Top 5 Strategies to Reduce LLM Token Usage and Costs

Token spend is the largest variable cost in most production LLM applications, and it scales with every request, every retry, and every tool definition loaded into context. Teams running agents across multiple providers routinely see monthly bills grow faster than usage, because input tokens accumulate from repeated prompts, oversized contexts, and inefficient routing decisions. Bifrost, the open-source AI gateway built in Go by Maxim AI, is the best overall choice for enterprise teams that want to reduce LLM token usage and costs at the infrastructure layer without rewriting application code. This post covers five strategies to reduce LLM token usage and costs, and how Bifrost implements each one as a centralized control point for all AI traffic.

Strategy 1: Cache Repeated and Similar Requests

Caching is the most effective strategy to reduce LLM token usage, because a cache hit eliminates the provider call entirely. Production workloads contain large fractions of repeated or near-duplicate requests: identical system prompts, repeated retrieval contexts, and semantically similar user queries that resolve to the same answer.

There are two complementary caching approaches:

• Exact-match caching: Deterministic replay of an identical request. The request is normalized and hashed, and a matching prior request is served without a provider round-trip.
• Semantic caching: Embedding-based lookup that serves a cached answer when a new request is close enough to a previous one, even when the wording differs.

Provider-side prompt caching is one form of this. OpenAI applies prompt caching automatically on prompts longer than 1,024 tokens and offers a 50% discount on cached input tokens, with no code changes required. A gateway-level cache goes further by eliminating the call across providers, not just discounting a portion of the input.

Bifrost implements both paths through semantic caching, which combines direct hash matching for exact-match replay with embedding-based similarity search for fuzzy matches. Direct lookup runs first, and the semantic search runs only on a direct miss. Cache writes are asynchronous, so the first request never blocks on a cache write, and cached entries persist across restarts. Because caching is configured at the gateway, every application and provider behind Bifrost benefits from the same cache without per-service integration work.

Strategy 2: Route Each Request to the Cheapest Capable Model

Model routing reduces token costs by matching each request to the least expensive model that can handle it, rather than sending every request to a flagship model. Budget-tier models cost a fraction of frontier models for classification, extraction, and routing tasks, and most production traffic does not require the most capable model.

A routing strategy that reduces cost typically combines:

• Task-based routing: Direct simple classification or extraction to smaller, cheaper models, and reserve frontier models for complex reasoning.
• Provider and key selection: Distribute traffic across providers and API keys based on cost and capacity.
• Automatic fallback: Route around a provider that returns errors so retries do not multiply token spend on a single overloaded endpoint.

Bifrost centralizes routing across 1000+ models through a single OpenAI-compatible API, so routing logic lives in the gateway rather than in each application. Provider routing supports governance-based rules defined through virtual keys, where teams specify allowed models and providers per key, and adaptive load balancing for automatic performance-based distribution. Automatic fallbacks reroute requests when a provider becomes unavailable, which prevents repeated failed calls from inflating token usage. Teams evaluating gateway options can review the LLM Gateway Buyer's Guide for a capability comparison across routing and cost-control features.

Strategy 3: Compress Prompts and Trim Context

Context size is a direct multiplier on input token cost, and most prompts carry redundant tokens that add cost without improving output quality. Prompt compression and context trimming reduce the number of input tokens sent on every request.

Common techniques include:

• Prompt compression: Remove low-importance tokens while preserving meaning. Microsoft Research's LLMLingua achieves up to 20x prompt compression with minimal performance loss by scoring each token's importance and dropping the lowest-ranked ones.
• Context trimming: Send only the retrieval chunks and conversation history relevant to the current request, rather than the full available context.
• Output shaping: Constrain response length and format, since output tokens are typically priced higher than input tokens.

These techniques reduce LLM token usage at the prompt level. The remaining challenge is enforcing them consistently across teams. A centralized gateway is the natural enforcement point: it sees every request before it reaches a provider, which makes it the place to apply request-level controls, measure token usage per consumer, and identify which workloads carry the largest context. Bifrost provides request monitoring and observability across all traffic, so teams can find oversized prompts and high-cost workloads before optimizing them.

Strategy 4: Use Code Mode to Cut Token Usage in Agentic Workflows

Agentic workflows that connect multiple Model Context Protocol (MCP) servers carry a hidden token cost: every request includes the full tool catalog in context. When an agent connects 8 to 10 MCP servers with 150 or more tools, the model spends most of its input budget reading tool definitions on every turn instead of doing work.

Code Mode addresses this by exposing four generic tools to the model instead of the full catalog. The model writes Python in a sandbox to orchestrate the underlying tools, loading tool signatures on demand rather than carrying all of them in context. Intermediate results are processed in the sandbox instead of flowing through the model on every turn.

The token reduction grows with the number of connected tools. In Bifrost's Code Mode benchmarks, a workflow across 16 MCP servers with 508 tools showed up to 92.8% fewer input tokens, 92.2% lower estimated cost, and around 40% faster execution compared to classic MCP, with pass rates held at 100%. At roughly 500 tools, average input tokens per query fell from about 1.15 million to 83 thousand.

Bifrost runs Code Mode as part of the MCP gateway, which centralizes tool connections, authentication, and governance across all connected MCP servers. Teams can enable Code Mode for tool-heavy servers and keep small utilities as direct tool calls, so the token savings apply where the tool catalog is largest. For a deeper breakdown of the benchmark methodology and access-control model, the MCP Gateway writeup on cost governance and lower token costs at scale covers the full results.

Strategy 5: Enforce Budgets, Rate Limits, and Per-Team Governance

The strategies above reduce per-request token usage. Governance controls the aggregate: budgets cap spend, rate limits throttle runaway workloads, and per-team attribution makes token usage measurable. Without enforced budgets, a single misconfigured agent or a retry loop can consume a large share of a monthly budget before anyone notices.

Effective cost governance includes:

• Hierarchical budgets: Independent spend limits at the team, project, and key level, checked on every request.
• Rate limits: Request-based and token-based throttling to bound the cost of any single consumer.
• Usage attribution: Real-time tracking of token usage and cost per team, project, or customer.

Bifrost enforces these through virtual keys, the primary governance entity. Each virtual key carries its own budgets and rate limits, and budgets nest hierarchically across customers, teams, and individual keys, with every applicable budget checked independently before a request proceeds. A provider that exceeds its budget or rate limit is excluded from routing, which prevents overspend from cascading. Cost is calculated automatically from token usage and provider pricing, including reduced cost for cached responses. The governance resource page details how these controls map to team structures and cost-attribution requirements.

For regulated industries and large deployments, Bifrost runs in air-gapped, VPC-isolated, and on-prem environments, so budget enforcement and usage tracking operate inside private infrastructure with full control over data and access.

Putting the Strategies Together

The five strategies to reduce LLM token usage and costs compound when applied together at the infrastructure layer:

• Cache repeated and similar requests to eliminate redundant provider calls.
• Route each request to the cheapest capable model and fall back automatically on errors.
• Compress prompts and trim context to lower input token counts.
• Use Code Mode to cut tool-definition tokens in multi-server agentic workflows.
• Govern with hierarchical budgets, rate limits, and per-team usage attribution.

Applying these in separate application code paths is fragile and hard to measure. A centralized gateway applies all five consistently across every model, provider, and team, and reports the token usage and cost data needed to keep optimizing. Bifrost combines caching, routing, Code Mode, observability, and governance into a single control point for AI traffic, with 11 microseconds of overhead per request at 5,000 requests per second so cost controls do not add latency. Additional implementation guides are available in the Bifrost resources hub.

To see how Bifrost can reduce LLM token usage and costs across your AI infrastructure, book a demo with the Bifrost team.