AICentralResources
AI Tool Comparison

Comparing as AI LLM APIs (Foundation Models)
Databricks vs Together AI

Databricks

Databricks

VS
Together AI

Together AI

Verdict by Category

Detailed category analysis is not available for this comparison.
View Databricks View Together AI

Detailed Comparison

Feature
Databricks
Together AI
Pricing
FreemiumDatabricks uses consumption-based pricing measured in Databricks Units (DBUs), with rates varying by workload type and platform edition (Standard, Premium, Enterprise). Representative per-DBU rates include Data Engineering at approximately $0.15/DBU, Data Warehousing at $0.22/DBU, Interactive/All-Purpose compute at up to $0.40/DBU, and AI/ML workloads at around $0.07/DBU. Premium edition costs roughly 37% more per DBU than Standard but adds role-based access control, Unity Catalog governance, audit logging, and SQL Serverless warehouses; Enterprise tier pricing is typically higher still or governed by custom committed-use agreements with added compliance features like HIPAA support and customer-managed encryption keys. Underlying cloud infrastructure costs (compute instances, storage, networking) from AWS, Azure, or GCP are billed separately from DBU charges, except for certain bundled serverless SKUs. Databricks offers a 14-day free trial with usage credits and a permanently free, quota-limited Free Edition for learning and experimentation, but no free tier for production use. Committing to 1-3 year contracts can reduce DBU costs by up to 37%, and using Jobs Compute instead of All-Purpose Compute can cut costs up to 4x for eligible workloads. Note that Azure Databricks' Standard tier is being retired in October 2026, requiring affected customers to migrate to Premium.
PaidTogether AI uses pay-as-you-go pricing across its products. Serverless inference is billed per model, priced per 1M tokens for text (e.g., MiniMax M3 at $0.30 input/$1.20 output, GLM-5.2 at $1.40 input/$4.40 output, gpt-oss-120B at $0.15 input/$0.60 output), per image for image generation (e.g., FLUX.1 [schnell] at $0.0027/image), per video for video models (e.g., ByteDance Seedance 2.5 at $0.115/video, Google Veo 3.0 at $1.60/video), and per audio minute or character for speech models. Dedicated Inference runs on single-tenant GPUs starting at $5.49/GPU/hour on-demand for NVIDIA HGX H100 and $8.99/hour for HGX B200, with reserved options available via sales. GPU Clusters offer on-demand rates from $3.99/hour (H100) to $8.19/hour (B200), with reserved pricing dropping as low as $3.19/hour for 181+ day H100 commitments. Sandbox compute costs $0.0446/vCPU/hour and $0.0149/GiB RAM/hour, with Code Interpreter sessions at $0.03 per 60-minute session. Fine-tuning is priced per 1M tokens processed, ranging from $0.48 (LoRA, up to 16B parameters) to $8.00 (full fine-tuning, 70-100B parameters) for standard models, with specialized model pricing (e.g., DeepSeek-R1, GLM-5) ranging $5-$40 per 1M tokens plus a minimum job charge. Managed Storage costs $0.16/GiB/month.
Categories
AI Developer APIs & PlatformsAI Data & Analytics ToolsAI No-Code / Automation Tools
AI Developer APIs & PlatformsLarge Language Models (LLMs)
Summary
The Data Intelligence Platform for building and scaling data and AI
Full-stack AI cloud for inference, fine-tuning, and GPU clusters
Databricks

Databricks Pros & Cons

Pros

  • Founded and led by the original creators of Apache Spark, Delta Lake, and MLflow, giving deep technical credibility
  • Lakehouse architecture genuinely unifies data warehousing and AI/ML workloads instead of requiring separate tools
  • True multi-cloud support across AWS, Azure, and GCP avoids vendor lock-in to one cloud provider
  • Unity Catalog's open-source governance layer extends value beyond just the Databricks platform itself
  • Proven at massive scale: $5.4B ARR, free-cash-flow positive, and used by over 60% of the Fortune 500

Cons

  • Consumption-based DBU pricing makes costs hard to predict without careful workload monitoring and governance
  • Per-DBU rates for many workload types require contacting sales rather than a fully public rate card
  • Azure Databricks Standard tier is being retired in October 2026, forcing some customers to migrate to pricier Premium
  • Steep learning curve for teams without existing Spark, data engineering, or MLOps experience
  • Cloud infrastructure costs (compute, storage, networking) are billed separately from DBUs, adding a second cost layer to track
Together AI

Together AI Pros & Cons

Pros

  • OpenAI-compatible API makes migrating from closed-model providers straightforward
  • Transparent per-model, pay-as-you-go pricing across 200+ open-source models
  • Vertically integrated GPU cloud offers competitive on-demand and reserved rates
  • Backed by deep systems research, including FlashAttention and other efficiency breakthroughs
  • Full-stack coverage from inference to fine-tuning to raw GPU compute in one platform
  • Proven at scale with customers like Cursor, Zoom, Quora, and ElevenLabs

Cons

  • Pricing spans many separate model and product pages, making total cost estimation more complex than flat-rate competitors
  • Dedicated GPU and reserved cluster pricing largely requires contacting sales rather than transparent self-serve rates
  • Focus on open-source models means access to closed frontier models like GPT or Claude isn't the platform's core strength
  • Fine-tuning costs vary significantly by model size and technique, requiring careful comparison before committing
  • Provisioned throughput and PTU-based pricing has a learning curve for teams new to capacity-based billing