Accelerate Developer Output Across Your Enterprise
Context-aware code generation, inline fixes, and real-time autocomplete reduce cycle time, cut debugging costs, and keep engineering teams shipping faster from ideation to production
Context-Blind AI Slows Coding and Breaks Developer Flow
Off-the-shelf AI misses your code context, creating errors, wasted time, and stalled projects
Data scattered across sources slows discovery
Run top models like Llama3, Mixtral, and Stable Diffusion, optimized for speed and efficiency. FireAttention serves them 4X faster than vLLM without quality loss
AI assistants deliver irrelevant or risky outputs
Without fine-tuning to your domain, assistants give inaccurate or untrustworthy responses, undermining productivity and trust
Fragmented tooling increases costs and risks
Manually stitching models, evaluations, and infrastructure slows launches and complicates compliance
Solution
Keep Developers in Flow
Fireworks AI delivers context-aware, streaming code assistance to reduce debugging time, enforce team standards, and scale across your engineering teams
Context-Aware Code Generation
Stream completions tailored to your stack, coding style, and workflows
Inline Fixes & Refactors
Apply syntax-safe transformations for bug fixes and mid-stream edits that preserve team standards
Real-Time Autocomplete
Multi-line suggestions delivered as developers type, reducing context switching and accelerating iteration
Deep Code Understanding
Fine-tune models on your internal codebase for idiomatic, architecture-aware output
Low-Latency Performance
Streaming completions with speculative decoding for sub-100ms response times
Scalable Infrastructure
GPU autoscaling and batching to handle millions of concurrent requests cost-efficiently
Model library
Recommended Models for Production-Grade Code AI
High-capacity models tackle complex code, mid-size models power scalable fixes, and lightweight models deliver fast autocomplete. All provide streaming, multi-line editing and enterprise-grade reliability
Stream completions with real-time streaming to keep developers in flow
30% Lower Latency at Scale
Sub-100ms response times for high-concurrency workloads
2.5X Higher Fix Acceptance
Inline edits and refactors produce consistent, idiomatic code
3X Higher Throughput per GPU
GPU autoscaling and batching handle enterprise workloads efficiently
CASE STUDY
Cursor achieves 3X cost savings and 2X faster completions
Cursor leveraged Fireworks AI for real-time streaming and high-concurrency handling, reducing infrastructure costs while boosting developer productivity and flow
