks does Langwatch support?
Langwatch works with LangGraph, LangChain, CrewAI, OpenAI Agents, AWS Bedrock, Azure OpenAI, Vertex AI, and other major LLM frameworks and platforms.
SourceRelated pages
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