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Automation Integration · head to head

Flowable vs Together AI

Flowable logo

Flowable

Automation Integration

Apache 2.0 BPMN, CMMN and DMN engine from the team that originally built Activiti

From
Free
Rated
-
Full reviewWebsite
Together AI logo

Together AI

AI

Open-source AI at scale

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Flowable the open source engine is genuinely usable but ships without the modelling studio, work application and admin console, so an open source deployment means building your own user interfaces before anyone can use it.; Together AI free tier limits not clearly specified in pricing documentation
  • They diverge on capability: Flowable covers BPMN 2.0 engine, Together AI covers Open-source models.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Flowable and Together AI actually diverge.

Attributes where Flowable and Together AI differ
AttributeFlowableTogether AI
Pricing modelquoteusage-based
PlatformsLinux, Windows, Docker, Kubernetes, CloudApi, Cloud
CategoryAutomation IntegrationAI
FoundedUnknown2022

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 Flowable

  • BPMN 2.0 engine
  • CMMN case management
  • DMN decision tables
  • Spring Boot integration
  • Modelling studio
  • Task and case application
  • REST APIs
  • History and audit

Only in Together AI

  • Open-source models
  • Fine-tuning
  • Fast inference
  • Embeddings
  • REST API
  • Python SDK
  • OpenAI compatible
  • Api support

What people use each for

The jobs each tool is most often brought in to do.

Flowable

  • An insurer automating claims handling where each case follows a different path and BPMN alone cannot express itnot Together AI
  • A bank embedding process orchestration inside an existing Spring Boot estate rather than buying a separate platformnot Together AI
  • A team leaving Activiti or jBPM that wants the same API family with active commercial maintenancenot Together AI
  • A regulated organisation needing complete process history and decision table audit trails for supervisorsnot Together AI

Together AI

  • LLM inference for production AI applicationsnot Flowable
  • Content generation at scalenot Flowable
  • Code execution and embeddingsnot Flowable
  • Model fine-tuning and trainingnot Flowable
  • Startup and enterprise AI deploymentnot Flowable

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Flowable

  • The open source engine is genuinely usable but ships without the modelling studio, work application and admin console, so an open source deployment means building your own user interfaces before anyone can use it.
  • It expects Java engineering capacity; teams looking for a business-user low-code automation tool will find the modelling and deployment cycle far too technical.
  • Enterprise pricing is not published and is quoted per deployment, leaving buyers without a benchmark against Camunda or IBM alternatives.
  • The Activiti fork left three engines with related APIs and overlapping documentation in circulation, and search results routinely return advice for the wrong one.
  • Flowable AG is a small Swiss company relative to its competitors, so the partner network, training market and pool of engineers with production experience are all limited outside Europe.

Together AI

  • Free tier limits not clearly specified in pricing documentation
  • Pricing varies significantly by model and use case
  • Requires account setup for production access
  • Batch API discounts apply only to non-urgent workloads

Pricing, plan by plan

Flowable

Free
  • Flowable Open SourceFree
    • Apache 2.0 licensed BPMN, CMMN and DMN engines
    • Embeddable Java library or standalone apps
    • No modelling studio or enterprise applications
  • Flowable Enterprise$undefined/year
    • Modelling studio, work application and admin console
    • Connectors, forms and advanced identity management
    • Support with SLA and version maintenance

Together AI

Free
  • Serverless Inference$0.03/1M input tokens
    • Chat and Vision models
    • Image generation
    • Video generation
  • Provisioned Throughput$21600/month
    • Up to 83% savings vs commercial alternatives
    • Reserved capacity
    • Guaranteed throughput
  • Dedicated Inference$5.49/hour
    • H100 GPU instance
    • Single-tenant deployment
    • No resource sharing
  • GPU Clusters$3.99/GPU-hour
    • On-demand capacity
    • Volume discounts available
    • Reserved options with up to 35% savings

Which should you pick?

Choose Flowable if

  • You need bpmn 2.0 engine.
  • You want to start without paying.
  • You work on Linux, Windows, Docker, Kubernetes, Cloud.
  • You also want cmmn case management.
Flowable review

Choose Together AI if

  • You need open-source models.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want fine-tuning.
Together AI review

Questions people ask

Is Flowable or Together AI better?
Neither clearly leads. Flowable starts at Free and Together AI at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Flowable or Together AI?
Flowable starts at Free and Together AI at Free.
Does Flowable or Together AI run on more platforms?
Flowable runs on Linux, Windows, Docker, Kubernetes, Cloud. Together AI runs on Api, Cloud.
Can I use Flowable for free?
Both have a free tier, so you can try either at no cost before committing.
What is Flowable best used for?
Flowable is most often used for an insurer automating claims handling where each case follows a different path and bpmn alone cannot express it, a bank embedding process orchestration inside an existing spring boot estate rather than buying a separate platform, a team leaving activiti or jbpm that wants the same api family with active commercial maintenance, a regulated organisation needing complete process history and decision table audit trails for supervisors. Of those, an insurer automating claims handling where each case follows a different path and bpmn alone cannot express it and a bank embedding process orchestration inside an existing spring boot estate rather than buying a separate platform are not what Together AI is typically brought in for.
What can Flowable do that Together AI cannot?
Flowable covers BPMN 2.0 engine, CMMN case management, DMN decision tables, Spring Boot integration. Together AI covers Open-source models, Fine-tuning, Fast inference, Embeddings.

Answered from the vendors’ own pages

Flowable: Is Flowable open source?

Yes, the BPMN, CMMN and DMN engines are Apache 2.0. The modelling studio and enterprise applications are commercial.

Together AI: Does Together AI offer a free tier?

Yes, Together AI advertises 'Start for free, scale on demand,' but specific free tier usage limits are not detailed on the pricing page.

Source
Flowable: How does it relate to Activiti and jBPM?

The same core developers built jBPM, then Activiti, then forked Activiti in 2016 to create Flowable.

Together AI: What are Together AI's highest model prices?

Serverless inference pricing ranges from free for base models up to $4.40 per 1M input tokens for premium models. Video generation costs $0.14 to $3.20 per video depending on resolution.

Source
Flowable: What does Flowable Enterprise cost?

Nothing is published. It is quoted per deployment by the vendor.

Together AI: How much can I save with Provisioned Throughput?

Together AI offers up to 83% savings compared to commercial alternatives when using their Provisioned Throughput option with reserved capacity.

Source
Flowable: Why choose it over Camunda?

CMMN case management is a first-class engine, which matters when work is exception-driven rather than a fixed sequence.

Together AI: What is Together AI's fine-tuning pricing?

Standard fine-tuning costs $0.48 to $2.90 per 1M tokens depending on model size, with a minimum charge of $4.00 per job.

Source
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