AI Tool Comparison
Comparing as AI Agent & Orchestration FrameworksQdrant vs Together AI
Compare features, pricing, pros & cons, and user ratings to decide which AI tool is best for your needs.
Qdrant
VS
Together AI
Verdict by Category
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Detailed Comparison
Feature
Qdrant
Together AI
Pricing
FreemiumQdrant's Free Tier is free forever, offering a single-node cluster with 0.5 vCPU, 1GB RAM, and 4GB disk, plus free cloud inference with selected models, ideal for testing and prototypes. The Standard Tier uses usage-based pricing for production workloads, billed hourly based on compute (vCPU), memory (GB), storage (GB), backup storage, and used inference tokens for paid models; it includes dedicated resources, flexible vertical and horizontal scaling, high availability setups, backup and disaster recovery, and a 99.5% uptime SLA. The Premium Tier requires a minimum spend and adds SSO, private VPC links, a 99.9% uptime SLA, and extra support for enterprises with additional security and compliance needs, available by contacting sales. Qdrant Hybrid Cloud lets teams run managed Qdrant clusters on their own infrastructure for local data residency and regulated workloads, while Private Cloud offers a fully isolated, air-gapped deployment for large enterprises; both require contacting the Qdrant team for pricing. The open-source Qdrant engine itself remains free and self-hostable under an Apache 2.0 license.
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 & Platforms
AI Developer APIs & Platforms
Summary
Open-source vector search engine for production-grade AI retrieval
Full-stack AI cloud for inference, fine-tuning, and GPU clusters
Qdrant Pros & Cons
Pros
- Free forever tier with no time limit, ideal for testing and small projects
- Open-source core under Apache 2.0 with full self-hosting flexibility
- High-performance Rust architecture built for real-time, large-scale vector search
- Native hybrid dense-sparse search and advanced filtering in a single query
- Flexible deployment across managed cloud, hybrid, private, and edge environments
- SOC 2 and HIPAA compliant with strong enterprise security options
Cons
- Standard and Premium Cloud tiers use usage-based or minimum-spend pricing rather than flat, published rates
- Premium tier features like SSO and private VPC links require talking to sales for pricing
- Self-hosting the open-source engine requires managing your own infrastructure and scaling
- As a specialized vector database, it requires pairing with separate embedding models and application logic
- Some advanced enterprise features like custom SLAs are only available through Hybrid or Private Cloud contracts
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