AI · head to head
Deepgram vs Seldon
Deepgram
AI
Voice AI API platform for speech-to-text, text-to-speech, and voice agents
- From
- Free
- Free plan
- Yes
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- From
- Free
- Free plan
- Yes
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.; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- They diverge on capability: Deepgram covers Flux speech-to-text, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Deepgram and Seldon 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 Seldon
- Kubernetes custom resources
- Inference graphs
- Traffic strategies
- Open Inference Protocol
- Alibi Explain
- Alibi Detect
- Kafka-backed pipelines in v2
- Commercial control plane
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 Seldon
- Transcribing recorded audio at scale via batch STTnot Seldon
- Adding conversational text-to-speech to voice applicationsnot Seldon
- Self-hosting speech models for data residency requirementsnot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot Deepgram
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot Deepgram
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot Deepgram
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot 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 $4K/year commitment to unlock discounted rates.
- Enterprise features and custom SLAs require a direct sales conversation rather than self-serve signup.
- Some promotional per-minute rates are time-limited, meaning long-term pricing may differ from current promotional rates.
Seldon
- Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
- Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
- Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
- Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.
Pricing, plan by plan
Deepgram
Free- Pay As You Go$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
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
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 Seldon if
- You need kubernetes custom resources.
- You want to start without paying.
- You work on Linux.
- You also want inference graphs.
Questions people ask
- Is Deepgram or Seldon better?
- Neither clearly leads. Deepgram starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Deepgram or Seldon?
- Deepgram starts at Free and Seldon at Free.
- Does Deepgram or Seldon run on more platforms?
- Deepgram runs on web, api. Seldon runs on Linux.
- 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 Seldon is typically brought in for.
- What can Deepgram do that Seldon cannot?
- Deepgram covers Flux speech-to-text, Flux text-to-speech, Voice Agent API, Real-time and batch processing. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
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.
SourceSeldon: Is Seldon open source?
Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.
Deepgram: 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.
SourceSeldon: What is the difference between v1 and v2?
Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.
Deepgram: 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.
SourceSeldon: Do I need Kubernetes?
Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.
Seldon: What is MLServer?
Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.
Seldon: Do I have to run Kafka?
For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.
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