The AI inference market: 25 providers ranked by price, speed, and reliability
My most thorough inference provider comparison yet. 25 providers, 60 days of monitoring, 3 metrics. Cerebras leads on speed. Together AI leads on open source model selection. Anthropic leads on reliability. Full rankings and methodology inside.
I expanded my monitoring to 25 providers. Sixty days of data. Three metrics: speed, cost, reliability.
This is the most complete inference provider comparison I've published.
The rankings
| Overall rank | Provider | Speed rank | Cost rank | Reliability rank | |-------------|----------|-----------|-----------|-----------------| | 1 | Anthropic | 12 | 18 | 1 | | 2 | Cerebras | 1 | 20 | 8 | | 3 | Fireworks AI | 4 | 2 | 3 | | 4 | Google AI | 5 | 5 | 4 | | 5 | Together AI | 6 | 1 | 6 | | 6 | Groq | 2 | 8 | 9 | | 7 | OpenAI | 10 | 15 | 5 | | 8 | Baseten | 7 | 3 | 10 | | 9 | Modal | 8 | 4 | 11 | | 10 | AWS Bedrock | 18 | 22 | 2 |
Sources: My monitoring infrastructure, 60-day average (December 2025 to February 2026), Artificial Analysis for cross-reference.
Overall ranking weights: 40% reliability, 30% cost, 30% speed. Reliability matters most for production use.
Speed leaders (tokens per second, Llama 3.1 70B equivalent)
| Rank | Provider | Tokens/sec | TTFT | |------|----------|-----------|------| | 1 | Cerebras | 2,140 | 72ms | | 2 | Groq | 920 | 88ms | | 3 | SambaNova | 640 | 105ms | | 4 | Fireworks AI | 480 | 128ms | | 5 | Together AI | 420 | 155ms |
Cerebras continues to dominate speed at 2,140 t/s. That's 2.3x faster than Groq (#2). Their wafer-scale architecture is genuinely in a different performance class.
Cost leaders (per million output tokens, open source models)
| Rank | Provider | Llama 70B output/M | Model selection | |------|----------|--------------------|--------------------| | 1 | Together AI | $0.72 | 40+ models | | 2 | Fireworks AI | $0.78 | 35+ models | | 3 | Baseten | $0.82 | Custom deployments | | 4 | Modal | $0.85 | Custom deployments | | 5 | Lepton AI | $0.88 | 25+ models |
Together AI and Fireworks are in a tight race for cheapest open source inference. The 8% price difference between #1 and #2 is negligible. Model selection (Together's 40+ vs Fireworks' 35+) might be the tiebreaker.
Reliability leaders (uptime, 60 days)
| Rank | Provider | Uptime | Incidents | Avg resolution | |------|----------|--------|-----------|---------------| | 1 | Anthropic | 99.82% | 3 | 45 min | | 2 | AWS Bedrock | 99.74% | 2 | 68 min | | 3 | Fireworks AI | 99.68% | 5 | 38 min | | 4 | Google AI | 99.61% | 4 | 56 min | | 5 | OpenAI | 99.54% | 6 | 48 min |
Anthropic takes the reliability crown at 99.82% uptime. Only 3 incidents in 60 days, averaging 45 minutes to resolve. The combination of uptime and fast resolution makes them the most dependable API.
Fireworks at #3 is impressive for a smaller provider. Their incidents are resolved faster (38 min) than anyone else, suggesting good operational processes.
Provider profiles
| Best for... | Provider | Why | |------------|----------|-----| | Production reliability | Anthropic | 99.82% uptime, own frontier models | | Raw speed | Cerebras | 2,140 t/s, nothing else close | | Cost-optimized open source | Together AI | Cheapest per token, largest model selection | | Balanced speed + cost | Fireworks AI | 4th on speed, 2nd on cost, 3rd on reliability | | Enterprise compliance | AWS Bedrock | SOC2, HIPAA, existing AWS integration | | Speed + reasonable cost | Groq | 920 t/s, mid-range pricing |
Year-over-year changes
| Metric | Feb 2025 | Feb 2026 | Change | |--------|----------|----------|--------| | Providers monitored | 15 | 25 | +67% | | Fastest provider speed | 1,847 t/s (Cerebras) | 2,140 t/s (Cerebras) | +16% | | Cheapest per M tokens | $0.88 | $0.72 | -18% | | Best uptime | 99.7% (Anthropic) | 99.82% (Anthropic) | +0.12 pts |
The market is maturing. More providers, faster speeds, lower costs, better reliability. All trends pointing in the right direction.
The inference market in 2026 is genuinely competitive. You have good options no matter what you optimize for.
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-- dataku