AI Tool Comparison
Comparing as AI Agent & Orchestration FrameworksFireworks AI vs Devin
Compare features, pricing, pros & cons, and user ratings to decide which AI tool is best for your needs.
Fireworks AI
VS
Devin
Verdict by Category
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Detailed Comparison
Feature
Fireworks AI
Devin
Pricing
PaidFireworks AI's serverless inference is pay-per-token with postpaid billing and $1 in free starter credits, with per-model rates across Standard, Priority, and Fast tiers detailed in its documentation (e.g. GLM 5.2 at $1.40/M input and $4.40/M output tokens, MiniMax M3 at $0.30/M input and $1.20/M output tokens). Embeddings are priced by base model size, from $0.008 to $0.10 per 1M input tokens. Training is priced per 1M training tokens for supervised fine-tuning (SFT) and direct preference optimization (DPO): LoRA SFT ranges from $0.50 (models up to 16B parameters) to $10.00 (models over 300B), with Full Param SFT and DPO costing roughly 2-4x more depending on model size and method. Reinforcement fine-tuning is billed per GPU hour at on-demand rates. The Serverless Training API charges separately for prefill, cached prefill, sample, and train tokens (e.g. Qwen 3.5 9B at $0.66-$1.995 per 1M tokens depending on operation). On-demand GPU deployments are billed per GPU hour: $7.00 for H100 or H200, $10.00 for B200, $12.00 for B300, and $18.00 for GB300, with region-restricted (US/Europe) deployments priced at 1.5x standard rates. Reserved and enterprise capacity pricing is available by contacting sales.
FreemiumDevin's Free plan costs $0/month with a light quota to code with agents, limited model availability, and unlimited inline edits and Tab completions. Pro costs $20/month and adds increased quotas, access to OpenAI, Claude, and Gemini frontier models, free use of SWE 1.7 and leading open-source models, Devin Cloud access, and the ability to purchase extra usage at API pricing. Max costs $200/month with everything in Pro plus significantly higher usage quotas for power users. Teams costs $80/month as a base team fee plus $40/month per full developer seat, and includes unlimited team members via flex seats, sharing and collaboration, centralized billing, an admin dashboard with analytics, and priority support. Enterprise is custom-priced ("let's talk") and adds highest-priority support, dedicated account management, SAML/OIDC SSO, centralized enterprise admin controls, and dedicated VPC deployment options. Usage allowances refresh daily and weekly, and extra usage beyond included quotas is billed at API pricing.
Categories
AI Developer APIs & Platforms
AI Coding Assistants
Summary
High-performance training and inference platform for open-source AI models
Autonomous AI software engineer that plans, codes, and ships end-to-end
Fireworks AI Pros & Cons
Pros
- Founded by former core PyTorch engineers with deep inference optimization expertise
- OpenAI and Anthropic-compatible API simplifies migration from closed-model providers
- Proprietary FireAttention and FireOptimizer deliver strong throughput and latency gains
- Full spectrum of training options from guided runs to fully custom RL loops
- Proven at massive scale, processing tens of trillions of tokens daily for 10,000+ customers
- Backed by major investors and used in production by Cursor, Notion, Vercel, and Quora
Cons
- Pricing is spread across serverless, on-demand, and training pages, requiring some effort to estimate total costs
- Region-restricted deployments in the US or Europe cost 1.5x standard on-demand rates
- Reserved and enterprise capacity requires contacting sales rather than transparent self-serve pricing
- Reinforcement fine-tuning billed per GPU hour can be harder to predict than flat per-token pricing
- Primarily focused on open-weight models, so access to fully closed frontier models is more limited
Devin Pros & Cons
Pros
- Handles full engineering workflows end-to-end, not just inline suggestions
- Fleet-based parallel agents can tackle large-scale migrations across many repos
- Deep integrations with GitHub, Linear, Jira, Slack, and Teams for real dev workflows
- Free tier available to try core agent capabilities with no cost
- Documented enterprise results, including major efficiency and cost gains at Nubank
- VPC deployment and SSO support enterprise security requirements
Cons
- Early benchmark and demo claims were criticized as overstated, so results should be evaluated against a team's own workflows
- Best suited to well-scoped, reviewable tasks rather than fully unsupervised production work
- Usage-based cost can climb quickly for teams running many parallel sessions
- Full model availability and cloud agents require the $20/month Pro plan or higher
- Quality of output still requires human review, especially on complex or ambiguous tasks