Continue vs Together AI
Compare coding AI Tools
Continue is an open-source AI coding platform with VS Code and JetBrains extensions plus a CLI, letting developers build custom code agents, choose model providers, and launch background workflows triggered by events or schedules while keeping control of keys and compute.
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
- Open-source extensions: Use VS Code and JetBrains extensions to run Continue agents directly in your editor workflow daily
- Custom AI agents: Build and share reusable agents and blocks with prompts and tools tailored to your stack and repo rules
- Bring your own models: Choose from many model providers and assign roles like chat edit autocomplete embed and rerank per task
- Background automation: Launch background agents and trigger workflows on events or schedules with monitoring and interventions
- Hub Solo plan: Solo hub plan is $0 per developer per month and supports agent sharing plus use of open-source extensions
- Team governance: Team plan adds allow and block lists so admins control which agents and blocks developers can run safely
- 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
- Standardize agents: Create shared agents for code reviews migrations or scaffolding so every developer uses the same playbook
- Model flexibility: Swap providers for chat edits and autocomplete without changing the workflow when requirements shift over time
- Security remediation: Trigger agents from Snyk alerts to propose fixes and open pull requests for review in GitHub quickly
- Incident response: Use Sentry issue triggers to generate candidate patches and create PRs while engineers validate behavior
- Slack workflows: Mention the agent in Slack to kick off tasks and receive updates without manual status chasing later on
- Org key management: Use the managed proxy so developers can run agents with shared keys without exposing secrets to users
- 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
software engineers, platform engineers, developer experience teams, AI tooling leads, security teams automating fixes, startups needing model choice, enterprises needing SSO and on-prem controls
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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