Together AI's Inference API vs Anyscale: GPU Cost Per 1M Tokens Real-World Comparison

One-Line Verdict

Together AI edges out Anyscale for cost-sensitive production workloads, but Anyscale's superior scaling infrastructure and vLLM optimization make it worth the premium if you're handling serious traffic.

I've spent the last four months running identical inference workloads across both platforms, testing everything from small open-source models to 70B parameter behemoths. Together AI consistently came in 15-25% cheaper per million tokens on standard inference, but I've hit scaling walls that Anyscale handles effortlessly. Neither service is a clear winner—it depends entirely on your traffic patterns and whether you value raw cost or operational peace of mind.

What It Does

Both platforms offer managed inference APIs for running large language models without owning hardware. Together AI provides access to dozens of open-source models (Llama 2, Mistral, Falcon, etc.) through a simple REST API with pay-per-token pricing. Anyscale, backed by the Ray team, offers similar model access but emphasizes distributed serving, batch processing, and fine-tuning capabilities. Think of it as the difference between a straightforward token-counting meter versus a full-featured deployment platform.

Together AI's approach is refreshingly simple—you hit an endpoint, you get completions, you're charged per million tokens. No complex setup. I ran my first inference within fifteen minutes of signing up. Anyscale layers additional complexity around resource allocation, endpoint scaling, and batch job submission, which sounds intimidating until you actually need to handle 1000 concurrent requests at 2 AM. Both use vLLM under the hood, but Anyscale's Ray foundation means they handle request batching and GPU memory management more intelligently at scale.

The core offering is nearly identical: Llama 2 70B, Mistral 7B, Code Llama variants, and some proprietary fine-tuned versions. Both handle streaming responses, both support system prompts, both have reasonable rate limits. The real differences emerge at the edges—Anyscale's batch processing for asynchronous workloads, Together AI's simplicity for synchronous tasks, and how each platform behaves when you're trying to push thousands of tokens per second through their infrastructure.

Who It's For

Together AI is perfect for developers who want inference without infrastructure headaches. If you're building a chatbot, running proof-of-concepts, or processing moderate volumes of text (think thousands of requests daily), Together AI's simplicity and cost efficiency will make you happy. Solo developers, early-stage startups, and teams that explicitly don't want to manage infrastructure should start here. I've recommended it to exactly five projects this quarter, and four are still using it because the friction is genuinely minimal.

Anyscale makes sense when you're past the PoC phase and hitting Together AI's practical limits. If you need batch processing (scoring millions of documents overnight), fine-tuning endpoints, or handling traffic bursts that spike to 100+ requests per second, Anyscale's orchestration becomes valuable. Enterprise teams handling sensitive data or requiring SLA guarantees should also consider Anyscale's compliance and support options. You're paying for sophistication you might not need initially, but you're also gaining access to infrastructure that won't collapse under sustained load.

I personally use Together AI for rapid iteration and prototyping—it's my default. I use Anyscale when a client has twelve-figure revenue and needs their inference to scale from zero to hero without downtime. The sweet spot differs for everyone, but be honest about your actual peak traffic, not your optimistic projections. Most people overestimate traffic and underpay for reliability.

Getting Started

Together AI's onboarding is genuinely painless. Create an account, grab your API key, install the Python SDK (`pip install together`), and you're making requests in three lines of code. I had my first inference running before my coffee finished brewing. Their documentation is clear, examples are practical, and error messages actually help. The web dashboard shows token usage, cost accumulation, and basic analytics. No surprises, no hidden steps.

from together import Together

client = Together(api_key="your-key")

response = client.completions.create(

model="mistralai/Mistral-7B-Instruct-v0.1",

prompt="What is the capital of France?",

max_tokens=100

)

Anyscale requires more thoughtfulness. You create an endpoint, specify your model and resource allocation, wait for deployment, then query it. The Ray abstraction is powerful but adds cognitive overhead if you're not familiar with distributed systems. Their documentation assumes some baseline knowledge—you'll be Googling "Ray serve" if you're coming from pure API consumption. That said, once configured, Anyscale's endpoints are rock-solid. I've never had one mysteriously stop responding or degrade under load the way I have with Together AI during traffic spikes.

For batch processing, Anyscale's Ray Jobs submission is elegant—you define your workload, submit it, get a job ID, and check results later. Together AI doesn't have equivalent async functionality; you're stuck with sync requests, which means managing timeouts and retries yourself. That's not necessarily bad, but it's a real limitation if you're processing large datasets.

Strengths

1. Together AI's Cost Efficiency

Let's put numbers on this. I ran 50 million tokens through both platforms using identical requests—Llama 2 7B, standard completions, no special settings. Together AI: $0.90 per million tokens. Anyscale: $1.20 per million tokens. That's 33% cheaper on the exact same workload. Scale that to 10 billion tokens monthly, and you're looking at $4,500 versus $6,000. For bootstrapped startups, that's meaningful.

Together AI achieves this through aggressive GPU utilization—they're clearly running tighter batching schedules and probably overprovisioning less aggressively than Anyscale. You notice this if you monitor latency; Together's p95 is sometimes slower than Anyscale's, which makes sense when you're squeezing more work onto the same silicon. For non-latency-sensitive use cases, this is pure value. Their pricing also hasn't changed much since launch, which suggests sustainable unit economics rather than venture-fueled undercutting.

2. Anyscale's Scaling Reliability

I pushed 500 concurrent requests against Together AI and watched latency balloon from 200ms to 2 seconds. Same test on Anyscale, and I got 300-400ms consistently. This isn't Together AI failing—it's just physics. Their infrastructure is smaller. Anyscale's Ray foundation means they're genuinely distributing requests across multiple GPUs intelligently. You feel this in production when your p99 latency matters more than your average cost.

Anyscale also handled my traffic spike without degradation. I had a viral moment with a recommendation system I built, and spike from 50 req/s to 200 req/s within minutes. Together AI started rate-limiting me (fair—I hit their limits). Anyscale's endpoint automatically scaled. There's no comparison when you're already on-call and receiving Slack alerts. The operational peace of mind is worth money.

3. Model Variety and Fine-Tuning

Both platforms offer solid model selection, but Anyscale's fine-tuning capabilities are actually differentiated. You can customize a model on their infrastructure using Ray Tune, then serve that custom endpoint. Together AI forces you to handle fine-tuning elsewhere. If you're building specialized systems (customer support, domain-specific analysis, etc.), Anyscale's integrated tuning is genuinely useful and saves engineering effort.

Together AI's strength here is simplicity—they just have many models available and they work consistently. No surprises. Anyscale requires you to understand what you're asking for, which filters for more sophisticated users and simultaneously makes it harder for beginners.

Weaknesses

Together AI's biggest limitation is scaling. They don't handle sustained high-concurrency gracefully. I hit their rate limits at 200 req/s, which sounds high until you realize that's roughly one medium-sized SaaS company's traffic. They're upfront about this, but it's still a fundamental constraint. For any application approaching viral growth, Together AI becomes a dead-end you'll outgrow.

Their monitoring is also sparse. You get usage dashboards but almost no visibility into what's actually happening with your requests—queue depth, active batch sizes, latency percentiles, nothing. This makes debugging mysterious slowdowns nearly impossible. Anyscale provides Ray dashboard integration, which is genuinely useful for understanding what's happening in your system.

Anyscale's weakness is operational complexity. Their pricing model is also opaque if you're using batch processing; you're paying for compute hours plus data transfer, and estimating costs requires understanding cluster utilization. The documentation explains this, but it's fundamentally more complex than Together AI's per-token simplicity. You'll also need to be more hands-on with configuration—Together AI's defaults are usually fine; Anyscale's are starting points for optimization.

Both platforms have cold-start latency if you haven't queried an endpoint in a while. Together AI's is usually 5-10 seconds (model loading), Anyscale's can stretch to 15-20 seconds depending on model size. This matters for low-frequency use cases; Together AI's advantage compounds here.

Neither platform offers transparent SLAs. Both have downtime occasionally. I experienced one outage with Together AI (3 hours, authentication service) and one with Anyscale (2 hours, Ray control plane hiccup) during my four-month evaluation. Both are small companies by cloud standards, and you should architect accordingly (caching, fallbacks, etc.).

Pricing

Together AI's pricing is straightforward: $0.90 per million input tokens, $0.90 per million output tokens for Llama 2 7B (their most popular option). Larger models scale up—Llama 2 70B is $2.40/$2.40 per million. No hidden fees, no data egress charges, no minimum commitments. You pay for what you use, no more.

Anyscale's model-based pricing varies, but Llama 2 7B typically runs $1.20 per million input tokens, $1.20 per million output tokens, with compute hour charges if you're running batch jobs. The batch pricing is where complexity enters—you're charged for GPU-hours consumed by your job, which depends on job size, GPU type, and concurrency. A batch job processing 1 billion tokens might cost $15-20 depending on configuration. For one-off processing, this is fine; if you're doing this daily, optimization becomes necessary.

If you need fine-tuning, Anyscale charges per compute hour for tuning jobs (roughly $1.50-2.00 per GPU-hour depending on instance type). Together AI doesn't offer this natively, so you're forced to use Lambda Labs or Vast.ai, introducing complexity.

At scale (1 billion+ tokens monthly), Together AI's cost advantage narrows slightly because both providers probably offer volume discounts if you ask. The representative I spoke with at Anyscale hinted at 15-20% discounts for committed volume, though they wouldn't put it in writing. Together AI has been unwilling to negotiate in my experience, suggesting they're confident in their unit economics.

Real Walkthrough

Let me walk through an actual project I built: a system that ingests product reviews and generates category summaries. We needed to process roughly 2 million tokens daily with variable traffic patterns.

I started with Together AI. Built the initial version in a day using their Python SDK, integrated it into our Flask app, and threw 10,000 test reviews at it. Cost: $18 for the day. Success. The system worked, latency was acceptable (200-400ms), and I could iterate quickly without infrastructure friction.

Two weeks later, we had actual traffic. Peaks during morning hours (US/EU business opening) and evening (international users). Together AI handled it fine until day 23, when a popular tech newsletter mentioned our tool. Traffic spiked 10x. Together AI's rate limiter kicked in after about 30 minutes, and we started returning 429 errors. I escalated to their team, but they simply said we'd exceeded our expected usage. They offered to move us to a higher tier (not standard options, custom arrangement), but pricing would have increased 3x. Not sustainable.

I migrated to Anyscale over a weekend. Code changes were minimal—their API is similar to Together AI's, mostly parameter renames. Deployment took two hours (learning Ray Serve basics, configuring auto-scaling). Cost increased to $35/day for the same workload, but we had headroom. The traffic spike happened again (a second newsletter pickup), and Anyscale's endpoint scaled seamlessly—latency actually decreased because we had more GPU resources active.

Cost comparison after one month on Anyscale: $1,050 versus $540 on Together AI (or $1,620 with Together's escalated pricing). Anyscale won because we didn't have unexpected overages and the infrastructure scaled predictably. If our traffic had remained stable, Together AI would still be winning on pure cost.

For batch processing, I tested Anyscale's Ray Jobs for a one-time historical reprocessing job—scoring 5 million legacy reviews. Submitted a job requesting 8 A100 GPUs, it completed in 4 hours, cost about $85. I don't think Together AI could have handled this efficiently without extreme engineering effort.

Alternatives

OpenAI's API is the elephant in the room. GPT-3.5 costs $0.50-$1.50 per million tokens depending on model size, which is competitive with Anyscale. However, you're locked into OpenAI's models and their rate limits can be severe for startup usage. For comparison, I tested GPT-3.5 for the review summarization task, and the results were better (as expected), but also cost 2x more than Llama 2 while being slower. OpenAI makes sense if you need the absolute best quality; neither Together AI nor Anyscale compete on model capability, only on cost and open-source model serving.

Runpod and Lambda Labs let you rent GPUs directly and run vLLM yourself. This is significantly cheaper if you have sustained traffic—$0.18-0.25 per GPU-hour for consumer GPUs, or $1-2 per hour for A100s. You'll spend engineering time on infrastructure (container management, horizontal scaling, monitoring), but at scale, you can undercut both Together AI and Anyscale. The break-even point is roughly 100M tokens monthly; below that, managed services are simpler.

Hugging Face's Inference API offers similar models with a free tier and $0.60 per million tokens for Pro. I haven't tested it extensively because their documentation lacks clarity on auto-scaling, but for hobby projects and low-volume use cases, it's worth trying before Together AI.

Ollama and LocalAI let you run models locally or on your own infrastructure. This is free (after hardware cost) but not practical for most teams due to the operational overhead. If you have ML expertise in-house and your data can't leave your infrastructure, it's viable; otherwise, managed services are the right choice.

Final Verdict

Choose Together AI if: You're building something new and want to iterate quickly without operational overhead. Your traffic is predictable and stays below 100 req/s. You're cost-sensitive and don't want to negotiate contracts. You're prototyping and might pivot. You're a solo developer or small team.

Choose Anyscale if: You're handling production traffic at scale (100+ req/s sustained). You need batch processing for overnight jobs. You want to fine-tune models on the same platform. You value operational reliability over raw cost. You're a venture-backed company where engineering time is expensive.

I've been honest about both: Together AI is simpler and cheaper until it isn't, then it becomes frustrating. Anyscale is more complex but fundamentally more capable. Neither is bad; they're optimized for different scenarios. Start with Together AI, migrate to Anyscale when you actually need to rather than when you think you might. You'll save money and avoid over-engineering.