Kite (Discontinued) vs Together AI
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Former AI code completion assistant for editors like VS Code and PyCharm. The company ended development and support and published a farewell note.
Together AI is a cloud platform that provides API access to multiple AI model families for inference and generation, with per unit billing and account tier limits, letting developers run text, image, audio, and video models through a single service and documentation.
Feature Tags Comparison
Key Features
- Legacy editor plugins for popular IDEs during active years
- Local context indexing to improve token suggestions
- Early language model work for Python and more
- Documentation lookups and API hinting in editor
- Telemetry options and privacy settings historically
- Official shutdown and end of support by the founder
- Serverless inference API: Call hosted text and multimodal models with per unit billing so you can scale without managing GPUs
- Model catalog pricing: View published model rates and modality sections so cost estimation can be tied to a chosen model id
- Billing and credits: Start with a minimum credit purchase and track balances and limits so usage stays within budget rules
- Rate limit tiers: Qualification based tiers define request and media limits which helps plan throughput for production loads
- Fine tuning services: Offers documented fine tuning workflows with minimum balance requirements and job monitoring tools
- Dedicated infrastructure: Provides options for dedicated endpoints or clusters when you need isolated capacity and controls
Use Cases
- Audit machines and remove old plugins to avoid confusion
- Review legacy repos created during Kite usage periods
- Educate teams on evolution toward modern copilots
- Map migration to maintained assistants with security fixes
- Discuss pricing and PMF lessons in internal tech talks
- Document editor integration approaches that worked and failed
- Prototype an API product: Integrate a single model endpoint for chat and iterate on prompts while tracking per request cost
- Model benchmarking: Swap model ids and compare latency and output quality under the same workload to select a stable baseline
- Image generation backend: Generate images via API for an app and enforce spend limits with credit based billing controls
- Video generation experiments: Test short video models for marketing clips and measure cost per output before scaling usage
- Fine tune for domain tone: Run a fine tuning job for internal style and evaluate improvements with controlled test sets at scale
- Operational guardrails: Implement rate limit aware retries and budget alerts so production traffic stays within set limits
Perfect For
engineering managers developer advocates and students studying the history of AI coding assistants and planning migrations to supported tools
ml engineers, backend developers, ai product teams, startup founders building ai apps, researchers running benchmarks, platform engineers managing api throughput, teams evaluating model costs
Capabilities
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